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

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.

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.

Do climate model predictions agree with long-term precipitation trends in the arid southwestern United States?

NASA Astrophysics Data System (ADS)

Elias, E.; Rango, A.; James, D.; Maxwell, C.; Anderson, J.; Abatzoglou, J. T.

2016-12-01

Researchers evaluating climate projections across southwestern North America observed a decreasing precipitation trend. Aridification was most pronounced in the cold (non-monsoonal) season, whereas downward trends in precipitation were smaller in the warm (monsoonal) season. In this region, based upon a multimodel mean of 20 Coupled Model Intercomparison Project 5 models using a business-as-usual (Representative Concentration Pathway 8.5) trajectory, midcentury precipitation is projected to increase slightly during the monsoonal time period (July-September; 6%) and decrease slightly during the remainder of the year (October-June; -4%). We use observed long-term (1915-2015) monthly precipitation records from 16 weather stations to investigate how well measured trends corroborate climate model predictions during the monsoonal and non-monsoonal timeframe. Running trend analysis using the Mann-Kendall test for 15 to 101 year moving windows reveals that half the stations showed significant (p≤0.1), albeit small, increasing trends based on the longest term record. Trends based on shorter-term records reveal a period of significant precipitation decline at all stations representing the 1950s drought. Trends from 1930 to 2015 reveal significant annual, monsoonal and non-monsoonal increases in precipitation (Fig 1). The 1960 to 2015 time window shows no significant precipitation trends. The more recent time window (1980 to 2015) shows a slight, but not significant, increase in monsoonal precipitation and a larger, significant decline in non-monsoonal precipitation. GCM precipitation projections are consistent with more recent trends for the region. Running trends from the most recent time window (mid-1990s to 2015) at all stations show increasing monsoonal precipitation and decreasing Oct-Jun precipitation, with significant trends at 6 of 16 stations. Running trend analysis revealed that the long-term trends were not persistent throughout the series length, but depended on the period examined. Recent trends in Southwest precipitation are directionally consistent with anthropogenic climate change.

Trends in Cigarette Use amongst Kansas Eighth Grade Students: "Communities That Care Survey" Results, 1995-2000.

ERIC Educational Resources Information Center

Kingsley, David E.

This paper reports on models that clarify the meaning of trends in 8th grade smoking in one of America's most rural and least densely populated states. It is based on cross-sectional analysis of data collected in the "Kansas Communities That Care Survey" from 1995 to 1999. The analysis of trends data is presented in table form utilizing…

River catchment rainfall series analysis using additive Holt-Winters method

NASA Astrophysics Data System (ADS)

Puah, Yan Jun; Huang, Yuk Feng; Chua, Kuan Chin; Lee, Teang Shui

2016-03-01

Climate change is receiving more attention from researchers as the frequency of occurrence of severe natural disasters is getting higher. Tropical countries like Malaysia have no distinct four seasons; rainfall has become the popular parameter to assess climate change. Conventional ways that determine rainfall trends can only provide a general result in single direction for the whole study period. In this study, rainfall series were modelled using additive Holt-Winters method to examine the rainfall pattern in Langat River Basin, Malaysia. Nine homogeneous series of more than 25 years data and less than 10% missing data were selected. Goodness of fit of the forecasted models was measured. It was found that seasonal rainfall model forecasts are generally better than the monthly rainfall model forecasts. Three stations in the western region exhibited increasing trend. Rainfall in southern region showed fluctuation. Increasing trends were discovered at stations in the south-eastern region except the seasonal analysis at station 45253. Decreasing trend was found at station 2818110 in the east, while increasing trend was shown at station 44320 that represents the north-eastern region. The accuracies of both rainfall model forecasts were tested using the recorded data of years 2010-2012. Most of the forecasts are acceptable.

Can the combined use of an ensemble based modelling approach and the analysis of measured meteorological trends lead to increased confidence in climate change impact assessments?

NASA Astrophysics Data System (ADS)

Gädeke, Anne; Koch, Hagen; Pohle, Ina; Grünewald, Uwe

2014-05-01

In anthropogenically heavily impacted river catchments, such as the Lusatian river catchments of Spree and Schwarze Elster (Germany), the robust assessment of possible impacts of climate change on the regional water resources is of high relevance for the development and implementation of suitable climate change adaptation strategies. Large uncertainties inherent in future climate projections may, however, reduce the willingness of regional stakeholder to develop and implement suitable adaptation strategies to climate change. This study provides an overview of different possibilities to consider uncertainties in climate change impact assessments by means of (1) an ensemble based modelling approach and (2) the incorporation of measured and simulated meteorological trends. The ensemble based modelling approach consists of the meteorological output of four climate downscaling approaches (DAs) (two dynamical and two statistical DAs (113 realisations in total)), which drive different model configurations of two conceptually different hydrological models (HBV-light and WaSiM-ETH). As study area serve three near natural subcatchments of the Spree and Schwarze Elster river catchments. The objective of incorporating measured meteorological trends into the analysis was twofold: measured trends can (i) serve as a mean to validate the results of the DAs and (ii) be regarded as harbinger for the future direction of change. Moreover, regional stakeholders seem to have more trust in measurements than in modelling results. In order to evaluate the nature of the trends, both gradual (Mann-Kendall test) and step changes (Pettitt test) are considered as well as both temporal and spatial correlations in the data. The results of the ensemble based modelling chain show that depending on the type (dynamical or statistical) of DA used, opposing trends in precipitation, actual evapotranspiration and discharge are simulated in the scenario period (2031-2060). While the statistical DAs simulate a strong decrease in future long term annual precipitation, the dynamical DAs simulate a tendency towards increasing precipitation. The trend analysis suggests that precipitation has not changed significantly during the period 1961-2006. Therefore, the decrease simulated by the statistical DAs should be interpreted as a rather dry future projection. Concerning air temperature, measured and simulated trends agree on a positive trend. Also the uncertainty related to the hydrological model within the climate change modelling chain is comparably low when long-term averages are considered but increases significantly during extreme events. This proposed framework of combining an ensemble based modelling approach with measured trend analysis is a promising approach for regional stakeholders to gain more confidence into the final results of climate change impact assessments. However, climate change impact assessments will remain highly uncertain. Thus, flexible adaptation strategies need to be developed which should not only consider climate but also other aspects of global change.

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.

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.

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.

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.

Assessment of trend and seasonality in road accident data: an Iranian case study.

PubMed

Razzaghi, Alireza; Bahrampour, Abbas; Baneshi, Mohammad Reza; Zolala, Farzaneh

2013-06-01

Road traffic accidents and their related deaths have become a major concern, particularly in developing countries. Iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. In this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying ARIMA models on data from Taybad city. This study is a cross-sectional study. We used data from accidents occurring in Taybad between 2007 and 2011. We obtained the data from the Ministry of Health (MOH) and used the time series method with a time lag of one month. After plotting the trend, non-stationary data in mean and variance were removed using Box-Cox transformation and a differencing method respectively. The ACF and PACF plots were used to control the stationary situation. The traffic accidents in our study had an increasing trend over the five years of study. Based on ACF and PACF plots gained after applying Box-Cox transformation and differencing, data did not fit to a time series model. Therefore, neither ARIMA model nor seasonality were observed. Traffic accidents in Taybad have an upward trend. In addition, we expected either the AR model, MA model or ARIMA model to have a seasonal trend, yet this was not observed in this analysis. Several reasons may have contributed to this situation, such as uncertainty of the quality of data, weather changes, and behavioural factors that are not taken into account by time series analysis.

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.

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

Decreased consumption of sugar-sweetened beverages improved selected biomarkers of chronic disease risk among US adults: 1999 to 2010.

PubMed

Hert, Kerrie A; Fisk, Paul S; Rhee, Yeong S; Brunt, Ardith R

2014-01-01

Consumption of sugar-sweetened beverages (SSBs) increased greatly from the late 1970s to the early part of this decade. Although recent data show that consumption of SSB may now be declining, consumption levels still remain much higher than recommended. Using data from the National Health and Nutrition Examination Survey, we assessed trends in intakes of SSB and levels of chronic disease biomarkers from 1999 to 2010 and examined the associations of SSB intake and biomarkers of chronic disease risk. We hypothesized that SSB intake will decrease and biomarkers of chronic disease risk will improve, therefore indicating that high intake of SSB is associated with greater chronic disease risk. Univariate analysis showed that from 1999 to 2010, SSB consumption decreased (P for trend = .0026), high-density lipoprotein increased (P for trend < .0001), low-density lipoprotein decreased (P for trend = .0007), and C-reactive protein decreased (P for trend = .0096). Using multivariate analysis, we showed that higher intakes of SSB were associated with lower high-density lipoprotein (P for trend < .0001), in an unadjusted model and all models with increasing numbers of covariates, and higher C-reactive protein (P for trend < .05), in an unadjusted model and in models with age, race/ethnicity, sex, education level, and poverty income ratio adjustments. We conclude that SSB consumption is associated with biomarkers of chronic disease risk, independent of demographic and lifestyle factors. © 2014.

Modeling seasonal variation of hip fracture in Montreal, Canada.

PubMed

Modarres, Reza; Ouarda, Taha B M J; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre

2012-04-01

The investigation of the association of the climate variables with hip fracture incidences is important in social health issues. This study examined and modeled the seasonal variation of monthly population based hip fracture rate (HFr) time series. The seasonal ARIMA time series modeling approach is used to model monthly HFr incidences time series of female and male patients of the ages 40-74 and 75+ of Montreal, Québec province, Canada, in the period of 1993-2004. The correlation coefficients between meteorological variables such as temperature, snow depth, rainfall depth and day length and HFr are significant. The nonparametric Mann-Kendall test for trend assessment and the nonparametric Levene's test and Wilcoxon's test for checking the difference of HFr before and after change point are also used. The seasonality in HFr indicated sharp difference between winter and summer time. The trend assessment showed decreasing trends in HFr of female and male groups. The nonparametric test also indicated a significant change of the mean HFr. A seasonal ARIMA model was applied for HFr time series without trend and a time trend ARIMA model (TT-ARIMA) was developed and fitted to HFr time series with a significant trend. The multi criteria evaluation showed the adequacy of SARIMA and TT-ARIMA models for modeling seasonal hip fracture time series with and without significant trend. In the time series analysis of HFr of the Montreal region, the effects of the seasonal variation of climate variables on hip fracture are clear. The Seasonal ARIMA model is useful for modeling HFr time series without trend. However, for time series with significant trend, the TT-ARIMA model should be applied for modeling HFr time series. Copyright © 2011 Elsevier Inc. All rights reserved.

Combined analysis of roadside and off-road breeding bird survey data to assess population change in Alaska

USGS Publications Warehouse

Handel, Colleen M.; Sauer, John

2017-01-01

Management interest in North American birds has increasingly focused on species that breed in Alaska, USA, and Canada, where habitats are changing rapidly in response to climatic and anthropogenic factors. We used a series of hierarchical models to estimate rates of population change in 2 forested Bird Conservation Regions (BCRs) in Alaska based on data from the roadside North American Breeding Bird Survey (BBS) and the Alaska Landbird Monitoring Survey, which samples off-road areas on public resource lands. We estimated long-term (1993–2015) population trends for 84 bird species from the BBS and short-term (2003–2015) trends for 31 species from both surveys. Among the 84 species with long-term estimates, 11 had positive trends and 17 had negative trends in 1 or both BCRs; negative trends were primarily found among aerial insectivores and wetland-associated species, confirming range-wide negative continental trends for many of these birds. Three species with negative trends in the contiguous United States and southern Canada had positive trends in Alaska, suggesting different population dynamics at the northern edges of their ranges. Regional population trends within Alaska differed for several species, particularly those represented by different subspecies in the 2 BCRs, which are separated by rugged, glaciated mountain ranges. Analysis of the roadside and off-road data in a joint hierarchical model with shared parameters resulted in improved precision of trend estimates and suggested a roadside-related difference in underlying population trends for several species, particularly within the Northwestern Interior Forest BCR. The combined analysis highlights the importance of considering population structure, physiographic barriers, and spatial heterogeneity in habitat change when assessing patterns of population change across a landscape as broad as Alaska. Combined analysis of roadside and off-road survey data in a hierarchical framework may be particularly useful for evaluating patterns of population change in relatively undeveloped regions with sparse roadside BBS coverage.

The Use of Multiple Regression and Trend Analysis to Understand Enrollment Fluctuations. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Campbell, S. Duke; Greenberg, Barry

The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…

[Analysis on the trend of long-term change of blood pressure in hypertensive patients treated with benazepril].

PubMed

Lu, Jun; Li, Li-Ming; He, Ping-Ping; Cao, Wei-Hua; Zhan, Si-Yan; Hu, Yong-Hua

2004-06-01

To introduce the application of mixed linear model in the analysis of secular trend of blood pressure under antihypertensive treatment. A community-based postmarketing surveillance of benazepril was conducted in 1831 essential hypertensive patients (age range from 35 to 88 years) in Shanghai. Data of blood pressure was analyzed every 3 months with mixed linear model to describe the secular trend of blood pressure and changes of age-specific and gender-specific. The changing trends of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were found to fit the curvilinear models. A piecewise model was fit for pulse pressure (PP), i.e., curvilinear model in the first 9 months and linear model after 9 months of taking medication. Both blood pressure and its velocity gradually slowed down. There were significant variation for the curve parameters of intercept, slope, and acceleration. Blood pressure in patients with higher initial levels was persistently declining in the 3-year-treatment. However blood pressures of patients with relatively low initial levels remained low when dropped down to some degree. Elderly patients showed high SBP but low DBP, so as with higher PP. The velocity and sizes of blood pressure reductions increased with the initial level of blood pressure. Mixed linear model is flexible and robust when applied to the analysis of longitudinal data but with missing values and can also make the maximum use of available information.

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

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.

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.

Sediment transport modelling based on grain size trend analysis in Augusta Harbour (Sicily)

NASA Astrophysics Data System (ADS)

Barbera, Giuseppe; Feo, Roberto; Freni, Gabriele

2015-12-01

To support marine civil engineer in pollutant studies, sediment management or dredging operations, is useful to know how the sediments move in accumulation basin. This paper investigates the dynamic of the sediment path using a two-dimensional numeric model: the Grain Size Trend Analysis (GSTA). The GSTA was applied using GiSedTrend plugin, under GIS software. The case study is the Augusta Harbour, which is one of the most polluted Italian harbours. It is the marine part of the Site of National Interest (SNI) of Priolo Gargallo (Siracusa, Italy) and it can be hydrodynamically considered as a lagoon. Two scenarios were obtained by using different geostatistical criteria.

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.

Natural and Anthropogenic Aerosol Trends from Satellite and Surface Observations and Model Simulations over the North Atlantic Ocean from 2002 to 2012

NASA Technical Reports Server (NTRS)

Jongeward, Andrew R.; Li, Zhanqing; He, Hao; Xiong, Xiaoxiong

2016-01-01

Aerosols contribute to Earths radiative budget both directly and indirectly, and large uncertainties remain in quantifying aerosol effects on climate. Variability in aerosol distribution and properties, as might result from changing emissions and transport processes, must be characterized. In this study, variations in aerosol loading across the eastern seaboard of theUnited States and theNorthAtlanticOcean during 2002 to 2012 are analyzed to examine the impacts of anthropogenic emission control measures using monthly mean data from MODIS, AERONET, and IMPROVE observations and Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulation.MODIS observes a statistically significant negative trend in aerosol optical depth (AOD) over the midlatitudes (-0.030 decade(sup-1)). Correlation analyses with surface AOD from AERONET sites in the upwind region combined with trend analysis from GOCART component AOD confirm that the observed decrease in the midlatitudes is chiefly associated with anthropogenic aerosols that exhibit significant negative trends from the eastern U.S. coast extending over the western North Atlantic. Additional analysis of IMPROVE surface PM(sub 2.5) observations demonstrates statistically significant negative trends in the anthropogenic components with decreasing mass concentrations over the eastern United States. Finally, a seasonal analysis of observational datasets is performed. The negative trend seen by MODIS is strongest during spring (MAM) and summer (JJA) months. This is supported by AERONET seasonal trends and is identified from IMPROVE seasonal trends as resulting from ammonium sulfate decreases during these seasons.

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.

Hierarchical modeling of population stability and species group attributes from survey data

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.

2002-01-01

Many ecological studies require analysis of collections of estimates. For example, population change is routinely estimated for many species from surveys such as the North American Breeding Bird Survey (BBS), and the species are grouped and used in comparative analyses. We developed a hierarchical model for estimation of group attributes from a collection of estimates of population trend. The model uses information from predefined groups of species to provide a context and to supplement data for individual species; summaries of group attributes are improved by statistical methods that simultaneously analyze collections of trend estimates. The model is Bayesian; trends are treated as random variables rather than fixed parameters. We use Markov Chain Monte Carlo (MCMC) methods to fit the model. Standard assessments of population stability cannot distinguish magnitude of trend and statistical significance of trend estimates, but the hierarchical model allows us to legitimately describe the probability that a trend is within given bounds. Thus we define population stability in terms of the probability that the magnitude of population change for a species is less than or equal to a predefined threshold. We applied the model to estimates of trend for 399 species from the BBS to estimate the proportion of species with increasing populations and to identify species with unstable populations. Analyses are presented for the collection of all species and for 12 species groups commonly used in BBS summaries. Overall, we estimated that 49% of species in the BBS have positive trends and 33 species have unstable populations. However, the proportion of species with increasing trends differs among habitat groups, with grassland birds having only 19% of species with positive trend estimates and wetland birds having 68% of species with positive trend estimates.

Indicators of AEI applied to the Delaware Estuary.

PubMed

Barnthouse, Lawrence W; Heimbuch, Douglas G; Anthony, Vaughn C; Hilborn, Ray W; Myers, Ransom A

2002-05-18

We evaluated the impacts of entrainment and impingement at the Salem Generating Station on fish populations and communities in the Delaware Estuary. In the absence of an agreed-upon regulatory definition of "adverse environmental impact" (AEI), we developed three independent benchmarks of AEI based on observed or predicted changes that could threaten the sustainability of a population or the integrity of a community. Our benchmarks of AEI included: (1) disruption of the balanced indigenous community of fish in the vicinity of Salem (the "BIC" analysis); (2) a continued downward trend in the abundance of one or more susceptible fish species (the "Trends" analysis); and (3) occurrence of entrainment/impingement mortality sufficient, in combination with fishing mortality, to jeopardize the future sustainability of one or more populations (the "Stock Jeopardy" analysis). The BIC analysis utilized nearly 30 years of species presence/absence data collected in the immediate vicinity of Salem. The Trends analysis examined three independent data sets that document trends in the abundance of juvenile fish throughout the estuary over the past 20 years. The Stock Jeopardy analysis used two different assessment models to quantify potential long-term impacts of entrainment and impingement on susceptible fish populations. For one of these models, the compensatory capacities of the modeled species were quantified through meta-analysis of spawner-recruit data available for several hundred fish stocks. All three analyses indicated that the fish populations and communities of the Delaware Estuary are healthy and show no evidence of an adverse impact due to Salem. Although the specific models and analyses used at Salem are not applicable to every facility, we believe that a weight of evidence approach that evaluates multiple benchmarks of AEI using both retrospective and predictive methods is the best approach for assessing entrainment and impingement impacts at existing facilities.

Extreme sea storm in the Mediterranean Sea. Trends during the 2nd half of the 20th century.

NASA Astrophysics Data System (ADS)

Pino, C.; Lionello, P.; Galati, M. B.

2009-04-01

Extreme sea storm in the Mediterranean Sea. Trends during the 2nd half of the 20th century Piero Lionello, University of Salento, piero.lionello@unisalento.it Maria Barbara Galati, University of Salento, mariabarbara.galati@unisalento.it Cosimo Pino, University of Salento, pino@le.infn.it The analysis of extreme Significant Wave Height (SWH) values and their trend is crucial for planning and managing coastal defences and off-shore activities. The analysis provided by this study covers a 44-year long period (1958-2001). First the WW3 (Wave Watch 3) model forced with the REMO-Hipocas regional model wind fields has been used for the hindcast of extreme SWH values over the Mediterranean basin with a 0.25 deg lat-lon resolution. Subsequently, the model results have been processed with an ad hoc software to detect storms. GEV analysis has been perfomed and a set of indicators for extreme SWH have been computed, using the Mann Kendall test for assessing statistical significance of trends for different parameter such as the number of extreme events, their duration and their intensity. Results suggest a transition towards weaker extremes and a milder climate over most of the Mediterranean Sea.

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).

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.

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.

Secular trend analysis of lung cancer incidence in Sihui city, China between 1987 and 2011.

PubMed

Du, Jin-Lin; Lin, Xiao; Zhang, Li-Fang; Li, Yan-Hua; Xie, Shang-Hang; Yang, Meng-Jie; Guo, Jie; Lin, Er-Hong; Liu, Qing; Hong, Ming-Huang; Huang, Qi-Hong; Liao, Zheng-Er; Cao, Su-Mei

2015-07-31

With industrial and econom ic development in recent decades in South China, cancer incidence may have changed due to the changing lifestyle and environment. However, the trends of lung cancer and the roles of smoking and other environmental risk factors in the development of lung cancer in rural areas of South China remain unclear. The purpose of this study was to explore the lung cancer incidence trends and the possible causes of these trends. Joinpoint regression analysis and the age-period-cohort (APC) model were used to analyze the lung cancer incidence trends in Sihui, Guangdong province, China between 1987 and 2011, and explore the possible causes of these trends. A total of 2,397 lung cancer patients were involved in this study. A 3-fold increase in the incidence of lung cancer in both sexes was observed over the 25-year period. Joinpoint regression analysis showed that while the incidence continued to increase steadily in females during the entire period, a sharp acceleration was observed in males starting in 2005. The full APC model was selected to describe age, period, and birth cohort effects on lung cancer incidence trends in Sihui. The age cohorts in both sexes showed a continuously significant increase in the relative risk (RR) of lung cancer, with a peak in the eldest age group (80-84 years). The RR of lung cancer showed a fluctuating curve in both sexes. The birth cohorts identified an increased trend in both males and females; however, males had a plateau in the youngest cohorts who were born during 1955-1969. Increasing trends of the incidence of lung cancer in Sihui were dominated by the effects of age and birth cohorts. Social aging, smoking, and environmental changes may play important roles in such trends.

Hierarchical models and bayesian analysis of bird survey information

Treesearch

John R. Sauer; William A. Link; J. Andrew Royle

2005-01-01

Summary of bird survey information is a critical component of conservation activities, but often our summaries rely on statistical methods that do not accommodate the limitations of the information. Prioritization of species requires ranking and analysis of species by magnitude of population trend, but often magnitude of trend is a misleading measure of actual decline...

Modeling trends from North American Breeding Bird Survey data: a spatially explicit approach

USGS Publications Warehouse

Bled, Florent; Sauer, John R.; Pardieck, Keith L.; Doherty, Paul; Royle, J. Andy

2013-01-01

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.

Oral cavity cancer trends over the past 25 years in Hong Kong: a multidirectional statistical analysis.

PubMed

Ushida, Keisuke; McGrath, Colman P; Lo, Edward C M; Zwahlen, Roger A

2015-07-24

Even though oral cavity cancer (OCC; ICD 10 codes C01, C02, C03, C04, C05, and C06) ranks eleventh among the world's most common cancers, accounting for approximately 2 % of all cancers, a trend analysis of OCC in Hong Kong is lacking. Hong Kong has experienced rapid economic growth with socio-cultural and environmental change after the Second World War. This together with the collected data in the cancer registry provides interesting ground for an epidemiological study on the influence of socio-cultural and environmental factors on OCC etiology. A multidirectional statistical analysis of the OCC trends over the past 25 years was performed using the databases of the Hong Kong Cancer Registry. The age, period, and cohort (APC) modeling was applied to determine age, period, and cohort effects on OCC development. Joinpoint regression analysis was used to find secular trend changes of both age-standardized and age-specific incidence rates. The APC model detected that OCC development in men was mainly dominated by the age effect, whereas in women an increasing linear period effect together with an age effect became evident. The joinpoint regression analysis showed a general downward trend of age-standardized incidence rates of OCC for men during the entire investigated period, whereas women demonstrated a significant upward trend from 2001 onwards. The results suggest that OCC incidence in Hong Kong appears to be associated with cumulative risk behaviors of the population, despite considerable socio-cultural and environmental changes after the Second World War.

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.

Applications of MIDAS regression in analysing trends in water quality

NASA Astrophysics Data System (ADS)

Penev, Spiridon; Leonte, Daniela; Lazarov, Zdravetz; Mann, Rob A.

2014-04-01

We discuss novel statistical methods in analysing trends in water quality. Such analysis uses complex data sets of different classes of variables, including water quality, hydrological and meteorological. We analyse the effect of rainfall and flow on trends in water quality utilising a flexible model called Mixed Data Sampling (MIDAS). This model arises because of the mixed frequency in the data collection. Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily. The advantage of using MIDAS regression is in the flexible and parsimonious modelling of the influence of the rain and flow on trends in water quality variables. We discuss the model and its implementation on a data set from the Shoalhaven Supply System and Catchments in the state of New South Wales, Australia. Information criteria indicate that MIDAS modelling improves upon simplistic approaches that do not utilise the mixed data sampling nature of the data.

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

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.

Performance of CMIP3 and CMIP5 GCMs to simulate observed rainfall characteristics over the Western Himalayan region

NASA Astrophysics Data System (ADS)

Meher, J. K.; Das, L.

2017-12-01

The Western Himalayan Region (WHR) was subject to a significant negative trend in the annual and monsoon rainfall during 1902-2005. Annual and seasonal rainfall change over WHR of India was estimated using 22 rain gauge station rainfall data from the India Meteorological Department. The performance of 13 global climate models (GCMs) from the coupled model intercomparison project phase 3 (CMIP3) and 42 GCMs from CMIP5 was evaluated through multiple analysis: the evaluation of the mean annual cycle, annual cycles of interannual variability, spatial patterns, trends and signal-to-noise ratio. In general, CMIP5 GCMs were more skillful in terms of simulating the annual cycle of interannual variability compared to CMIP3 GCMs. The CMIP3 GCMs failed to reproduce the observed trend whereas 50% of the CMIP5 GCMs reproduced the statistical distribution of short-term (30-years) trend-estimates than for the longer term (99-years). GCMs from both CMIP3 and CMIP5 were able to simulate the spatial distribution of observed rainfall in pre-monsoon and winter months. Based on performance, each model of CMIP3 and CMIP5 was given an overall rank, which puts the high resolution version of the MIROC3.2 model (MIROC3.2 hires) and MIROC5 at the top in CMIP3 and CMIP5 respectively. Robustness of the ranking was judged through a sensitivity analysis, which indicated that ranks were independent during the process of adding or removing any individual method. It also revealed that trend analysis was not a robust method of judging performances of the model as compared to other methods.

Monitoring, analyzing and simulating of spatial-temporal changes of landscape pattern over mining area

NASA Astrophysics Data System (ADS)

Liu, Pei; Han, Ruimei; Wang, Shuangting

2014-11-01

According to the merits of remotely sensed data in depicting regional land cover and Land changes, multi- objective information processing is employed to remote sensing images to analyze and simulate land cover in mining areas. In this paper, multi-temporal remotely sensed data were selected to monitor the pattern, distri- bution and trend of LUCC and predict its impacts on ecological environment and human settlement in mining area. The monitor, analysis and simulation of LUCC in this coal mining areas are divided into five steps. The are information integration of optical and SAR data, LULC types extraction with SVM classifier, LULC trends simulation with CA Markov model, landscape temporal changes monitoring and analysis with confusion matrixes and landscape indices. The results demonstrate that the improved data fusion algorithm could make full use of information extracted from optical and SAR data; SVM classifier has an efficient and stable ability to obtain land cover maps, which could provide a good basis for both land cover change analysis and trend simulation; CA Markov model is able to predict LULC trends with good performance, and it is an effective way to integrate remotely sensed data with spatial-temporal model for analysis of land use / cover change and corresponding environmental impacts in mining area. Confusion matrixes are combined with landscape indices to evaluation and analysis show that, there was a sustained downward trend in agricultural land and bare land, but a continues growth trend tendency in water body, forest and other lands, and building area showing a wave like change, first increased and then decreased; mining landscape has undergone a from small to large and large to small process of fragmentation, agricultural land is the strongest influenced landscape type in this area, and human activities are the primary cause, so the problem should be pay more attentions by government and other organizations.

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

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.

Models for Rational Decision Making. Analysis of Literature and Selected Bibliography. Analysis and Bibliography Series, No. 6.

ERIC Educational Resources Information Center

Hall, John S.

This review analyzes the trend in educational decision making to replace hierarchical authority structures with more rational models for decision making drawn from management science. Emphasis is also placed on alternatives to a hierarchical decision-making model, including governing models, union models, and influence models. A 54-item…

Trends in Reported Foodborne Illness in the United States; 1996-2013.

PubMed

Powell, Mark R

2016-08-01

Retrospective review is a key to designing effective food safety measures. The analysis examines trends in the reported incidence of illness due to bacterial pathogens commonly transmitted by food in the United States during 1996-2013 with and without specifying a model form for trend. The findings indicate early declines in reported incidence followed by a period of no significant trend for Campylobacter, Listeria, Shiga toxin-producing Escherichia coli O157, and Yersinia. The results are inconclusive about whether there is no trend or an increasing trend for Salmonella. While Shigella exhibits a continuous decline, Vibrio exhibits a continuous increase. Overall, the findings indicate a lack of evidence for continuous reduction in illness due to bacterial pathogens commonly transmitted by food in the United States during 1996-2013. © 2015 Society for Risk Analysis.

Publication Trends in Model Organism Research

PubMed Central

Dietrich, Michael R.; Ankeny, Rachel A.; Chen, Patrick M.

2014-01-01

In 1990, the National Institutes of Health (NIH) gave some organisms special status as designated model organisms. This article documents publication trends for these NIH-designated model organisms over the past 40 years. We find that being designated a model organism by the NIH does not guarantee an increasing publication trend. An analysis of model and nonmodel organisms included in GENETICS since 1960 does reveal a sharp decline in the number of publications using nonmodel organisms yet no decline in the overall species diversity. We suggest that organisms with successful publication records tend to share critical characteristics, such as being well developed as standardized, experimental systems and being used by well-organized communities with good networks of exchange and methods of communication. PMID:25381363

What is the impact of different VLBI analysis setups of the tropospheric delay on precipitable water vapor trends?

NASA Astrophysics Data System (ADS)

Balidakis, Kyriakos; Nilsson, Tobias; Heinkelmann, Robert; Glaser, Susanne; Zus, Florian; Deng, Zhiguo; Schuh, Harald

2017-04-01

The quality of the parameters estimated by global navigation satellite systems (GNSS) and very long baseline interferometry (VLBI) are distorted by erroneous meteorological observations applied to model the propagation delay in the electrically neutral atmosphere. For early VLBI sessions with poor geometry, unsuitable constraints imposed on the a priori tropospheric gradients is a source of additional hassle of VLBI analysis. Therefore, climate change indicators deduced from the geodetic analysis, such as the long-term precipitable water vapor (PWV) trends, are strongly affected. In this contribution we investigate the impact of different modeling and parameterization of the propagation delay in the troposphere on the estimates of long-term PWV trends from geodetic VLBI analysis results. We address the influence of the meteorological data source, and of the a priori non-hydrostatic delays and gradients employed in the VLBI processing, on the estimated PWV trends. In particular, we assess the effect of employing temperature and pressure from (i) homogenized in situ observations, (ii) the model levels of the ERA Interim reanalysis numerical weather model and (iii) our own blind model in the style of GPT2w with enhanced parameterization, calculated using the latter data set. Furthermore, we utilize non-hydrostatic delays and gradients estimated from (i) a GNSS reprocessing at GeoForschungsZentrum Potsdam, rigorously considering tropospheric ties, and (ii)) direct ray-tracing through ERA Interim, as additional observations. To evaluate the above, the least-squares module of the VieVS@GFZ VLBI software was appropriately modified. Additionally, we study the noise characteristics of the non-hydrostatic delays and gradients estimated from our VLBI and GNSS analyses as well as from ray-tracing. We have modified the Theil-Sen estimator appropriately to robustly deduce PWV trends from VLBI, GNSS, ray-tracing and direct numerical integration in ERA Interim. We disseminate all our solutions in the latest Tropo-SINEX format.

Dissolved-solids sources, loads, yields, and concentrations in streams of the conterminous United States

USGS Publications Warehouse

Anning, David W.; Flynn, Marilyn E.

2014-01-01

Results from the trend analysis and from the SPARROW model indicate that, compared to monitoring stations with no trends or decreasing trends, stations with increasing trends are associated with a smaller percentage of the predicted dissolved-solids load originating from geologic sources, and a larger percentage originating from urban lands and road deicers. Conversely, compared to stations with increasing trends or no trends, stations with decreasing trends have a larger percentage of the predicted dissolved-solids load originating from geologic sources and a smaller percentage originating from urban lands and road deicers. Stations with decreasing trends also have larger percentages of predicted dissolved-solids load originating from cultivated lands and pasture lands, compared to stations with increasing trends or no trends.

Trends in MODIS Geolocation Error Analysis

NASA Technical Reports Server (NTRS)

Wolfe, R. E.; Nishihama, Masahiro

2009-01-01

Data from the two MODIS instruments have been accurately geolocated (Earth located) to enable retrieval of global geophysical parameters. The authors describe the approach used to geolocate with sub-pixel accuracy over nine years of data from M0DIS on NASA's E0S Terra spacecraft and seven years of data from MODIS on the Aqua spacecraft. The approach uses a geometric model of the MODIS instruments, accurate navigation (orbit and attitude) data and an accurate Earth terrain model to compute the location of each MODIS pixel. The error analysis approach automatically matches MODIS imagery with a global set of over 1,000 ground control points from the finer-resolution Landsat satellite to measure static biases and trends in the MO0lS geometric model parameters. Both within orbit and yearly thermally induced cyclic variations in the pointing have been found as well as a general long-term trend.

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 2. Simulation Analysis Using the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Content Analysis of Research Trends in Instructional Design Models: 1999-2014

ERIC Educational Resources Information Center

Göksu, Idris; Özcan, Kursat Volkan; Çakir, Recep; Göktas, Yuksel

2017-01-01

This study examines studies on instructional design models by applying content analysis. It covers 113 papers published in 44 international Social Science Citation Index (SSCI) and Science Citation Index (SCI) journals. Studies on instructional design models are explored in terms of journal of publication, preferred model, country where the study…

Uncertainties in observations and climate projections for the North East India

NASA Astrophysics Data System (ADS)

Soraisam, Bidyabati; Karumuri, Ashok; D. S., Pai

2018-01-01

The Northeast-India has undergone many changes in climatic-vegetation related issues in the last few decades due to increased human activities. However, lack of observations makes it difficult to ascertain the climate change. The study involves the mean, seasonal cycle, trend and extreme-month analysis for summer-monsoon and winter seasons of observed climate data from Indian Meteorological Department (1° × 1°) and Aphrodite & CRU-reanalysis (both 0.5° × 0.5°), and five regional-climate-model simulations (LMDZ, MPI, GFDL, CNRM and ACCESS) data from AR5/CORDEX-South-Asia (0.5° × 0.5°). Long-term (1970-2005) observed, minimum and maximum monthly temperature and precipitation, and the corresponding CORDEX-South-Asia data for historical (1970-2005) and future-projections of RCP4.5 (2011-2060) have been analyzed for long-term trends. A large spread is found across the models in spatial distributions of various mean maximum/minimum climate statistics, though models capture a similar trend in the corresponding area-averaged seasonal cycles qualitatively. Our observational analysis broadly suggests that there is no significant trend in rainfall. Significant trends are observed in the area-averaged minimum temperature during winter. All the CORDEX-South-Asia simulations for the future project either a decreasing insignificant trend in seasonal precipitation, but increasing trend for both seasonal maximum and minimum temperature over the northeast India. The frequency of extreme monthly maximum and minimum temperature are projected to increase. It is not clear from future projections how the extreme rainfall months during JJAS may change. The results show the uncertainty exists in the CORDEX-South-Asia model projections over the region in spite of the relatively high resolution.

Evapotranspiration trends over the eastern United States during the 20th century

USGS Publications Warehouse

Kramer, Ryan J.; Bounoua, Lahouari; Zhang, Ping; Wolfe, Robert E.; Huntington, Thomas G.; Imhoff, Marc L.; Thome, Kurtis; Noyce, Genevieve L.

2015-01-01

Most models evaluated by the Intergovernmental Panel for Climate change estimate projected increases in temperature and precipitation with rising atmospheric CO2 levels. Researchers have suggested that increases in CO2 and associated increases in temperature and precipitation may stimulate vegetation growth and increase evapotranspiration (ET), which acts as a cooling mechanism, and on a global scale, may slow the climate-warming trend. This hypothesis has been modeled under increased CO2 conditions with models of different vegetation-climate dynamics. The significance of this vegetation negative feedback, however, has varied between models. Here we conduct a century-scale observational analysis of the Eastern US water balance to determine historical evapotranspiration trends and whether vegetation greening has affected these trends. We show that precipitation has increased significantly over the twentieth century while runoff has not. We also show that ET has increased and vegetation growth is partially responsible.

Satellite and Model Assessment of Regional Aerosol Trends and Potential Impacts on Clouds in the western North Atlantic Ocean

NASA Astrophysics Data System (ADS)

Jongeward, A.; Li, Z.

2014-12-01

Aerosols and clouds contribute to atmospheric variability and to Earth's radiative balance, and while aerosol-cloud interactions have been studied in the past, long-term assessments of their regional interactions are only beginning to be realized. Changes in emissions and air quality policies as well as socioeconomic factors ultimately lead to changes in AOD (aerosol optical depth) with cascading effects on clouds and ultimately on the combined radiative effects where agreement is yet to be seen. In this work, an assessment of any trends observed in the aerosol loading over the western North Atlantic Ocean during the period of 2000 to 2012 is presented. Monthly mean data from NASA's MODIS instruments onboard both Terra and Aqua satellites are employed. Two aerosol models (GOCART and MERRAero) with the capability to model five individual aerosol species are also used and can separate anthropogenic from natural contributions to the total aerosol load and the aerosol trend. Preliminary results show two distinct regions of opposite trend in the satellite AOD over the western North Atlantic. From analysis of the model trends, the trends in these two regions are also of different origin: the negative AOD trend (ranging from -0.020 to -0.040 per decade) seen just off the eastern coast of the U.S. is of anthropogenic origin while the positive AOD trend (ranging from 0.015 to 0.030 per decade) seen in the south of the domain is of natural origins. Compelling evidence from a ground-based aerosol record (AERONET) as well as EPA emissions records corroborates the anthropogenic origin of the negative trend off the eastern U.S. coast. Finally, any trends seen in the cloud effective radius are explored to examine the presence of the first indirect effect (Twomey effect). The analysis from Aqua appears stronger and more coherent, likely a testament to its calibration stability relative to Terra. Statistical significance tests are performed for the 90% and 95% levels using the Student's t-test. This research can not only provided information for modeling and validation studies of aerosol trends but also act as an initial study into the long-term impacts of air quality improvement policies on the aerosol field, aerosol-cloud interactions, and the combined complex radiative effects.

Long-term trend analysis on total and extreme precipitation over Shasta Dam watershed.

PubMed

Toride, Kinya; Cawthorne, Dylan L; Ishida, Kei; Kavvas, M Levent; Anderson, Michael L

2018-06-01

California's interconnected water system is one of the most advanced water management systems in the world, and understanding of long-term trends in atmospheric and hydrologic behavior has increasingly being seen as vital to its future well-being. Knowledge of such trends is hampered by the lack of long-period observation data and the uncertainty surrounding future projections of atmospheric models. This study examines historical precipitation trends over the Shasta Dam watershed (SDW), which lies upstream of one of the most important components of California's water system, Shasta Dam, using a dynamical downscaling methodology that can produce atmospheric data at fine time-space scales. The Weather Research and Forecasting (WRF) model is employed to reconstruct 159years of long-term hourly precipitation data at 3km spatial resolution over SDW using the 20th Century Reanalysis Version 2c dataset. Trend analysis on this data indicates a significant increase in total precipitation as well as a growing intensity of extreme events such as 1, 6, 12, 24, 48, and 72-hour storms over the period of 1851 to 2010. The turning point of the increasing trend and no significant trend periods is found to be 1940 for annual precipitation and the period of 1950 to 1960 for extreme precipitation using the sequential Mann-Kendall test. Based on these analysis, we find the trends at the regional scale do not necessarily apply to the watershed-scale. The sharp increase in the variability of annual precipitation since 1970s is also detected, which implies an increase in the occurrence of extreme wet and dry conditions. These results inform long-term planning decisions regarding the future of Shasta Dam and California's water system. Copyright © 2018 Elsevier B.V. All rights reserved.

Classifying geometric variability by dominant eigenmodes of deformation in regressing tumours during active breath-hold lung cancer radiotherapy

NASA Astrophysics Data System (ADS)

Badawi, Ahmed M.; Weiss, Elisabeth; Sleeman, William C., IV; Hugo, Geoffrey D.

2012-01-01

The purpose of this study is to develop and evaluate a lung tumour interfraction geometric variability classification scheme as a means to guide adaptive radiotherapy and improve measurement of treatment response. Principal component analysis (PCA) was used to generate statistical shape models of the gross tumour volume (GTV) for 12 patients with weekly breath hold CT scans. Each eigenmode of the PCA model was classified as ‘trending’ or ‘non-trending’ depending on whether its contribution to the overall GTV variability included a time trend over the treatment course. Trending eigenmodes were used to reconstruct the original semi-automatically delineated GTVs into a reduced model containing only time trends. Reduced models were compared to the original GTVs by analyzing the reconstruction error in the GTV and position. Both retrospective (all weekly images) and prospective (only the first four weekly images) were evaluated. The average volume difference from the original GTV was 4.3% ± 2.4% for the trending model. The positional variability of the GTV over the treatment course, as measured by the standard deviation of the GTV centroid, was 1.9 ± 1.4 mm for the original GTVs, which was reduced to 1.2 ± 0.6 mm for the trending-only model. In 3/13 cases, the dominant eigenmode changed class between the prospective and retrospective models. The trending-only model preserved GTV and shape relative to the original GTVs, while reducing spurious positional variability. The classification scheme appears feasible for separating types of geometric variability by time trend.

Analysis options for estimating status and trends in long-term monitoring

USGS Publications Warehouse

Bart, Jonathan; Beyer, Hawthorne L.

2012-01-01

This chapter describes methods for estimating long-term trends in ecological parameters. Other chapters in this volume discuss more advanced methods for analyzing monitoring data, but these methods may be relatively inaccessible to some readers. Therefore, this chapter provides an introduction to trend analysis for managers and biologists while also discussing general issues relevant to trend assessment in any long-term monitoring program. For simplicity, we focus on temporal trends in population size across years. We refer to the survey results for each year as the “annual means” (e.g. mean per transect, per plot, per time period). The methods apply with little or no modification, however, to formal estimates of population size, other temporal units (e.g. a month), to spatial or other dimensions such as elevation or a north–south gradient, and to other quantities such as chemical or geological parameters. The chapter primarily discusses methods for estimating population-wide parameters rather than studying variation in trend within the population, which can be examined using methods presented in other chapters (e.g. Chapters 7, 12, 20). We begin by reviewing key concepts related to trend analysis. We then describe how to evaluate potential bias in trend estimates. An overview of the statistical models used to quantify trends is then presented. We conclude by showing ways to estimate trends using simple methods that can be implemented with spreadsheets.

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.

Analysis of high-resolution simulations for the Black Forest region from a point of view of tourism climatology - a comparison between two regional climate models (REMO and CLM)

NASA Astrophysics Data System (ADS)

Endler, Christina; Matzarakis, Andreas

2011-03-01

An analysis of climate simulations from a point of view of tourism climatology based on two regional climate models, namely REMO and CLM, was performed for a regional domain in the southwest of Germany, the Black Forest region, for two time frames, 1971-2000 that represents the twentieth century climate and 2021-2050 that represents the future climate. In that context, the Intergovernmental Panel on Climate Change (IPCC) scenarios A1B and B1 are used. The analysis focuses on human-biometeorological and applied climatologic issues, especially for tourism purposes - that means parameters belonging to thermal (physiologically equivalent temperature, PET), physical (precipitation, snow, wind), and aesthetic (fog, cloud cover) facets of climate in tourism. In general, both models reveal similar trends, but differ in their extent. The trend of thermal comfort is contradicting: it tends to decrease in REMO, while it shows a slight increase in CLM. Moreover, REMO reveals a wider range of future climate trends than CLM, especially for sunshine, dry days, and heat stress. Both models are driven by the same global coupled atmosphere-ocean model ECHAM5/MPI-OM. Because both models are not able to resolve meso- and micro-scale processes such as cloud microphysics, differences between model results and discrepancies in the development of even those parameters (e.g., cloud formation and cover) are due to different model parameterization and formulation. Climatic changes expected by 2050 are small compared to 2100, but may have major impacts on tourism as for example, snow cover and its duration are highly vulnerable to a warmer climate directly affecting tourism in winter. Beyond indirect impacts are of high relevance as they influence tourism as well. Thus, changes in climate, natural environment, demography, tourists' demands, among other things affect economy in general. The analysis of the CLM results and its comparison with the REMO results complete the analysis performed within the project Climate Trends and Sustainable Development of Tourism in Coastal and Low Mountain Range Regions (CAST) funded by the German Federal Ministry of Education and Research (BMBF).

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.

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.

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.

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.

Power analysis and trend detection for water quality monitoring data. An application for the Greater Yellowstone Inventory and Monitoring Network

USGS Publications Warehouse

Irvine, Kathryn M.; Manlove, Kezia; Hollimon, Cynthia

2012-01-01

An important consideration for long term monitoring programs is determining the required sampling effort to detect trends in specific ecological indicators of interest. To enhance the Greater Yellowstone Inventory and Monitoring Network’s water resources protocol(s) (O’Ney 2006 and O’Ney et al. 2009 [under review]), we developed a set of tools to: (1) determine the statistical power for detecting trends of varying magnitude in a specified water quality parameter over different lengths of sampling (years) and different within-year collection frequencies (monthly or seasonal sampling) at particular locations using historical data, and (2) perform periodic trend analyses for water quality parameters while addressing seasonality and flow weighting. A power analysis for trend detection is a statistical procedure used to estimate the probability of rejecting the hypothesis of no trend when in fact there is a trend, within a specific modeling framework. In this report, we base our power estimates on using the seasonal Kendall test (Helsel and Hirsch 2002) for detecting trend in water quality parameters measured at fixed locations over multiple years. We also present procedures (R-scripts) for conducting a periodic trend analysis using the seasonal Kendall test with and without flow adjustment. This report provides the R-scripts developed for power and trend analysis, tutorials, and the associated tables and graphs. The purpose of this report is to provide practical information for monitoring network staff on how to use these statistical tools for water quality monitoring data sets.

Effects on U.S. Timber Outlook of Recent Economic Recession, Collapse in Housing Construction, and Wood Energy Trends

Treesearch

Peter J. Ince; Prakash Nepal

2012-01-01

This paper reviews recent trends and structural changes in U.S. forest product markets and projects their effects on the long-range U.S. timber market outlook. The analysis derives from the same U.S. and global economic model that produced 50-year projections for the 2010 RPA nationwide forest assessment, but analysis is revised to more accurately include the economic...

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 1. The Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

An Analysis of Inter-annual Variability and Uncertainty of Continental Surface Heat Fluxes

NASA Astrophysics Data System (ADS)

Huang, S. Y.; Deng, Y.; Wang, J.

2016-12-01

The inter-annual variability and the corresponding uncertainty of land surface heat fluxes during the first decade of the 21st century are re-evaluated at continental scale based on the heat fluxes estimated by the maximum entropy production (MEP) model. The MEP model predicted heat fluxes are constrained by surface radiation fluxes, automatically satisfy surface energy balance, and are independent of temperature/moisture gradient, wind speed, and roughness lengths. The surface radiation fluxes and temperature data from Clouds and the Earth's Radiant Energy System and the surface specific humidity data from Modern-Era Retrospective analysis for Research and Applications were used to reproduce the global surface heat fluxes with land-cover data from the NASA Energy and Water cycle Study (NEWS). Our analysis shows that the annual means of continental latent heat fluxes have increasing trends associated with increasing trends in surface net radiative fluxes. The sensible heat fluxes also have increasing trends over most continents except for South America. Ground heat fluxes have little trends. The continental-scale analysis of the MEP fluxes are compared with other existing global surface fluxes data products and the implications of the results for inter-annual to decadal variability of regional surface energy budget are discussed.

A Bibliometric Analysis on Cancer Population Science with Topic Modeling.

PubMed

Li, Ding-Cheng; Rastegar-Mojarad, Majid; Okamoto, Janet; Liu, Hongfang; Leichow, Scott

2015-01-01

Bibliometric analysis is a research method used in library and information science to evaluate research performance. It applies quantitative and statistical analyses to describe patterns observed in a set of publications and can help identify previous, current, and future research trends or focus. To better guide our institutional strategic plan in cancer population science, we conducted bibliometric analysis on publications of investigators currently funded by either Division of Cancer Preventions (DCP) or Division of Cancer Control and Population Science (DCCPS) at National Cancer Institute. We applied two topic modeling techniques: author topic modeling (AT) and dynamic topic modeling (DTM). Our initial results show that AT can address reasonably the issues related to investigators' research interests, research topic distributions and popularities. In compensation, DTM can address the evolving trend of each topic by displaying the proportion changes of key words, which is consistent with the changes of MeSH headings.

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.

Variations in northern hemisphere snowfall: An analysis of historical trends and the projected response to anthropogenic forcing in the twenty-first century

NASA Astrophysics Data System (ADS)

Krasting, John P.

Snowfall is an important feature of the Earth's climate system that has the ability to influence both the natural world and human activity. This dissertation examines past and future changes in snowfall related to increasing concentrations of anthropogenic greenhouse gases. Snowfall observations for North America, derived snowfall products for the Northern Hemisphere, and simulations performed with 13 coupled atmosphere-ocean global climate models are analyzed. The analysis of the spatial pattern of simulated annual trends on a grid point basis from 1951 to 1999 indicates that a transition zone exists above 60° N latitude across the Northern Hemisphere that separates negative trends in annual snowfall in the mid-latitudes and positive trends at higher latitudes. Regional analysis of observed annual snowfall indicates that statistically significant trends are found in western North America, Japan, and southern Russia. A majority of the observed historical trends in annual snowfall elsewhere in the Northern Hemisphere, however, are not statistically significant and this result is consistent with model simulations. Projections of future snowfall indicate the presence of a similar transition zone between negative and positive snowfall trends that corresponds with the area between the -10 to -15°C isotherms of the multi-model mean temperature of the late twentieth century in each of the fall, winter, and spring seasons. Redistributions of snowfall throughout the entire snow season are likely -- even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in precipitation typically contribute to increases in snowfall. Snowfall events less than or equal to 5 cm are found to decrease in the future across most of the Northern Hemisphere, while snowfall events greater than or equal to 20 cm increase in some locations, such as northern Quebec. A signal-to-noise analysis reveals that the projected changes in snowfall are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere.

A spatial econometric analysis of land-use change with land cover trends data: an application to the Pacific Northwest

Treesearch

David J. Lewis; Ralph J. Alig

2014-01-01

This paper develops a plot-level spatial econometric land-use model and estimates it with U.S. Geological Survey Land Cover Trends (LCT) geographic information system panel data for the western halves of the states of Oregon and Washington. The discrete-choice framework we use models plot-scale choices of the three dominant land uses in this region: forest, agriculture...

Health care units and human resources management trends.

PubMed

André, Adriana Maria; Ciampone, Maria Helena Trench; Santelle, Odete

2013-02-01

To identify factors producing new trends in basic health care unit management and changes in management models. This was a prospective study with ten health care unit managers and ten specialists in the field of Health in São Paulo, Southeastern Brazil, in 2010. The Delphi methodology was adopted. There were four stages of data collection, three quantitative and the fourth qualitative. The first three rounds dealt with changing trends in management models, manager profiles and required competencies, and the Mann-Whitney test was used in the analysis. The fourth round took the form of a panel of those involved, using thematic analysis. The main factors which are driving change in basic health care units were identified, as were changes in management models. There was consensus that this process is influenced by the difficulties in managing teams and by politics. The managers were found to be up-to-date with trends in the wider context, with the arrival of social health organizations, but they are not yet anticipating these within the institutions. Not only the content, but the professional development aspect of training courses in this area should be reviewed. Selection and recruitment, training and assessment of these professionals should be guided by these competencies aligned to the health service mission, vision, values and management models.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

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.

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.

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

HISTORICAL EMISSION AND OZONE TRENDS IN THE HOUSTON AREA

EPA Science Inventory

An analysis of historical trend data for emissions and air quality in Houston for period of 1974-78 is conducted for the purposes of checking the EKMA O3-predicting model and of exploring empirical relations between emission changes and O3 air quality in the Houston area. Results...

SENSITIVITY ANALYSIS OF THE MULTI-LAYER MODEL USED IN THE CLEAN AIR STATUS AND TRENDS NETWORK (CASTNET)

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA) established the Clean Air Status and Trends Network (CASTNET) and its predecessor, the National Dry Deposition Network (NDDN), as national air quality and meteorological monitoring networks. The purpose of CASTNET is to track the pr...

Examining Long-Term Trends in Mobile Source Related Pollutants through Analysis of Emissions, Observations and Model Simulations

EPA Science Inventory

Anthropogenic emissions from a variety of sectors including mobile sources have decreased substantially over the past decades despite continued growth in population and economic activity. In this study, we analyze 1990-2010 trends in emission inventories, ambient observations and...

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.

Observed SWE trends and climate analysis for Northwest Pacific North America: validation for future projection of SWE using the CRCM and VIC

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Bronaugh, D.; Rodenhuis, D.

2008-12-01

Observational databases of snow water equivalent (SWE) have been collected from Alaska, western US states and the Canadian provinces of British Columbia, Alberta, Saskatchewan, and territories of NWT, and the Yukon. These databases were initially validated to remove inconsistencies and errors in the station records, dates or the geographic co-ordinates of the station. The cleaned data was then analysed for historical (1950 to 2006) trend using emerging techniques for trend detection based on (first of the month) estimates for January to June. Analysis of SWE showed spatial variability in the count of records across the six month time period, and this study illustrated differences between Canadian and US (or the north and south) collection. Two different data sets (one gridded and one station) were then used to analyse April 1st records, for which there was the greatest spatial spread of station records for analysis with climate information. Initial results show spatial variability (in both magnitude and direction of trend) for trend results, and climate correlations and principal components indicate different drivers of change in SWE across the western US, Canada and north to Alaska. These results will be used to validate future predictions of SWE that are being undertaken using the Canadian Regional Climate Model (CRCM) and the Variable Infiltration Capacity (VIC) hydrologic model for Western Northern America (CRCM) and British Columbia (VIC).

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

Temporal trend of the snow-related variables in Sierra Nevada in the last years: An analysis combining Earth Observation and hydrological modelling

NASA Astrophysics Data System (ADS)

Pérez-Luque, Antonio J.; Herrero, Javier; Bonet, Francisco J.; Pérez-Pérez, Ramón

2016-04-01

Climate change is causing declines in snow-cover extent and duration in European mountain ranges. This is especially important in Mediterranean mountain ranges where the observed trends towards precipitation and higher temperatures can provoke problems of water scarcity. In this work, we analyzed temporal trends (2000 to 2014) of snow-related variables obtained from satellite and modelling data in Sierra Nevada, a Mediterranean high-mountain range located in Southern Spain, at 37°N. Snow cover indicators (snow-cover duration, snow-cover onset dates and snow-cover melting dates) were obtained by processing images of MOD10A2 MODIS product using an automated workflow. Precipitation data were obtained using WiMMed, a complete and fully distributed hydrological model that is used to map the annual rainfall and snowfall with a resolution of 30x30 m over the whole study area. It uses expert algorithms to interpolate precipitation and temperature at an hourly scale, and simulates partition of precipitation into snowfall with several methods. For each snow-related indicator (snow-covers and snowfall), a trend analysis was applied at the MODIS pixel scale during the study period (2000-2014). We applied Mann-Kendall test and Theil-Sen slope estimation in each of the pixels comprising Sierra Nevada. The trend analysis assesses the intensity, magnitude and degree of statistical significance during the period analysed. The spatial pattern of these trends was explored according to elevation ranges. Finally, we explored the relationship between trends of snow-cover related indicators and precipitation trends. Our results show that snow-cover has undergone significant changes in the last 14 years. 80 % of the pixels covering Sierra Nevada showed a negative trend in the duration of snow-cover. We also observed a delay in the snow-cover onset date (68.03 % pixels showing a positive trend in the snow-cover onset date) and an advance in the melt date (80.72 % of pixels followed a negative trend for the snow-cover melting date). Precipitation does not show a significant trend for these years, even though its inter-annual variability has been outstanding. The maximum mean annual precipitation of 906 mm/year doubles the mean precipitation, which somehow compensates for the occurrence of a sequence of dry years with a minimum of 250 mm/year. The assessment of the spatial pattern of snow cover duration shows that both the trend and the slope of the trend becomes more pronounced with elevation. At higher elevations the snow-cover duration decreased an average of 3 days from 2000-2014. This research has been funded by ECOPOTENTIAL (Improving future ecosystem benefits through Earth Observations) Horizon 2020 EU project, and Sierra Nevada Global Change Observatory (LTER-site)

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.

Analysis of the Automobile Market : Modeling the Long-Run Determinants of the Demand for Automobiles : Volume 3. Appendices to the Wharton EFA Automobile Demand Model

DOT National Transportation Integrated Search

1979-12-01

An econometric model is developed which provides long-run policy analysis and forecasting of annual trends, for U.S. auto stock, new sales, and their composition by auto size-class. The concept of "desired" (equilibrium) stock is introduced. "Desired...

Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model.

ERIC Educational Resources Information Center

Muraki, Eiji

1999-01-01

Extended an Item Response Theory (IRT) method for detection of differential item functioning to the partial credit model and applied the method to simulated data using a stepwise procedure. Then applied the stepwise DIF analysis based on the multiple-group partial credit model to writing trend data from the National Assessment of Educational…

An Economic Analysis of Investment in the United States Shipbuilding Industry

DTIC Science & Technology

2010-06-01

using U.S. Bureau of Economic Analysis (BEA) input/output data and the “Leontief inversion process” modeled at Carnegie Mellon University. This... modeled at Carnegie Mellon University. This sector was compared with five alternative investments. Second, the benefits of the shipyard-related...EIO-LCA Model ..................................39 2. Shipyard Direct Labor Trends .........................................................43 viii 3

A human-driven decline in global burned area

NASA Astrophysics Data System (ADS)

Andela, N.; Morton, D. C.; Chen, Y.; van der Werf, G.; Giglio, L.; Kasibhatla, P. S.; Randerson, J. T.

2016-12-01

Fire is an important and dynamic ecosystem process that influences many aspects of the global Earth system. Here, we used several different satellite datasets to assess trends in global burned area during 1998 to 2014. Global burned area decreased by about 21.6 ± 8.5% over the period from 1998-2014, with large regional declines observed in savanna and grassland ecosystems in northern Africa, Eurasia, and South America. The decrease in burned area remained robust after removing the influence of climate (16.0 ± 6.0%), implicating human activity as a likely driver. To further investigate the mechanisms contributing to regional and global trends, we conducted several kinds of analysis, including separation of burned area into ignition and fire size components and geospatial analysis of fire trends in relationship with demographic and land use variables. We found that fire number was a more important factor contributing to burned area trends than fire size, suggesting a reduction in the use of fire for management purposes. Concurrent decreases in fire size also contributed to the trend outside of North and South America, suggesting a role for greater landscape fragmentation. From our geospatial analysis, we developed a conceptual model that incorporates a range of drivers for human-driven changes in biomass burning that can be used to guide global fire models, currently unable to reproduce these large scale recent trends. Patterns of agricultural expansion and land use intensification are likely to further contribute to declining burned area trends in future decades, with important consequences for Earth system processes mediated by surface albedo, greenhouse gas emissions, and aerosols. Our results also highlight the vulnerability of savannas and grassland to land use changes with unprecedented global scale consequences for vegetation structure and the carbon cycle.

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;

Empirical support for global integrated assessment modeling: Productivity trends and technological change in developing countries' agriculture and electric power sectors

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

Sathaye, Jayant A.

2000-04-01

Integrated assessment (IA) modeling of climate policy is increasingly global in nature, with models incorporating regional disaggregation. The existing empirical basis for IA modeling, however, largely arises from research on industrialized economies. Given the growing importance of developing countries in determining long-term global energy and carbon emissions trends, filling this gap with improved statistical information on developing countries' energy and carbon-emissions characteristics is an important priority for enhancing IA modeling. Earlier research at LBNL on this topic has focused on assembling and analyzing statistical data on productivity trends and technological change in the energy-intensive manufacturing sectors of five developing countries,more » India, Brazil, Mexico, Indonesia, and South Korea. The proposed work will extend this analysis to the agriculture and electric power sectors in India, South Korea, and two other developing countries. They will also examine the impact of alternative model specifications on estimates of productivity growth and technological change for each of the three sectors, and estimate the contribution of various capital inputs--imported vs. indigenous, rigid vs. malleable-- in contributing to productivity growth and technological change. The project has already produced a data resource on the manufacturing sector which is being shared with IA modelers. This will be extended to the agriculture and electric power sectors, which would also be made accessible to IA modeling groups seeking to enhance the empirical descriptions of developing country characteristics. The project will entail basic statistical and econometric analysis of productivity and energy trends in these developing country sectors, with parameter estimates also made available to modeling groups. The parameter estimates will be developed using alternative model specifications that could be directly utilized by the existing IAMs for the manufacturing, agriculture, and electric power sectors.« less

Impact of remote sensing upon the planning, management and development of water resources. Summary of computers and computer growth trends for hydrologic modeling and the input of ERTS image data processing load

NASA Technical Reports Server (NTRS)

Castruccio, P. A.; Loats, H. L., Jr.

1975-01-01

An analysis of current computer usage by major water resources users was made to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era. The analysis showns significant impact due to the utilization and processing of ERTS CCT's data.

Learning from Trending, Precursor Analysis, and System Failures

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

Youngblood, R. W.; Duffey, R. B.

2015-11-01

Models of reliability growth relate current system unreliability to currently accumulated experience. But “experience” comes in different forms. Looking back after a major accident, one is sometimes able to identify previous events or measurable performance trends that were, in some sense, signaling the potential for that major accident: potential that could have been recognized and acted upon, but was not recognized until the accident occurred. This could be a previously unrecognized cause of accidents, or underestimation of the likelihood that a recognized potential cause would actually operate. Despite improvements in the state of practice of modeling of risk and reliability,more » operational experience still has a great deal to teach us, and work has been going on in several industries to try to do a better job of learning from experience before major accidents occur. It is not enough to say that we should review operating experience; there is too much “experience” for such general advice to be considered practical. The paper discusses the following: 1. The challenge of deciding what to focus on in analysis of operating experience. 2. Comparing what different models of learning and reliability growth imply about trending and precursor analysis.« less

Climate change impacts on rainfall extremes and urban drainage: state-of-the-art review

NASA Astrophysics Data System (ADS)

Willems, Patrick; Olsson, Jonas; Arnbjerg-Nielsen, Karsten; Beecham, Simon; Pathirana, Assela; Bülow Gregersen, Ida; Madsen, Henrik; Nguyen, Van-Thanh-Van

2013-04-01

Under the umbrella of the IWA/IAHR Joint Committee on Urban Drainage, the International Working Group on Urban Rainfall (IGUR) has reviewed existing methodologies for the analysis of long-term historical and future trends in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic climate change. Current practises have several limitations and pitfalls, which are important to be considered by trend or climate change impact modellers and users of trend/impact results. The review considers the following aspects: Analysis of long-term historical trends due to anthropogenic climate change: influence of data limitation, instrumental or environmental changes, interannual variations and longer term climate oscillations on trend testing results. Analysis of long-term future trends due to anthropogenic climate change: by complementing empirical historical data with the results from physically-based climate models, dynamic downscaling to the urban scale by means of Limited Area Models (LAMs) including explicitly small-scale cloud processes; validation of RCM/GCM results for local conditions accounting for natural variability, limited length of the available time series, difference in spatial scales, and influence of climate oscillations; statistical downscaling methods combined with bias correction; uncertainties associated with the climate forcing scenarios, the climate models, the initial states and the statistical downscaling step; uncertainties in the impact models (e.g. runoff peak flows, flood or surcharge frequencies, and CSO frequencies and volumes), including the impacts of more extreme conditions than considered during impact model calibration and validation. Implications for urban drainage infrastructure design and management: upgrading of the urban drainage system as part of a program of routine and scheduled replacement and renewal of aging infrastructure; how to account for the uncertainties; flexible and sustainable solutions; adaptive approach that provides inherent flexibility and reversibility and avoids closing off options; importance of active learning. References: Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Bülow Gregersen, I., Madsen, H., Nguyen, V-T-V. (2012). Impacts of climate change on rainfall extremes and urban drainage. IWA Publishing, 252 p., Paperback Print ISBN 9781780401256; Ebook ISBN 9781780401263 Willems, P., Arnbjerg-Nielsen, K., Olsson, J., Nguyen, V.T.V. (2012), 'Climate change impact assessment on urban rainfall extremes and urban drainage: methods and shortcomings', Atmospheric Research, 103, 106-118

A Methodological Framework for Model Selection in Interrupted Time Series Studies.

PubMed

Lopez Bernal, J; Soumerai, S; Gasparrini, A

2018-06-06

Interrupted time series is a powerful and increasingly popular design for evaluating public health and health service interventions. The design involves analysing trends in the outcome of interest and estimating the change in trend following an intervention relative to the counterfactual (the expected ongoing trend if the intervention had not occurred). There are two key components to modelling this effect: first, defining the counterfactual; second, defining the type of effect that the intervention is expected to have on the outcome, known as the impact model. The counterfactual is defined by extrapolating the underlying trends observed before the intervention to the post-intervention period. In doing this, authors must consider the pre-intervention period that will be included, any time varying confounders, whether trends may vary within different subgroups of the population and whether trends are linear or non-linear. Defining the impact model involves specifying the parameters that model the intervention, including for instance whether to allow for an abrupt level change or a gradual slope change, whether to allow for a lag before any effect on the outcome, whether to allow a transition period during which the intervention is being implemented and whether a ceiling or floor effect might be expected. Inappropriate model specification can bias the results of an interrupted time series analysis and using a model that is not closely tailored to the intervention or testing multiple models increases the risk of false positives being detected. It is important that authors use substantive knowledge to customise their interrupted time series model a priori to the intervention and outcome under study. Where there is uncertainty in model specification, authors should consider using separate data sources to define the intervention, running limited sensitivity analyses or undertaking initial exploratory studies. Copyright © 2018. Published by Elsevier Inc.

Climatic trends over Ethiopia: regional signals and drivers

USGS Publications Warehouse

Jury, Mark R.; Funk, Christopher C.

2013-01-01

This study analyses observed and projected climatic trends over Ethiopia, through analysis of temperature and rainfall records and related meteorological fields. The observed datasets include gridded station records and reanalysis products; while projected trends are analysed from coupled model simulations drawn from the IPCC 4th Assessment. Upward trends in air temperature of + 0.03 °C year−1 and downward trends in rainfall of − 0.4 mm month−1 year−1 have been observed over Ethiopia's southwestern region in the period 1948-2006. These trends are projected to continue to 2050 according to the Geophysical Fluid Dynamics Lab model using the A1B scenario. Large scale forcing derives from the West Indian Ocean where significant warming and increased rainfall are found. Anticyclonic circulations have strengthened over northern and southern Africa, limiting moisture transport from the Gulf of Guinea and Congo. Changes in the regional Walker and Hadley circulations modulate the observed and projected climatic trends. Comparing past and future patterns, the key features spread westward from Ethiopia across the Sahel and serve as an early warning of potential impacts.

seawaveQ: an R package providing a model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.

2013-01-01

The seawaveQ R package fits a parametric regression model (seawaveQ) to pesticide concentration data from streamwater samples to assess variability and trends. The model incorporates the strong seasonality and high degree of censoring common in pesticide data and users can incorporate numerous ancillary variables, such as streamflow anomalies. The model is fitted to pesticide data using maximum likelihood methods for censored data and is robust in terms of pesticide, stream location, and degree of censoring of the concentration data. This R package standardizes this methodology for trend analysis, documents the code, and provides help and tutorial information, as well as providing additional utility functions for plotting pesticide and other chemical concentration data.

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.…

Development of a methodology to assess future trends in low flows at the watershed scale using solely climate data

NASA Astrophysics Data System (ADS)

Foulon, Étienne; Rousseau, Alain N.; Gagnon, Patrick

2018-02-01

Low flow conditions are governed by short-to-medium term weather conditions or long term climate conditions. This prompts the question: given climate scenarios, is it possible to assess future extreme low flow conditions from climate data indices (CDIs)? Or should we rely on the conventional approach of using outputs of climate models as inputs to a hydrological model? Several CDIs were computed using 42 climate scenarios over the years 1961-2100 for two watersheds located in Québec, Canada. The relationship between the CDIs and hydrological data indices (HDIs; 7- and 30-day low flows for two hydrological seasons) were examined through correlation analysis to identify the indices governing low flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs to trends in specific CDIs. Furthermore, results showed that, even during the spatial validation process, the methodological framework was able to assess trends in low flow series from: (i) trends in the effective drought index (EDI) computed from rainfall plus snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover season or (ii) the cumulative difference between rainfall and potential evapotranspiration over five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were successfully attributed to trends in CDIs. Overall, this paper introduces an efficient methodological framework to assess future trends in low flows given climate scenarios. The outcome may prove useful to municipalities concerned with source water management under changing climate conditions.

Population Trend and Elasticities of Vital Rates for Steller Sea Lions (Eumetopias jubatus) in the Eastern Gulf of Alaska: A New Life-History Table Analysis

PubMed Central

Maniscalco, John M.; Springer, Alan M.; Adkison, Milo D.; Parker, Pamela

2015-01-01

Steller sea lion (Eumetopias jubatus) numbers are beginning to recover across most of the western distinct population segment following catastrophic declines that began in the 1970s and ended around the turn of the century. This study makes use of contemporary vital rate estimates from a trend-site rookery in the eastern Gulf of Alaska (a sub-region of the western population) in a matrix population model to estimate the trend and strength of the recovery across this region between 2003 and 2013. The modeled population trend was projected into the future based on observed variation in vital rates and a prospective elasticity analysis was conducted to determine future trends and which vital rates pose the greatest threats to recovery. The modeled population grew at a mean rate of 3.5% per yr between 2003 and 2013 and was correlated with census count data from the local rookery and throughout the eastern Gulf of Alaska. If recent vital rate estimates continue with little change, the eastern Gulf of Alaska population could be fully recovered to pre-decline levels within 23 years. With density dependent growth, the population would need another 45 years to fully recover. Elasticity analysis showed that, as expected, population growth rate (λ) was most sensitive to changes in adult survival, less sensitive to changes in juvenile survival, and least sensitive to changes in fecundity. A population decline could be expected with only a 6% decrease in adult survival, whereas a 32% decrease in fecundity would be necessary to bring about a population decline. These results have important implications for population management and suggest current research priorities should be shifted to a greater emphasis on survival rates and causes of mortality. PMID:26488901

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

PubMed

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-Year Revenue and Expenditure Forecasting for Small Municipal Governments.

DTIC Science & Technology

1981-03-01

Management Audit Econometric Revenue Forecast Gap and Impact Analysis Deterministic Expenditure Forecast Municipal Forecasting Municipal Budget Formlto...together with a multi-year revenue and expenditure forecasting model for the City of Monterey, California. The Monterey model includes an econometric ...65 5 D. FORECAST BASED ON THE ECONOMETRIC MODEL ------- 67 E. FORECAST BASED ON EXPERT JUDGMENT AND TREND ANALYSIS

Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan

NASA Astrophysics Data System (ADS)

Amin, Asad; Nasim, Wajid; Mubeen, Muhammad; Kazmi, Dildar Hussain; Lin, Zhaohui; Wahid, Abdul; Sultana, Syeda Refat; Gibbs, Jim; Fahad, Shah

2017-09-01

Unpredictable precipitation trends have largely influenced by climate change which prolonged droughts or floods in South Asia. Statistical analysis of monthly, seasonal, and annual precipitation trend carried out for different temporal (1996-2015 and 2041-2060) and spatial scale (39 meteorological stations) in Pakistan. Statistical downscaling model (SimCLIM) was used for future precipitation projection (2041-2060) and analyzed by statistical approach. Ensemble approach combined with representative concentration pathways (RCPs) at medium level used for future projections. The magnitude and slop of trends were derived by applying Mann-Kendal and Sen's slop statistical approaches. Geo-statistical application used to generate precipitation trend maps. Comparison of base and projected precipitation by statistical analysis represented by maps and graphical visualization which facilitate to detect trends. Results of this study projects that precipitation trend was increasing more than 70% of weather stations for February, March, April, August, and September represented as base years. Precipitation trend was decreased in February to April but increase in July to October in projected years. Highest decreasing trend was reported in January for base years which was also decreased in projected years. Greater variation in precipitation trends for projected and base years was reported in February to April. Variations in projected precipitation trend for Punjab and Baluchistan highly accredited in March and April. Seasonal analysis shows large variation in winter, which shows increasing trend for more than 30% of weather stations and this increased trend approaches 40% for projected precipitation. High risk was reported in base year pre-monsoon season where 90% of weather station shows increasing trend but in projected years this trend decreased up to 33%. Finally, the annual precipitation trend has increased for more than 90% of meteorological stations in base (1996-2015) which has decreased for projected year (2041-2060) up to 76%. These result revealed that overall precipitation trend is decreasing in future year which may prolonged the drought in 14% of weather stations under study.

Long-term trends in the total electron content (TEC)

NASA Astrophysics Data System (ADS)

Laštovička, Jan

2017-04-01

The long-term trends in the total electron content (TEC) have very little been studied. Lean et al. (2011; J. Geophys. Res., 116, A00H04, doi:10.1029/2010JA016378) studied trends in TEC globally based on JPL maps for 1995-2010. However, their trends appear to be too positive, which is not plausible taking into account the trends in other ionospheric parameters. Therefore they prefer the less positive trends calculated under the assumption of the same level of solar activity in solar cycle minima 22/23 and 23/24. However, as it is now clear, this is not a correct assumption. Lastovicka (2013; J. Geophys. Res. Space Phys., 118, 3831-3835, doi:10.1002/jgra.50261) selected a region around Florence, Italy, as a region with available historical TEC data based on Faraday rotation measurements and remarkably larger than average trends in TEC by Lean et al. (2011). Historical data from Florence provide no trend in TEC. However, foF2 from Juliusruh provide slight negative trends for 1976-1996 but no trends for 1995-2010. Thus the question of reality of trends by Lean et al. (2011) remained open. Here we use TEC from GIM and JPL data for two European regions with high Lean's trends, regions around Florence and around Prague, using 10-14 LT medians, 1998-2015, yearly average values. A classical approach is applied. First a model of solar activity dependence of TEC is constructed separately for each region from all data. Then model data are subtracted from experimental data and analysis is made with residuals. This analysis shows that early data (1998-2001) are by several TECU lower than they should be according to solar activity, the year 2002 is intermediate and in 2003-2015 the data fit well a weak or rather no trend of TEC. The change in TEC data does not seem to be jump-like, it lasted at least a year, if not longer. Thus the positive TEC trends reported by Lean et al. (2011) appear to be affected by data problem; real trends are evidently less positive if any.

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.

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.

Trends in Mediation Analysis in Nursing Research: Improving Current Practice.

PubMed

Hertzog, Melody

2018-06-01

The purpose of this study was to describe common approaches used by nursing researchers to test mediation models and evaluate them within the context of current methodological advances. MEDLINE was used to locate studies testing a mediation model and published from 2004 to 2015 in nursing journals. Design (experimental/correlation, cross-sectional/longitudinal, model complexity) and analysis (method, inclusion of test of mediated effect, violations/discussion of assumptions, sample size/power) characteristics were coded for 456 studies. General trends were identified using descriptive statistics. Consistent with findings of reviews in other disciplines, evidence was found that nursing researchers may not be aware of the strong assumptions and serious limitations of their analyses. Suggestions for strengthening the rigor of such studies and an overview of current methods for testing more complex models, including longitudinal mediation processes, are presented.

Simultaneous or separated; comparison approach for saccharification and fermentation process in producing bio-ethanol from EFB

NASA Astrophysics Data System (ADS)

Bardant, Teuku Beuna; Dahnum, Deliana; Amaliyah, Nur

2017-11-01

Simultaneous Saccharification Fermentation (SSF) of palm oil (Elaeis guineensis) empty fruit bunch (EFB) pulp were investigated as a part of ethanol production process. SSF was investigated by observing the effect of substrate loading variation in range 10-20%w, cellulase loading 5-30 FPU/gr substrate and yeast addition 1-2%v to the ethanol yield. Mathematical model for describing the effects of these three variables to the ethanol yield were developed using Response Surface Methodology-Cheminformatics (RSM-CI). The model gave acceptable accuracy in predicting ethanol yield for Simultaneous Saccharification and Fermentation (SSF) with coefficient of determination (R2) 0.8899. Model validation based on data from previous study gave (R2) 0.7942 which was acceptable for using this model for trend prediction analysis. Trend prediction analysis based on model prediction yield showed that SSF gave trend for higher yield when the process was operated in high enzyme concentration and low substrate concentration. On the other hand, even SHF model showed better yield will be obtained if operated in lower substrate concentration, it still possible to operate in higher substrate concentration with slightly lower yield. Opportunity provided by SHF to operate in high loading substrate make it preferable option for application in commercial scale.

Colorectal cancer mortality trends in Serbia during 1991-2010: an age-period-cohort analysis and a joinpoint regression analysis.

PubMed

Ilic, Milena; Ilic, Irena

2016-06-22

For both men and women worldwide, colorectal cancer is among the leading causes of cancer-related death. This study aimed to assess the mortality trends of colorectal cancer in Serbia between 1991 and 2010, prior to the introduction of population-based screening. Joinpoint regression analysis was used to estimate average annual percent change (AAPC) with the corresponding 95% confidence interval (CI). Furthermore, age-period-cohort analysis was performed to examine the effects of birth cohort and calendar period on the observed temporal trends. We observed a significantly increased trend in colorectal cancer mortality in Serbia during the study period (AAPC = 1.6%, 95% CI 1.3%-1.8%). Colorectal cancer showed an increased mortality trend in both men (AAPC = 2.0%, 95% CI 1.7%-2.2%) and women (AAPC = 1.0%, 95% CI 0.6%-1.4%). The temporal trend of colorectal cancer mortality was significantly affected by birth cohort (P < 0.05), whereas the study period did not significantly affect the trend (P = 0.072). Colorectal cancer mortality increased for the first several birth cohorts in Serbia (from 1916 to 1955), followed by downward flexion for people born after the 1960s. According to comparability test, overall mortality trends for colon cancer and rectal and anal cancer were not parallel (the final selected model rejected parallelism, P < 0.05). We found that colorectal cancer mortality in Serbia increased considerably over the past two decades. Mortality increased particularly in men, but the trends were different according to age group and subsite. In Serbia, interventions to reduce colorectal cancer burden, especially the implementation of a national screening program, as well as treatment improvements and measures to encourage the adoption of a healthy lifestyle, are needed.

Pesticide trends in major rivers of the United States, 1992-2010

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.; Gilliom, Robert J.; Martin, Jeffrey D.

2014-01-01

This report is part of a series of pesticide trend assessments led by the National Water-Quality Assessment Program of the U.S. Geological Survey. This assessment focuses on major rivers of various sizes throughout the United States that have large watersheds with a range of land uses, changes in pesticide use, changes in management practices, and natural influences typical of the regions being drained. Trends were assessed at 59 sites for 40 pesticides and pesticide degradates during each of three overlapping periods: 1992–2001, 1997–2006, and 2001–10. In addition to trends in concentration, trends in agricultural-use intensity (agricultural use) were also assessed at 57 of the sites for 35 parent compounds with agricultural uses during the same three periods. The SEAWAVE-Q model was used to analyze trends in concentration, and parametric survival regression for interval-censored data was used to assess trends in agricultural use. All trends are provided in downloadable electronic files. A subset of 39 sites was chosen to represent non-nested, generally independent basins for a national analysis of pesticide and agricultural-use trends for the most prevalent pesticides (15 pesticides and 2 degradation products). Graphical and numerical results are presented to provide a national overview of concentration and use trends. As another perspective on understanding pesticide concentration trends in large rivers in relation to multiple tributary watersheds, this report also presents a detailed assessment of concentration and use trends for simazine, metolachlor, atrazine, deethylatrazine, and diazinon for a set of 17 nested sites in the Mississippi River Basin (including the Ohio and Missouri River Basins), for the second and third trend periods. Pesticides strongly dominated by agricultural use—cyanazine, metolachlor, atrazine, and alachlor—had widespread agreement between concentration trends and agricultural-use trends. Pesticides with substantial use in agricultural and urban applications—simazine, tebuthiuron, Dacthal, pendimethalin, chlorpyrifos, malathion, diazinon, fipronil, carbofuran, and carbaryl—had concentration trends that were mostly explained by a combination of agricultural-use trends and concentration trends in urban streams that were evaluated in a separate companion study. The importance of the urban stream trends for explaining concentration trends in major rivers indicates the significance of nonagricultural uses of some pesticides to concentrations in major rivers despite the much smaller area of urban land use compared to agriculture. Deethylatrazine, a degradate of atrazine, was the only pesticide compound assessed that had frequent occurrences during 1997–2006 and 2001–10 of concentration trends in the opposite direction of use trends (atrazine use). The nested analysis for the Mississippi River indicates that most trends observed in the largest rivers—multiple Mississippi River sites, the Ohio River, and the Missouri River—are consistent with streamflow contributions and concentration trends observed at tributary sites. Streamflow (incorporated into the trend model and shown in the nested basin analysis), trends in agricultural use of pesticides (quantified in this report), and urban use of pesticides (represented by concentration trends in a companion study of urban streams) are all important influences on pesticide concentrations in streams and rivers. Consideration of these influences is vital to understanding trends in pesticide concentrations.

The Darkening of the Greenland Ice Sheet: Trends, Drivers and Projections (1981-2100)

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Doherty, Sarah; Fettweis, Xavier; Alexander, Patrick; Jeyaratnam, Jeyavinoth; Stroeve, Julienne

2016-01-01

The surface energy balance and meltwater production of the Greenland ice sheet (GrIS) are modulated by snow and ice albedo through the amount of absorbed solar radiation. Here we show, using space-borne multispectral data collected during the 3 decades from 1981 to 2012, that summertime surface albedo over the GrIS decreased at a statistically significant (99 %) rate of 0.02 decade(sup -1) between 1996 and 2012. Over the same period, albedo modelled by the Modele Atmospherique Regionale (MAR) also shows a decrease, though at a lower rate (approximately -0.01 decade(sup -1)) than that obtained from space-borne data. We suggest that the discrepancy between modelled and measured albedo trends can be explained by the absence in the model of processes associated with the presence of light-absorbing impurities. The negative trend in observed albedo is confined to the regions of the GrIS that undergo melting in summer, with the dry snow zone showing no trend. The period 1981-1996 also showed no statistically significant trend over the whole GrIS. Analysis of MAR outputs indicates that the observed albedo decrease is attributable to the combined effects of increased near-surface air temperatures, which enhanced melt and promoted growth in snow grain size and the expansion of bare ice areas, and to trends in light-absorbing impurities (LAI) on the snow and ice surfaces. Neither aerosol models nor in situ and remote sensing observations indicate increasing trends in LAI in the atmosphere over Greenland. Similarly, an analysis of the number of fires and BC emissions from fires points to the absence of trends for such quantities. This suggests that the apparent increase of LAI in snow and ice might be related to the exposure of a "dark band" of dirty ice and to increased consolidation of LAI at the surface with melt, not to increased aerosol deposition. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening, with albedo anomalies averaged over the whole ice sheet lower by 0.08 in 2100 than in 2000, driven solely by a warming climate. Future darkening is likely underestimated because of known underestimates in modelled melting (as seen in hindcasts) and because the model albedo scheme does not currently include the effects of LAI, which have a positive feedback on albedo decline through increased melting, grain growth, and darkening.

Automating Trend Analysis for Spacecraft Constellations

NASA Technical Reports Server (NTRS)

Davis, George; Cooter, Miranda; Updike, Clark; Carey, Everett; Mackey, Jennifer; Rykowski, Timothy; Powers, Edward I. (Technical Monitor)

2001-01-01

Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015.

PubMed

Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

2016-01-01

Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. It was a cross-sectional study. All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents.

Analysis of trends in water-quality data for water conservation area 3A, the Everglades, Florida

USGS Publications Warehouse

Mattraw, H.C.; Scheidt, D.J.; Federico, A.C.

1987-01-01

Rainfall and water quality data bases from the South Florida Water Management District were used to evaluate water quality trends at 10 locations near or in Water Conservation Area 3A in The Everglades. The Seasonal Kendall test was applied to specific conductance, orthophosphate-phosphorus, nitrate-nitrogen, total Kjeldahl nitrogen, and total nitrogen regression residuals for the period 1978-82. Residuals of orthophosphate and nitrate quadratic models, based on antecedent 7-day rainfall at inflow gate S-11B, were the only two constituent-structure pairs that showed apparent significant (p < 0.05) increases in constituent concentrations. Elimination of regression models with distinct residual patterns and data outlines resulted in 17 statistically significant station water quality combinations for trend analysis. No water quality trends were observed. The 1979 Memorandum of Agreement outlining the water quality monitoring program between the Everglades National Park and the U.S. Army Corps of Engineers stressed collection four times a year at three stations, and extensive coverage of water quality properties. Trend analysis and other rigorous statistical evaluation programs are better suited to data monitoring programs that include more frequent sampling and that are organized in a water quality data management system. Pronounced areal differences in water quality suggest that a water quality monitoring system for Shark River Slough in Everglades National Park include collection locations near the source of inflow to Water Conservation Area 3A. (Author 's abstract)

Effects of climate change on evapotranspiration over the Okavango Delta water resources

NASA Astrophysics Data System (ADS)

Moses, Oliver; Hambira, Wame L.

2018-06-01

In semi-arid developing countries, most poor people depend on contaminated surface or groundwater resources since they do not have access to safe and centrally supplied water. These water resources are threatened by several factors that include high evapotranspiration rates. In the Okavango Delta region in the north-western Botswana, communities facing insufficient centrally supplied water rely mainly on the surface water resources of the Delta. The Delta loses about 98% of its water through evapotranspiration. However, the 2% remaining water rescues the communities facing insufficient water from the main stream water supply. To understand the effects of climate change on evapotranspiration over the Okavango Delta water resources, this study analysed trends in the main climatic parameters needed as input variables in evapotranspiration models. The Mann Kendall test was used in the analysis. Trend analysis is crucial since it reveals the direction of trends in the climatic parameters, which is helpful in determining the effects of climate change on evapotranspiration. The main climatic parameters required as input variables in evapotranspiration models that were of interest in this study were wind speeds, solar radiation and relative humidity. Very little research has been conducted on these climatic parameters in the Okavango Delta region. The conducted trend analysis was more on wind speeds, which had relatively longer data records than the other two climatic parameters of interest. Generally, statistically significant increasing trends have been found, which suggests that climate change is likely to further increase evapotranspiration over the Okavango Delta water resources.

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.

Some Observations on the Current Status of Performing Finite Element Analyses

NASA Technical Reports Server (NTRS)

Raju, Ivatury S.; Knight, Norman F., Jr; Shivakumar, Kunigal N.

2015-01-01

Aerospace structures are complex high-performance structures. Advances in reliable and efficient computing and modeling tools are enabling analysts to consider complex configurations, build complex finite element models, and perform analysis rapidly. Many of the early career engineers of today are very proficient in the usage of modern computers, computing engines, complex software systems, and visualization tools. These young engineers are becoming increasingly efficient in building complex 3D models of complicated aerospace components. However, the current trends demonstrate blind acceptance of the results of the finite element analysis results. This paper is aimed at raising an awareness of this situation. Examples of the common encounters are presented. To overcome the current trends, some guidelines and suggestions for analysts, senior engineers, and educators are offered.

Trend analysis of the long-term Swiss ozone measurements

NASA Technical Reports Server (NTRS)

Staehelin, Johannes; Bader, Juerg; Gelpke, Verena

1994-01-01

Trend analyses, assuming a linear trend which started at 1970, were performed from total ozone measurements from Arosa (Switzerland, 1926-1991). Decreases in monthly mean values were statistically significant for October through April showing decreases of about 2.0-4 percent per decade. For the period 1947-91, total ozone trends were further investigated using a multiple regression model. Temperature of a mountain peak in Switzerland (Mt. Santis), the F10.7 solar flux series, the QBO series (quasi biennial oscillation), and the southern oscillation index (SOI) were included as explanatory variables. Trends in the monthly mean values were statistically significant for December through April. The same multiple regression model was applied to investigate the ozone trends at various altitudes using the ozone balloon soundings from Payerne (1967-1989) and the Umkehr measurements from Arosa (1947-1989). The results show four different vertical trend regimes: On a relative scale changes were largest in the troposphere (increase of about 10 percent per decade). On an absolute scale the largest trends were obtained in the lower stratosphere (decrease of approximately 6 per decade at an altitude of about 18 to 22 km). No significant trends were observed at approximately 30 km, whereas stratospheric ozone decreased in the upper stratosphere.

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.

Recent Trends in Adversarial Attitudes among American Newspaper Journalists: A Cohort Analysis.

ERIC Educational Resources Information Center

Zhu, Jian-Hua

A study explored the question of whether there is an adversary press, by examining the recent trends in adversarial attitudes among newspaper journalists in the United States. Using a differentiation model for delineating the nature and boundaries of American adversarial journalism, the study re-analyzed the data from two national surveys. The…

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

Desertification in the south Junggar Basin, 2000-2009: Part II. Model development and trend analysis

NASA Astrophysics Data System (ADS)

Jiang, Miao; Lin, Yi

2018-07-01

The substantial objective of desertification monitoring is to derive its development trend, which facilitates pre-making policies to handle its potential influences. Aiming at this extreme goal, previous studies have proposed a large number of remote sensing (RS) based methods to retrieve multifold indicators, as reviewed in Part I. However, most of these indicators individually capable of characterizing a single aspect of land attributes, e.g., albedo quantifying land surface reflectivity, cannot show a full picture of desertification processes; few comprehensive RS-based models have either been published. To fill this gap, this Part II was dedicated to developing a RS information model for comprehensively characterizing the desertification and deriving its trend, based on the indicators retrieved in Part I in the same case of the south Junggar Basin, China in the last decade (2000-2009). The proposed model was designed to have three dominant component modules, i.e., the vegetation-relevant sub-model, the soil-relevant sub-model, and the water-relevant sub-model, which synthesize all of the retrieved indicators to integrally reflect the processes of desertification; based on the model-output indices, the desertification trends were derived using the least absolute deviation fitting algorithm. Tests indicated that the proposed model did work and the study area showed different development tendencies for different desertification levels. Overall, this Part II established a new comprehensive RS information model for desertification risk assessment and its trend deriving, and the whole study comprising Part I and Part II advanced a relatively standard framework for RS-based desertification monitoring.

Flood Frequency Analysis Under Non-stationarity Conditions: the Case of Southern Brazilian Hydroelectric Power Plants

NASA Astrophysics Data System (ADS)

Bartiko, Daniel; Chaffe, Pedro; Bonumá, Nadia

2017-04-01

Floods may be strongly affected by climate, land-use, land-cover and water infrastructure changes. However, it is common to model this process as stationary. This approach has been questioned, especially when it involves estimate of the frequency and magnitude of extreme events for designing and maintaining hydraulic structures, as those responsible for flood control and dams safety. Brazil is the third largest producer of hydroelectricity in the world and many of the country's dams are located in the Southern Region. So, it seems appropriate to investigate the presence of non-stationarity in the affluence in these plants. In our study, we used historical flood data from the Brazilian National Grid Operator (ONS) to explore trends in annual maxima in river flow of the 38 main rivers flowing to Southern Brazilian reservoirs (records range from 43 to 84 years). In the analysis, we assumed a two-parameter log-normal distribution a linear regression model was applied in order to allow for the mean to vary with time. We computed recurrence reduction factors to characterize changes in the return period of an initially estimated 100 year-flood by a log-normal stationary model. To evaluate whether or not a particular site exhibits positive trend, we only considered data series with linear regression slope coefficients that exhibit significance levels (p<0,05). The significance level was calculated using the one-sided Student's test. The trend model residuals were analyzed using the Anderson-Darling normality test, the Durbin-Watson test for the independence and the Breusch-Pagan test for heteroscedasticity. Our results showed that 22 of the 38 data series analyzed have a significant positive trend. The trends were mainly in three large basins: Iguazu, Uruguay and Paranapanema, which suffered changes in land use and flow regularization in the last years. The calculated return period for the series that presented positive trend varied from 50 to 77 years for a 100 year-flood estimated by stationary model when considering a planning horizon equal to ten years. We conclude that attention should be given for future projects developed in this area, including the incorporation of non-stationarity analysis, search for answers to such changes and incorporation of new data to increase the reliability of the estimates.

Statistical analysis of financial returns for a multiagent order book model of asset trading

NASA Astrophysics Data System (ADS)

Preis, Tobias; Golke, Sebastian; Paul, Wolfgang; Schneider, Johannes J.

2007-07-01

We recently introduced a realistic order book model [T. Preis , Europhys. Lett. 75, 510 (2006)] which is able to generate the stylized facts of financial markets. We analyze this model in detail, explain the consequences of the use of different groups of traders, and focus on the foundation of a nontrivial Hurst exponent based on the introduction of a market trend. Our order book model supports the theoretical argument that a nontrivial Hurst exponent implies not necessarily long-term correlations. A coupling of the order placement depth to the market trend can produce fat tails, which can be described by a truncated Lévy distribution.

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.

21st Century Trends in the Potential for Ozone Depletion

NASA Astrophysics Data System (ADS)

Hurwitz, M. M.; Newman, P. A.

2009-05-01

We find robust trends in the area where Antarctic stratospheric temperatures are below the threshold for polar stratospheric cloud (PSC) formation in Goddard Earth Observing System (GEOS) chemistry-climate model (CCM) simulations of the 21st century. In late winter (September-October-November), cold area trends are consistent with the respective trends in equivalent effective stratospheric chlorine (EESC), i.e. negative cold area trends in 'realistic future' simulations where EESC decreases and the ozone layer recovers. In the early winter (April through June), regardless of EESC scenario, we find an increasing cold area trend in all simulations; multiple linear regression analysis shows that this early winter cooling trend is associated with the predicted increase in greenhouse gas concentrations in the future. We compare the seasonality of the potential for Antarctic ozone depletion in two versions of the GEOS CCM and assess the impact of the above-mentioned cold area trends on polar stratospheric chemistry.

Two Aspects of the Simplex Model: Goodness of Fit to Linear Growth Curve Structures and the Analysis of Mean Trends.

ERIC Educational Resources Information Center

Mandys, Frantisek; Dolan, Conor V.; Molenaar, Peter C. M.

1994-01-01

Studied the conditions under which the quasi-Markov simplex model fits a linear growth curve covariance structure and determined when the model is rejected. Presents a quasi-Markov simplex model with structured means and gives an example. (SLD)

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.

Recent Decline in Extratropical Lower Stratospheric Ozone Attributed to Circulation Changes

NASA Astrophysics Data System (ADS)

Wargan, Krzysztof; Orbe, Clara; Pawson, Steven; Ziemke, Jerald R.; Oman, Luke D.; Olsen, Mark A.; Coy, Lawrence; Emma Knowland, K.

2018-05-01

The 1998-2016 ozone trends in the lower stratosphere are examined using the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) and related National Aeronautics and Space Administration products. After removing biases resulting from step changes in the MERRA-2 ozone observations, a discernible negative trend of -1.67 ± 0.54 Dobson units per decade (DU/decade) is found in the 10-km layer above the tropopause between 20°N and 60°N. A weaker but statistically significant trend of -1.17 ± 0.33 DU/decade exists between 50°S and 20°S. In the Tropics, a positive trend is seen in a 5-km layer above the tropopause. Analysis of an idealized tracer in a model simulation constrained by MERRA-2 meteorological fields provides strong evidence that these trends are driven by enhanced isentropic transport between the tropical (20°S-20°N) and extratropical lower stratosphere in the past two decades. This is the first time that a reanalysis data set has been used to detect and attribute trends in lower stratospheric ozone.

Counties eliminating racial disparities in colorectal cancer mortality.

PubMed

Rust, George; Zhang, Shun; Yu, Zhongyuan; Caplan, Lee; Jain, Sanjay; Ayer, Turgay; McRoy, Luceta; Levine, Robert S

2016-06-01

Although colorectal cancer (CRC) mortality rates are declining, racial-ethnic disparities in CRC mortality nationally are widening. Herein, the authors attempted to identify county-level variations in this pattern, and to characterize counties with improving disparity trends. The authors examined 20-year trends in US county-level black-white disparities in CRC age-adjusted mortality rates during the study period between 1989 and 2010. Using a mixed linear model, counties were grouped into mutually exclusive patterns of black-white racial disparity trends in age-adjusted CRC mortality across 20 three-year rolling average data points. County-level characteristics from census data and from the Area Health Resources File were normalized and entered into a principal component analysis. Multinomial logistic regression models were used to test the relation between these factors (clusters of related contextual variables) and the disparity trend pattern group for each county. Counties were grouped into 4 disparity trend pattern groups: 1) persistent disparity (parallel black and white trend lines); 2) diverging (widening disparity); 3) sustained equality; and 4) converging (moving from disparate outcomes toward equality). The initial principal component analysis clustered the 82 independent variables into a smaller number of components, 6 of which explained 47% of the county-level variation in disparity trend patterns. County-level variation in social determinants, health care workforce, and health systems all were found to contribute to variations in cancer mortality disparity trend patterns from 1990 through 2010. Counties sustaining equality over time or moving from disparities to equality in cancer mortality suggest that disparities are not inevitable, and provide hope that more communities can achieve optimal and equitable cancer outcomes for all. Cancer 2016;122:1735-48. © 2016 American Cancer Society. © 2016 American Cancer Society.

A Decadal (2004-2014) Analysis of Global-to-Regional Tropospheric Ozone Column Trends Using GFDL-AM3 Model Simulations and OMI Observations

NASA Astrophysics Data System (ADS)

Huang, G.; Liu, X.; Lin, M.; Ziemke, J. R.; Chance, K.; Zoogman, P.; Sun, K.

2017-12-01

Tropospheric ozone is a greenhouse gas, biological irritant, and significant source of highly reactive hydroxyl radicals, which remove many hazardous trace gases from the atmosphere. The decadal trend of tropospheric ozone columns (TOCs) can be influenced by many factors including anthropogenic and natural emissions of ozone precursors, large-scale atmospheric circulation patterns, and stratosphere-to-troposphere exchange. Since 2000, anthropogenic emissions of NOx have tended to shift from North America and Europe to Asia. This rapid shift has been implicated in raising background tropospheric ozone burden. However, large meteorologically-driven ozone variability complicates the unambiguous attribution of TOC trends calculated over short periods. In this study, we examine global-to-regional TOC trends during 2004-2014 using two independent satellite retrievals from OMI SAO (Smithsonian Astrophysical Observatory) and OMI/MLS, and interpret the results with a suite of GFDL-AM3 chemistry-climate model hindcasts designed to isolate the response of ozone to anthropogenic emissions, wildfires, and meteorology. Generally, OMI SAO, OMI/MLS and GFDL-AM3 BASE simulations agree on regional hot spots of TOC trends. On the regional scale, we find strong positive TOC trends during 2004-2014 in Mid-East (0.3-0.6 DU yr-1), South Asia (0.3-0.5 DU yr-1), Southeast Asia, East Asia ( 0.1-0.6 DU yr-1) and Central Africa ( 0.6 DU yr-1). Our initial analysis indicates that meteorological variability and anthropogenic emission trends play equally important roles in the positive TOC trends in East Asia and on a global scale during 2004-2014. We are working to investigate the potential influences from lightening NOx emissions, forest fires, and the stratosphere-to-troposphere exchange.

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.

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.

Modelling changes in small area disability free life expectancy: trends in London wards between 2001 and 2011.

PubMed

Congdon, Peter

2014-12-20

Existing analyses of trends in disability free life expectancy (DFLE) are mainly at aggregate level (national or broad regional). However, major differences in DFLE, and trends in these expectancies, exist between different neighbourhoods within regions, so supporting a small area perspective. However, this raises issues regarding the stability of conventional life table estimation methods at small area scales. This paper advocates a Bayesian borrowing strength technique to model trends in mortality and disability differences across 625 small areas in London, using illness data from the 2001 and 2011 population Censuses, and deaths data for two periods centred on the Census years. From this analysis, estimates of total life expectancy and DFLE are obtained. The spatio-temporal modelling perspective allows assessment of whether significant compression or expansion of morbidity has occurred in each small area. Appropriate models involve random effects that recognise correlation and interaction effects over relevant dimensions of the observed deaths and illness data (areas, ages), as well as major spatial trends (e.g. gradients in health and mortality according to area deprivation category). Whilst borrowing strength is a primary consideration (and demonstrated by raised precision for estimated life expectancies), so also is model parsimony. Therefore, pure borrowing strength models are compared with models allowing selection of random age-area interaction effects using a spike-slab prior, and in fact borrowing strength combined with random effects selection provides better fit. Copyright © 2014 John Wiley & Sons, Ltd.

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).

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.

Statistical trend analysis and extreme distribution of significant wave height from 1958 to 1999 - an application to the Italian Seas

NASA Astrophysics Data System (ADS)

Martucci, G.; Carniel, S.; Chiggiato, J.; Sclavo, M.; Lionello, P.; Galati, M. B.

2009-09-01

The study is a statistical analysis of sea states timeseries derived using the wave model WAM forced by the ERA-40 dataset in selected areas near the Italian coasts. For the period 1 January 1958 to 31 December 1999 the analysis yields: (i) the existence of a negative trend in the annual- and winter-averaged sea state heights; (ii) the existence of a turning-point in late 70's in the annual-averaged trend of sea state heights at a site in the Northern Adriatic Sea; (iii) the overall absence of a significant trend in the annual-averaged mean durations of sea states over thresholds; (iv) the assessment of the extreme values on a time-scale of thousand years. The analysis uses two methods to obtain samples of extremes from the independent sea states: the r-largest annual maxima and the peak-over-threshold. The two methods show statistical differences in retrieving the return values and more generally in describing the significant wave field. The study shows the existence of decadal negative trends in the significant wave heights and by this it conveys useful information on the wave climatology of the Italian seas during the second half of the 20th century.

Nitrogen oxides and ozone in Portugal: trends and ozone estimation in an urban and a rural site.

PubMed

Fernández-Guisuraga, José Manuel; Castro, Amaya; Alves, Célia; Calvo, Ana; Alonso-Blanco, Elisabeth; Blanco-Alegre, Carlos; Rocha, Alfredo; Fraile, Roberto

2016-09-01

This study provides an analysis of the spatial distribution and trends of NO, NO2 and O3 concentrations in Portugal between 1995 and 2010. Furthermore, an estimation model for daily ozone concentrations was developed for an urban and a rural site. NO concentration showed a significant decreasing trend in most urban stations. A decreasing trend in NO2 is only observed in the stations with less influence from emissions of primary NO2. Several stations showed a significant upward trend in O3 as a result of the decrease in the NO/NO2 ratio. In the northern rural region, ozone showed a strong correlation with wind direction, highlighting the importance of long-range transport. In the urban site, most of the variance is explained by the NO2/NOX ratio. The results obtained by the ozone estimation model in the urban site fit 2013 observed data. In the rural site, the estimated ozone during extreme events agrees with observed concentration.

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.

Advances and trends in structural and solid mechanics; Proceedings of the Symposium, Washington, DC, October 4-7, 1982

NASA Technical Reports Server (NTRS)

Noor, A. K. (Editor); Housner, J. M.

1983-01-01

The mechanics of materials and material characterization are considered, taking into account micromechanics, the behavior of steel structures at elevated temperatures, and an anisotropic plasticity model for inelastic multiaxial cyclic deformation. Other topics explored are related to advances and trends in finite element technology, classical analytical techniques and their computer implementation, interactive computing and computational strategies for nonlinear problems, advances and trends in numerical analysis, database management systems and CAD/CAM, space structures and vehicle crashworthiness, beams, plates and fibrous composite structures, design-oriented analysis, artificial intelligence and optimization, contact problems, random waves, and lifetime prediction. Earthquake-resistant structures and other advanced structural applications are also discussed, giving attention to cumulative damage in steel structures subjected to earthquake ground motions, and a mixed domain analysis of nuclear containment structures using impulse functions.

Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia

NASA Astrophysics Data System (ADS)

Chang, K. L.; Petropavlovskikh, I. V.; Cooper, O. R.; Schultz, M.; Wang, T.

2017-12-01

Surface ozone is a greenhouse gas and pollutant detrimental to human health and crop and ecosystem productivity. The Tropospheric Ozone Assessment Report (TOAR) is designed to provide the research community with an up-to-date observation-based overview of tropospheric ozone's global distribution and trends. The TOAR Surface Ozone Database contains ozone metrics at thousands of monitoring sites around the world, densely clustered across mid-latitude North America, western Europe and East Asia. Calculating regional ozone trends across these locations is challenging due to the uneven spacing of the monitoring sites across urban and rural areas. To meet this challenge we conducted a spatial and temporal trend analysis of several TOAR ozone metrics across these three regions for summertime (April-September) 2000-2014, using the generalized additive mixed model (GAMM). Our analysis indicates that East Asia has the greatest human and plant exposure to ozone pollution among investigating regions, with increasing ozone levels through 2014. The results also show that ozone mixing ratios continue to decline significantly over eastern North America and Europe, however, there is less evidence for decreases of daytime average ozone at urban sites. The present-day spatial coverage of ozone monitors in East Asia (South Korea and Japan) and eastern North America is adequate for estimating regional trends by simply taking the average of the individual trends at each site. However the European network is more sparsely populated across its northern and eastern regions and therefore a simple average of the individual trends at each site does not yield an accurate regional trend. This analysis demonstrates that the GAMM technique can be used to assess the regional representativeness of existing monitoring networks, indicating those networks for which a regional trend can be obtained by simply averaging the trends of all individual sites and those networks that require a more sophisticated statistical approach.

Significant calendar period deviations in testicular germ cell tumors indicate that postnatal exposures are etiologically relevant.

PubMed

Speaks, Crystal; McGlynn, Katherine A; Cook, Michael B

2012-10-01

The current working model of type II testicular germ cell tumor (TGCT) pathogenesis states that carcinoma in situ arises during embryogenesis, is a necessary precursor, and always progresses to cancer. An implicit condition of this model is that only in utero exposures affect the development of TGCT in later life. In an age-period-cohort analysis, this working model contends an absence of calendar period deviations. We tested this contention using data from the SEER registries of the United States. We assessed age-period-cohort models of TGCTs, seminomas, and nonseminomas for the period 1973-2008. Analyses were restricted to whites diagnosed at ages 15-74 years. We tested whether calendar period deviations were significant in TGCT incidence trends adjusted for age deviations and cohort effects. This analysis included 32,250 TGCTs (18,475 seminomas and 13,775 nonseminomas). Seminoma incidence trends have increased with an average annual percentage change in log-linear rates (net drift) of 1.25 %, relative to just 0.14 % for nonseminoma. In more recent time periods, TGCT incidence trends have plateaued and then undergone a slight decrease. Calendar period deviations were highly statistically significant in models of TGCT (p = 1.24(-9)) and seminoma (p = 3.99(-14)), after adjustment for age deviations and cohort effects; results for nonseminoma (p = 0.02) indicated that the effects of calendar period were much more muted. Calendar period deviations play a significant role in incidence trends of TGCT, which indicates that postnatal exposures are etiologically relevant.

Recent trends in the frequency and duration of global floods

NASA Astrophysics Data System (ADS)

Najibi, Nasser; Devineni, Naresh

2018-06-01

Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985-2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1-7, moderate: 8-20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann-Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. Results show that flood frequency and the tails of the flood duration (long duration) have increased at both the global and the latitudinal scales. In the tropics, floods have increased 4-fold since the 2000s. This increase is 2.5-fold in the north midlatitudes. However, much of the trend in frequency and duration of the floods can be placed within the long-term climate variability context since the Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were the main atmospheric teleconnections explaining this trend. There is no monotonic trend in the frequency of short-duration floods across all the global and latitudinal scales. There is a significant increasing trend in the annual median of flood durations globally and each latitudinal belt, and this trend is not related to these teleconnections. While the DFO data come with a certain level of epistemic uncertainty due to imprecision in the estimation of floods, overall, the analysis provides insights for understanding the frequency and persistence in hydrologic extremes and how they relate to changes in the climate, organization of global and local dynamical systems, and country-scale socioeconomic factors.

Future risk assessment by estimating historical heat wave trends with projected heat accumulation using SimCLIM climate model in Pakistan

NASA Astrophysics Data System (ADS)

Nasim, Wajid; Amin, Asad; Fahad, Shah; Awais, Muhammad; Khan, Naeem; Mubeen, Muhammad; Wahid, Abdul; Turan, Veysel; Rehman, Muhammad Habibur; Ihsan, Muhammad Zahid; Ahmad, Shakeel; Hussain, Sajjad; Mian, Ishaq Ahmad; Khan, Bushra; Jamal, Yousaf

2018-06-01

Climate change has adverse effects at global, regional and local level. Heat wave events have serious contribution for global warming and natural hazards in Pakistan. Historical (1997-2015) heat wave were analyzed over different provinces (Punjab, Sindh and Baluchistan) of Pakistan to identify the maximum temperature trend. Heat accumulation in Pakistan were simulated by the General Circulation Model (GCM) combined with 3 GHG (Green House Gases) Representative Concentration Pathways (RCPs) (RCP-4.5, 6.0, and 8.5) by using SimCLIM model (statistical downscaling model for future trend projections). Heat accumulation was projected for year 2030, 2060, and 2090 for seasonal and annual analysis in Pakistan. Heat accumulation were projected to increase by the baseline year (1995) was represented in percentage change. Projection shows that Sindh and southern Punjab was mostly affected by heat accumulation. This study identified the rising trend of heat wave over the period (1997-2015) for Punjab, Sindh and Baluchistan (provinces of Pakistan), which identified that most of the meteorological stations in Punjab and Sindh are highly prone to heat waves. According to model projection; future trend of annual heat accumulation, in 2030 was increased 17%, 26%, and 32% but for 2060 the trends were reported by 54%, 49%, and 86% for 2090 showed highest upto 62%, 75%, and 140% for RCP-4.5, RCP-6.0, and RCP-8.5, respectively. While seasonal trends of heat accumulation were projected to maximum values for monsoon and followed by pre-monsoon and post monsoon. Heat accumulation in monsoon may affect the agricultural activities in the region under study.

Trends in Educational Inequality in Different Eras (1940-2010)--A Re-Examination of Opportunity Inequalities in Urban-Rural Education

ERIC Educational Resources Information Center

Chunling, Li

2015-01-01

Based on national sampling survey data from 2006, 2008, and 2011, the author uses the Mare educational transition model to systematically examine changing trends in inequalities in urban-rural educational opportunities at all educational stages from 1940 to 2010. Through a comparative analysis of five birth year groups, inequalities in urban-rural…

Characterizing trends in fruit and vegetable intake in the US by self-report and by supply-and-disappearance data: 2001-2014

USDA-ARS?s Scientific Manuscript database

Objective: To examine the comparability of fruit and vegetable (F&V) intake data in the US from 2001-2014 between data acquired from two national data collection programs. Design: Cross-sectional analysis. Linear regression models estimated trends in daily per-capita intake of total F&V. Pooled di...

An Exploratory Analysis of the Navy Personnel Support Delivery Model

DTIC Science & Technology

2017-09-01

technology-competent generation. Our efforts are focused on providing a quantitative effort to understanding past trends in Personnel Support Detachment (PSD... quantitative effort to understanding past trends in Personnel Support Detachment (PSD) and Customer Service Desk (CSD) transactions that may aid manpower...56 xiv THIS PAGE INTENTIONALLY LEFT BLANK xv LIST OF TABLES Table 1. Final Dataset Column Names and Descriptions

Multi-scale modeling of relationships between forest health and climatic factors

Treesearch

Michael K. Crosby; Zhaofei Fan; Xingang Fan; Martin A. Spetich; Theodor D. Leininger

2015-01-01

Forest health and mortality trends are impacted by changes in climate. These trends can vary by species, plot location, forest type, and/or ecoregion. To assess the variation among these groups, Forest Inventory and Analysis (FIA) data were obtained for 10 states in the southeastern United States and combined with downscaled climate data from the Weather Research and...

Signal detection in global mean temperatures after "Paris": an uncertainty and sensitivity analysis

NASA Astrophysics Data System (ADS)

Visser, Hans; Dangendorf, Sönke; van Vuuren, Detlef P.; Bregman, Bram; Petersen, Arthur C.

2018-02-01

In December 2015, 195 countries agreed in Paris to hold the increase in global mean surface temperature (GMST) well below 2.0 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C. Since large financial flows will be needed to keep GMSTs below these targets, it is important to know how GMST has progressed since pre-industrial times. However, the Paris Agreement is not conclusive as regards methods to calculate it. Should trend progression be deduced from GCM simulations or from instrumental records by (statistical) trend methods? Which simulations or GMST datasets should be chosen, and which trend models? What is pre-industrial and, finally, are the Paris targets formulated for total warming, originating from both natural and anthropogenic forcing, or do they refer to anthropogenic warming only? To find answers to these questions we performed an uncertainty and sensitivity analysis where datasets and model choices have been varied. For all cases we evaluated trend progression along with uncertainty information. To do so, we analysed four trend approaches and applied these to the five leading observational GMST products. We find GMST progression to be largely independent of various trend model approaches. However, GMST progression is significantly influenced by the choice of GMST datasets. Uncertainties due to natural variability are largest in size. As a parallel path, we calculated GMST progression from an ensemble of 42 GCM simulations. Mean progression derived from GCM-based GMSTs appears to lie in the range of trend-dataset combinations. A difference between both approaches appears to be the width of uncertainty bands: GCM simulations show a much wider spread. Finally, we discuss various choices for pre-industrial baselines and the role of warming definitions. Based on these findings we propose an estimate for signal progression in GMSTs since pre-industrial.

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

MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)

EPA Science Inventory

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...

Consideration of some factors affecting low-frequency fuselage noise transmission for propeller aircraft

NASA Technical Reports Server (NTRS)

Mixson, J. S.; Roussos, L. A.

1986-01-01

Possible reasons for disagreement between measured and predicted trends of sidewall noise transmission at low frequency are investigated using simplified analysis methods. An analytical model combining incident plane acoustic waves with an infinite flat panel is used to study the effects of sound incidence angle, plate structural properties, frequency, absorption, and the difference between noise reduction and transmission loss. Analysis shows that these factors have significant effects on noise transmission but they do not account for the differences between measured and predicted trends at low frequencies. An analytical model combining an infinite flat plate with a normally incident acoustic wave having exponentially decaying magnitude along one coordinate is used to study the effect of a localized source distribution such as is associated with propeller noise. Results show that the localization brings the predicted low-frequency trend of noise transmission into better agreement with measured propeller results. This effect is independent of low-frequency stiffness effects that have been previously reported to be associated with boundary conditions.

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.

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.

Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015

PubMed Central

Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

2016-01-01

Background: Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Objective: Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. Settings and Design: It was a cross-sectional study. Materials and Methods: All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. Results: From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. Conclusion: From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents. PMID:27308255

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.

Age-period-cohort analysis of the suicide rate in Korea.

PubMed

Park, Chiho; Jee, Yon Ho; Jung, Keum Ji

2016-04-01

The suicide rate has been increasing in Korea, and the country now has the highest rank in the world. This study aimed to present the long-term trends in Korea's suicide rate using Joinpoint analysis and age-period-cohort (APC) modeling. The population and the number of suicides for each five-year age group were obtained from the National Statistical Office for the period 1984-2013 for Koreans aged 10 years and older. We determined the changes in the trends in age-standardized mortality rates using Joinpoint. APC modeling was performed to describe the trends in the suicide rate using the intrinsic estimator method. The age-standardized suicide rate in men rapidly increased from 1989 to 2004, and slightly increased thereafter, whereas the suicide rate in women increased from 1989 to 2009 and then decreased thereafter. Within the same period, the suicide rate was higher among the older age groups than in the younger groups. Within the same birth cohort, the suicide rate of the older groups was also higher than that in the younger groups. Within the same age group, the suicide rate of the younger cohorts was higher than it was in the older cohorts. In the APC modeling, old age, recent period, and having been born before 1924 were associated with higher suicide rates. The accuracy and completeness of the suicide rate data may lead to bias. This study showed an increasing trend in the suicide rates for men and women after 1989. These trends may be mainly attributed to cohort effects. Copyright © 2016 Elsevier B.V. All rights reserved.

Human growth and body weight dynamics: an integrative systems model.

PubMed

Rahmandad, Hazhir

2014-01-01

Quantifying human weight and height dynamics due to growth, aging, and energy balance can inform clinical practice and policy analysis. This paper presents the first mechanism-based model spanning full individual life and capturing changes in body weight, composition and height. Integrating previous empirical and modeling findings and validated against several additional empirical studies, the model replicates key trends in human growth including A) Changes in energy requirements from birth to old ages. B) Short and long-term dynamics of body weight and composition. C) Stunted growth with chronic malnutrition and potential for catch up growth. From obesity policy analysis to treating malnutrition and tracking growth trajectories, the model can address diverse policy questions. For example I find that even without further rise in obesity, the gap between healthy and actual Body Mass Indexes (BMIs) has embedded, for different population groups, a surplus of 14%-24% in energy intake which will be a source of significant inertia in obesity trends. In another analysis, energy deficit percentage needed to reduce BMI by one unit is found to be relatively constant across ages. Accompanying documented and freely available simulation model facilitates diverse applications customized to different sub-populations.

Human Growth and Body Weight Dynamics: An Integrative Systems Model

PubMed Central

Rahmandad, Hazhir

2014-01-01

Quantifying human weight and height dynamics due to growth, aging, and energy balance can inform clinical practice and policy analysis. This paper presents the first mechanism-based model spanning full individual life and capturing changes in body weight, composition and height. Integrating previous empirical and modeling findings and validated against several additional empirical studies, the model replicates key trends in human growth including A) Changes in energy requirements from birth to old ages. B) Short and long-term dynamics of body weight and composition. C) Stunted growth with chronic malnutrition and potential for catch up growth. From obesity policy analysis to treating malnutrition and tracking growth trajectories, the model can address diverse policy questions. For example I find that even without further rise in obesity, the gap between healthy and actual Body Mass Indexes (BMIs) has embedded, for different population groups, a surplus of 14%–24% in energy intake which will be a source of significant inertia in obesity trends. In another analysis, energy deficit percentage needed to reduce BMI by one unit is found to be relatively constant across ages. Accompanying documented and freely available simulation model facilitates diverse applications customized to different sub-populations. PMID:25479101

Bioeconomic and market models

Treesearch

Richard Haynes; Darius Adams; Peter Ince; John Mills; Ralph Alig

2006-01-01

The United States has a century of experience with the development of models that describe markets for forest products and trends in resource conditions. In the last four decades, increasing rigor in policy debates has stimulated the development of models to support policy analysis. Increasingly, research has evolved (often relying on computer-based models) to increase...

Analysis of childhood leukemia mortality trends in Brazil, from 1980 to 2010.

PubMed

Silva, Franciane F; Zandonade, Eliana; Zouain-Figueiredo, Glaucia P

2014-01-01

Leukemias comprise the most common group of cancers in children and adolescents. Studies conducted in other countries and Brazil have observed a decrease in their mortality.This study aimed to evaluate the trend of mortality from leukemia in children under 19 years of age in Brazil, from 1980 to 2010. This was an ecological study, using retrospective time series data from the Mortality Information System, from 1980 to 2010. Calculations of mortality rates were performed, including gross, gender-specific, and age-based. For trend analysis, linear and semi-log regression models were used. The significance level was 5%. Mortality rates for lymphoid and myeloid leukemias presented a growth trend, with the exception of lymphoid leukemia among children under 4 years of age (percentage decrease: 1.21% annually), while in the sub-group "Other types of leukemia", a downward trend was observed. Overall, mortality from leukemia tended to increase for boys and girls, especially in the age groups 10-14 years (annual percentage increase of 1.23% for males and 1.28% for females) and 15-19 years (annual percentage increase of 1.40% for males and 1.62% for females). The results for leukemia generally corroborate the results of other similar studies. A detailed analysis by subgroup of leukemia, age, and gender revealed no trends shown in other studies, thus indicating special requirements for each variable in the analysis. Copyright © 2014 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Trend in Air Quality of Kathmandu Valley: A Satellite, Observation and Modelling Perspective

NASA Astrophysics Data System (ADS)

Mahapatra, P. S.; Praveen, P. S.; Adhikary, B.; Panday, A. K.; Putero, D.; Bonasoni, P.

2016-12-01

Kathmandu (floor area of 340 km2) in Nepal is considered to be a `hot spot' of urban air pollution in South Asia. Its structure as a flat basin surrounded by tall mountains provides a unique case study for analyzing pollution trapped by topography. Only a very small number of cities with similar features have been studied extensively including Mexico and Santiago-de-Chile. This study presents the trend in satellite derived Aerosol Optical Depth (AOD) from MODIS AQUA and TERRA (3x3km, Level 2) over Kathmandu from 2000 to 2015. Trend analysis of AOD shows 35% increase during the study period. Determination of the background pollution would reveal the contribution of only Kathmandu Valley for the observation period. For this, AOD at 1340m altitude outside Kathmandu, but nearby areas were considered as background. This analysis was further supported by investigating AOD at different heights around Kathmandu as well as determining AOD from CALIPSO vertical profiles. These analysis suggest that background AOD contributed 30% in winter and 60% in summer to Kathmandu Valley's observed AOD. Thereafter the background AOD was subtracted from total Kathmandu AOD to determine contribution of only Kathmandu Valley's AOD. Trend analysis of only Kathmandu Valley AOD (subtracting background AOD) suggested an increase of 50% during the study period. Further analysis of Kathmandu's visibility and AOD suggest profound role of background AOD on decreasing visibility. In-situ Black Carbon (BC) mass concentration measurements (BC being used as a proxy for surface observations) at two sites within Kathmandu valley have been analyzed. Kathmandu valley lacks long term trends of ambient air quality measurement data. Therefore, surface observations would be coupled with satellite measurements for understanding the urban air pollution scenario. Modelling studies to estimate the contribution of background pollution to Kathmandu's own pollution as well as the weekend effect on air quality will be further discussed in detail.

Helicopter aeroelastic stability and response - Current topics and future trends

NASA Technical Reports Server (NTRS)

Friedmann, Peretz P.

1990-01-01

This paper presents several current topics in rotary wing aeroelasticity and concludes by attempting to anticipate future trends and developments. These topics are: (1) the role of geometric nonlinearities; (2) structural modeling, and aeroelastic analysis of composite rotor blades; (3) aeroelastic stability and response in forward flight; (4) modeling of coupled rotor/fuselage aeromechanical problems and their active control; and (5) the coupled rotor-fuselage vibration problem and its alleviation by higher harmonic control. Selected results illustrating the fundamental aspects of these topics are presented. Future developments are briefly discussed.

Attribution of recent ozone changes in the Southern Hemisphere mid-latitudes using statistical analysis and chemistry-climate model simulations

NASA Astrophysics Data System (ADS)

Zeng, Guang; Morgenstern, Olaf; Shiona, Hisako; Thomas, Alan J.; Querel, Richard R.; Nichol, Sylvia E.

2017-09-01

Ozone (O3) trends and variability from a 28-year (1987-2014) ozonesonde record at Lauder, New Zealand, have been analysed and interpreted using a statistical model and a global chemistry-climate model (CCM). Lauder is a clean rural measurement site often representative of the Southern Hemisphere (SH) mid-latitude background atmosphere. O3 trends over this period at this location are characterised by a significant positive trend below 6 km, a significant negative trend in the tropopause region and the lower stratosphere between 9 and 15 km, and no significant trend in the free troposphere (6-9 km) and the stratosphere above 15 km. We find that significant positive trends in lower tropospheric ozone are correlated with increasing temperature and decreasing relative humidity at the surface over this period, whereas significant negative trends in the upper troposphere and the lower stratosphere appear to be strongly linked to an upward trend of the tropopause height. Relative humidity and the tropopause height also dominate O3 variability at Lauder in the lower troposphere and the tropopause region, respectively. We perform an attribution of these trends to anthropogenic forcings including O3 precursors, greenhouse gases (GHGs), and O3-depleting substances (ODSs), using CCM simulations. Results indicate that changes in anthropogenic O3 precursors contribute significantly to stratospheric O3 reduction, changes in ODSs contribute significantly to tropospheric O3 reduction, and increased GHGs contribute significantly to stratospheric O3 increases at Lauder. Methane (CH4) likely contributes positively to O3 trends in both the troposphere and the stratosphere, but the contribution is not significant at the 95 % confidence level over this period. An extended analysis of CCM results covering 1960-2010 (i.e. starting well before the observations) reveals significant contributions from all forcings to O3 trends at Lauder - i.e. increases in GHGs and the increase in CH4 alone all contribute significantly to O3 increases, net increases in ODSs lead to O3 reduction, and increases in non-methane O3 precursors cause O3 increases in the troposphere and reductions in the stratosphere. This study suggests that a long-term ozonesonde record obtained at a SH mid-latitude background site (corroborated by a surface O3 record at a nearby SH mid-latitude site, Baring Head, which also shows a significant positive trend) is a useful indicator for detecting atmospheric composition and climate change associated with human activities.

Evaluation of methodology for detecting/predicting migration of forest species

Treesearch

Dale S. Solomon; William B. Leak

1996-01-01

Available methods for analyzing migration of forest species are evaluated, including simulation models, remeasured plots, resurveys, pollen/vegetation analysis, and age/distance trends. Simulation models have provided some of the most drastic estimates of species changes due to predicted changes in global climate. However, these models require additional testing...

Fuel load modeling from mensuration attributes in temperate forests in northern Mexico

Treesearch

Maricela Morales-Soto; Marín Pompa-Garcia

2013-01-01

The study of fuels is an important factor in defining the vulnerability of ecosystems to forest fires. The aim of this study was to model a dead fuel load based on forest mensuration attributes from forest management inventories. A scatter plot analysis was performed and, from explanatory trends between the variables considered, correlation analysis was carried out...

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.

Hierarchical models and the analysis of bird survey information

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.

2003-01-01

Management of birds often requires analysis of collections of estimates. We describe a hierarchical modeling approach to the analysis of these data, in which parameters associated with the individual species estimates are treated as random variables, and probability statements are made about the species parameters conditioned on the data. A Markov-Chain Monte Carlo (MCMC) procedure is used to fit the hierarchical model. This approach is computer intensive, and is based upon simulation. MCMC allows for estimation both of parameters and of derived statistics. To illustrate the application of this method, we use the case in which we are interested in attributes of a collection of estimates of population change. Using data for 28 species of grassland-breeding birds from the North American Breeding Bird Survey, we estimate the number of species with increasing populations, provide precision-adjusted rankings of species trends, and describe a measure of population stability as the probability that the trend for a species is within a certain interval. Hierarchical models can be applied to a variety of bird survey applications, and we are investigating their use in estimation of population change from survey data.

20 Years of Total and Tropical Ozone Time Series Based on European Satellite Observations

NASA Astrophysics Data System (ADS)

Loyola, D. G.; Heue, K. P.; Coldewey-Egbers, M.

2016-12-01

Ozone is an important trace gas in the atmosphere, while the stratospheric ozone layer protects the earth surface from the incident UV radiation, the tropospheric ozone acts as green house gas and causes health damages as well as crop loss. The total ozone column is dominated by the stratospheric column, the tropospheric columns only contributes about 10% to the total column.The ozone column data from the European satellite instruments GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B are available within the ESA Climate Change Initiative project with a high degree of inter-sensor consistency. The tropospheric ozone columns are based on the convective cloud differential algorithm. The datasets encompass a period of more than 20 years between 1995 and 2015, for the trend analysis the data sets were harmonized relative to one of the instruments. For the tropics we found an increase in the tropospheric ozone column of 0.75 ± 0.12 DU decade^{-1} with local variations between 1.8 and -0.8. The largest trends were observed over southern Africa and the Atlantic Ocean. A seasonal trend analysis led to the assumption that the increase is caused by additional forest fires.The trend for the total column was not that certain, based on model predicted trend data and the measurement uncertainty we estimated that another 10 to 15 years of observations will be required to observe a statistical significant trend. In the mid latitudes the trends are currently hidden in the large variability and for the tropics the modelled trends are low. Also the possibility of diverging trends at different altitudes must be considered; an increase in the tropospheric ozone might be accompanied by decreasing stratospheric ozone.The European satellite data record will be extended over the next two decades with the atmospheric satellite missions Sentinel 5 Precursor (launch end of 2016), Sentinel 4 and Sentinel 5.

Seasonal Responses of Terrestrial Ecosystem Water-use Efficiency to Climate Change

NASA Astrophysics Data System (ADS)

Huang, M.; Piao, S.; Zeng, Z.; Peng, S.; Ciais, P.; Cheng, L.; Mao, J.; Poulter, B.; Shi, X.; Yao, Y.; Yang, H.; Wang, Y.

2016-12-01

Ecosystem water-use efficiency (EWUE) is an indicator of carbon-water interactions and is defined as the ratio of carbon assimilation (GPP) to evapotranspiration (ET). Previous research suggests an increasing long-term trend in annual EWUE over many regions, and is largely attributed to the physiological effects of rising CO2. The seasonal trends in EWUE, however, have not yet been analyzed. In this study, we investigate seasonal EWUE trends and responses to various drivers during 1982-2008. The seasonal cycle for two variants of EWUE, water-use efficiency (WUE, GPP/ET) and transpiration-based WUE (WUEt, the ratio of GPP and transpiration), is analyzed from 0.5° gridded fields from four process-based models and satellite-based products, as well as a network of 63 local flux tower observations. WUE derived from flux tower observations shows moderate seasonal variation for most latitude bands, which is in agreement with satellite-based products. In contrast, the seasonal EWUE trends are not well captured by the same satellite-based products. Trend analysis, based on process-model factorial simulations separating effects of climate, CO2 and nitrogen deposition (NDEP), further suggests that the seasonal EWUE trends are mainly associated with seasonal trends of climate, whereas CO2 and NDEP do not show obvious seasonal difference in EWUE trends. About 66% grid cells show positive annual WUE trends, mainly over mid- and high northern latitudes. In these regions, spring climate change has amplified the effect of CO2 in increasing WUE by more than 0.005 gC m-2 mm-1 yr-1 for 41% pixels. Multiple regression analysis further shows that the increase in springtime WUE in the northern hemisphere is the result of GPP increasing faster than ET because of the higher temperature sensitivity of GPP relative to ET. The partitioning of annual EWUE to seasonal components provides new insight into the relative sensitivities of GPP and ET to climate, CO2 and NDEP.

Investigating the usefulness of a cluster-based trend analysis to detect visual field progression in patients with open-angle glaucoma.

PubMed

Aoki, Shuichiro; Murata, Hiroshi; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Hirasawa, Kazunori; Shoji, Nobuyuki; Asaoka, Ryo

2017-12-01

To investigate the usefulness of the Octopus (Haag-Streit) EyeSuite's cluster trend analysis in glaucoma. Ten visual fields (VFs) with the Humphrey Field Analyzer (Carl Zeiss Meditec), spanning 7.7 years on average were obtained from 728 eyes of 475 primary open angle glaucoma patients. Mean total deviation (mTD) trend analysis and EyeSuite's cluster trend analysis were performed on various series of VFs (from 1st to 10th: VF1-10 to 6th to 10th: VF6-10). The results of the cluster-based trend analysis, based on different lengths of VF series, were compared against mTD trend analysis. Cluster-based trend analysis and mTD trend analysis results were significantly associated in all clusters and with all lengths of VF series. Between 21.2% and 45.9% (depending on VF series length and location) of clusters were deemed to progress when the mTD trend analysis suggested no progression. On the other hand, 4.8% of eyes were observed to progress using the mTD trend analysis when cluster trend analysis suggested no progression in any two (or more) clusters. Whole field trend analysis can miss local VF progression. Cluster trend analysis appears as robust as mTD trend analysis and useful to assess both sectorial and whole field progression. Cluster-based trend analyses, in particular the definition of two or more progressing cluster, may help clinicians to detect glaucomatous progression in a timelier manner than using a whole field trend analysis, without significantly compromising specificity. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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.

The trends in total energy, macronutrients and sodium intake among Japanese: findings from the 1995-2016 National Health and Nutrition Survey.

PubMed

Saito, Aki; Imai, Shino; Htun, Nay Chi; Okada, Emiko; Yoshita, Katsushi; Yoshiike, Nobuo; Takimoto, Hidemi

2018-06-04

Monitoring nutritional status of the population is essential in the development and evaluation of national or local health policies. In this study, we aimed to demonstrate analysis on the trends in dietary intake of energy and macronutrients, as well as Na, in Japanese population using the data of series of cross-sectional national surveys - the National Nutrition Survey (NNS) and the National Health Nutrition Survey (NHNS) - during the period from 1995 to 2016. The NNS and NHNS participants aged 20-79 years were included in the analysis. Dietary intake was estimated using 1-d household-based dietary record. The trend in total energy intake, energy intake from macronutrients (fat and protein), Na intake and energy-adjusted Na intake were analysed using regression models adjusted to 2010 age distribution and anthropometry status. A total of 94 270 men and 107 890 women were included the analysis. Total energy intake showed a decreasing trend in both men and women. Similarly, energy intake from protein decreased, but energy intake (%) from fat increased in both sexes. Energy-adjusted Na intake showed a decreasing trend in both men and women. This study identified the decrease in total energy intake and energy intake from protein, whereas there were inverse trends in energy intake from fat among Japanese adults. Continued monitoring of trends in dietary intake will be needed, and there should be efforts to increase the accuracy of current survey procedures.

Air Emissions Inventories

EPA Pesticide Factsheets

This site provides access to emissions data, regulations and guidance, electronic system access, resources and tools to support trends analysis, regional, and local scale air quality modeling, regulatory impact assessments.

Techniques for analyses of trends in GRUAN data

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Kremser, S.

2015-04-01

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterized and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterized uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

Techniques for analyses of trends in GRUAN data

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Kremser, S.

2014-12-01

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterised and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterised uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

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.

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.

Tropospheric ozone (TOR) trend over three major inland Indian cities: Delhi, Hyderabad and Bangalore

NASA Astrophysics Data System (ADS)

Kulkarni, Pavan S.; Ghude, Sachin D.; Bortoli, D.

2010-10-01

An analysis of tropospheric column ozone using the NASA Langley TOR data during 1979-2005 has been done to investigate the trend over major Indian cities Delhi, Hyderabad and Bangalore. India was under social democratic-based policies before 1990s. Economic Liberalization began in nineties which lead to a significant growth in industrial, energy and transport sectors in major cities. Our analysis shows that there is a systematic increase in the number of months with higher tropospheric ozone values after 1990. A comparison of TOR climatology before and after 1990 over these cities shows evidence of increase in the tropospheric ozone after 1990. Trend obtained from the model shows significant change during monsoon over Delhi and during pre-monsoon and post-monsoon over Hyderabad and Bangalore. The present analysis using TOR technique demonstrates the TOR potential to detect changes in tropospheric ozone over large cities which are impacted by large anthropogenic pollution.

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.

How a future energy world could look?

NASA Astrophysics Data System (ADS)

Ewert, M.

2012-10-01

The future energy system will change significantly within the next years as a result of the following Mega Trends: de-carbonization, urbanization, fast technology development, individualization, glocalization (globalization and localization) and changing demographics. Increasing fluctuating renewable production will change the role of non-renewable generation. Distributed energy from renewables and micro generation will change the direction of the energy flow in the electricity grids. Production will not follow demand but demand has to follow production. This future system is enabled by the fast technical development of information and communication technologies which will be present in the entire system. In this paper the results of a comprehensive analysis with different scenarios is summarized. Tools were used like the analysis of policy trends in the European countries, modelling of the European power grid, modelling of the European power markets and the analysis of technology developments with cost reduction potentials. With these tools the interaction of the main actors in the energy markets like conventional generation and renewable generation, grid transport, electricity storage including new storage options from E-Mobility, Power to Gas, Compressed Air Energy storage and demand side management were considered. The potential application of technologies and investments in new energy technologies were analyzed within existing frameworks and markets as well as new business models in new markets with different frameworks. In the paper the over all trend of this analysis is presented by describing a potential future energy world. This world represents only one of numerous options with comparable characteristics.

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

Long-Term Vegetation Trends Detected In Northern Canada Using Landsat Image Stacks

NASA Astrophysics Data System (ADS)

Fraser, R.; Olthof, I.; Carrière, M.; Deschamps, A.; Pouliot, D.

2011-12-01

Evidence of recent productivity increases in arctic vegetation comes from a variety of sources. At local scales, long-term plot measurements in North America are beginning to record increases in vascular plant cover and biomass. At landscape scales, expansion and densification of shrubs has been observed using repeat oblique photographs. Finally, continental-scale increases in vegetation "greenness" have been documented based on analysis of coarse resolution (≥ 1 km) NOAA-AVHRR satellite imagery. In this study we investigated intermediate, regional-level changes occurring in tundra vegetation since 1984 using the Landsat TM and ETM+ satellite image archive. Four study areas averaging 13,619 km2 were located over widely distributed national parks in northern Canada (Ivvavik, Sirmilik, Torngat Mountains, and Wapusk). Time-series image stacks of 16-41 growing-season Landsat scenes from overlapping WRS-2 frames were acquired spanning periods of 17-25 years. Each pixel's unique temporal database of clear-sky values was then analyzed for trends in four indices (NDVI, Tasseled Cap Brightness, Greenness and Wetness) using robust linear regression. The trends were further related to changes in the fractional cover of functional vegetation types using regression tree models trained with plot data and high resolution (≤ 10 m) satellite imagery. We found all four study areas to have a larger proportion of significant (p<0.05) positive greenness trends (range 6.1-25.5%) by comparison to negative trends (range 0.3-4.1%). For the three study areas where regression tree models could be derived, consistent trends of increasing shrub or vascular fractional cover and decreasing bare cover were predicted. The Landsat-based observations were associated with warming trends in each park over the analysis periods. Many of the major changes observed could be corroborated using published studies or field observations.

Relation between increased numbers of safe playing areas and decreased vehicle related child mortality rates in Japan from 1970 to 1985: a trend analysis

PubMed Central

Nakahara, S.; Nakamura, Y.; Ichikawa, M.; Wakai, S.

2004-01-01

Objectives: To examine vehicle related mortality trends of children in Japan; and to investigate how environmental modifications such as the installation of public parks and pavements are associated with these trends. Design: Poisson regression was used for trend analysis, and multiple regression modelling was used to investigate the associations between trends in environmental modifications and trends in motor vehicle related child mortality rates. Setting: Mortality data of Japan from 1970 to 1994, defined as E-code 810–23 from 1970 to 1978 and E810–25 from 1979 to 1994, were obtained from vital statistics. Multiple regression modelling was confined to the 1970–1985 data. Data concerning public parks and other facilities were obtained from the Ministry of Land, Infrastructure, and Transport. Subjects: Children aged 0–14 years old were examined in this study and divided into two groups: 0–4 and 5–14 years. Main results: An increased number of public parks was associated with decreased vehicle related mortality rates among children aged 0–4 years, but not among children aged 5–14. In contrast, there was no association between trends in pavements and mortality rates. Conclusions: An increased number of public parks might reduce vehicle related preschooler deaths, in particular those involving pedestrians. Safe play areas in residential areas might reduce the risk of vehicle related child death by lessening the journey both to and from such areas as well as reducing the number of children playing on the street. However, such measures might not be effective in reducing the vehicle related mortalities of school age children who have an expanded range of activities and walk longer distances. PMID:15547055

NASA trend analysis procedures

NASA Technical Reports Server (NTRS)

1993-01-01

This publication is primarily intended for use by NASA personnel engaged in managing or implementing trend analysis programs. 'Trend analysis' refers to the observation of current activity in the context of the past in order to infer the expected level of future activity. NASA trend analysis was divided into 5 categories: problem, performance, supportability, programmatic, and reliability. Problem trend analysis uncovers multiple occurrences of historical hardware or software problems or failures in order to focus future corrective action. Performance trend analysis observes changing levels of real-time or historical flight vehicle performance parameters such as temperatures, pressures, and flow rates as compared to specification or 'safe' limits. Supportability trend analysis assesses the adequacy of the spaceflight logistics system; example indicators are repair-turn-around time and parts stockage levels. Programmatic trend analysis uses quantitative indicators to evaluate the 'health' of NASA programs of all types. Finally, reliability trend analysis attempts to evaluate the growth of system reliability based on a decreasing rate of occurrence of hardware problems over time. Procedures for conducting all five types of trend analysis are provided in this publication, prepared through the joint efforts of the NASA Trend Analysis Working Group.

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).

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

PubMed

Plassmann, Merle M; Tengstrand, Erik; Åberg, K Magnus; Benskin, Jonathan P

2016-06-01

Non-targeted mass spectrometry-based approaches for detecting novel xenobiotics in biological samples are hampered by the occurrence of naturally fluctuating endogenous substances, which are difficult to distinguish from environmental contaminants. Here, we investigate a data reduction strategy for datasets derived from a biological time series. The objective is to flag reoccurring peaks in the time series based on increasing peak intensities, thereby reducing peak lists to only those which may be associated with emerging bioaccumulative contaminants. As a result, compounds with increasing concentrations are flagged while compounds displaying random, decreasing, or steady-state time trends are removed. As an initial proof of concept, we created artificial time trends by fortifying human whole blood samples with isotopically labelled standards. Different scenarios were investigated: eight model compounds had a continuously increasing trend in the last two to nine time points, and four model compounds had a trend that reached steady state after an initial increase. Each time series was investigated at three fortification levels and one unfortified series. Following extraction, analysis by ultra performance liquid chromatography high-resolution mass spectrometry, and data processing, a total of 21,700 aligned peaks were obtained. Peaks displaying an increasing trend were filtered from randomly fluctuating peaks using time trend ratios and Spearman's rank correlation coefficients. The first approach was successful in flagging model compounds spiked at only two to three time points, while the latter approach resulted in all model compounds ranking in the top 11 % of the peak lists. Compared to initial peak lists, a combination of both approaches reduced the size of datasets by 80-85 %. Overall, non-target time trend screening represents a promising data reduction strategy for identifying emerging bioaccumulative contaminants in biological samples. Graphical abstract Using time trends to filter out emerging contaminants from large peak lists.

Vaporization and Zonal Mixing in Performance Modeling of Advanced LOX-Methane Rockets

NASA Technical Reports Server (NTRS)

Williams, George J., Jr.; Stiegemeier, Benjamin R.

2013-01-01

Initial modeling of LOX-Methane reaction control (RCE) 100 lbf thrusters and larger, 5500 lbf thrusters with the TDK/VIPER code has shown good agreement with sea-level and altitude test data. However, the vaporization and zonal mixing upstream of the compressible flow stage of the models leveraged empirical trends to match the sea-level data. This was necessary in part because the codes are designed primarily to handle the compressible part of the flow (i.e. contraction through expansion) and in part because there was limited data on the thrusters themselves on which to base a rigorous model. A more rigorous model has been developed which includes detailed vaporization trends based on element type and geometry, radial variations in mixture ratio within each of the "zones" associated with elements and not just between zones of different element types, and, to the extent possible, updated kinetic rates. The Spray Combustion Analysis Program (SCAP) was leveraged to support assumptions in the vaporization trends. Data of both thrusters is revisited and the model maintains a good predictive capability while addressing some of the major limitations of the previous version.

Dynamic fuzzy hierarchy analysis for evaluation of professionalization degree

NASA Astrophysics Data System (ADS)

Jin, Lin; Min, Luo; Ma, Jingxi

2016-06-01

This paper presents the model of dynamic fuzzy hierarchy analysis for evaluation of professionalization degree, as a combination of the dynamic fuzzy theory and the AHP, which can show the changes and trends of the value of each index of professionalization.

Content Analysis in Systems Engineering Acquisition Activities

DTIC Science & Technology

2016-04-30

Acquisition Activities Karen Holness, Assistant Professor, NPS Update on the Department of the Navy Systems Engineering Career Competency Model Clifford...systems engineering toolkit . Having a common analysis tool that is easy to use would support the feedback of observed system performance trends from the

Alternative Futures Analysis Of Farmington Bay Wetlands In The Great Salt Lake Ecosystem

EPA Science Inventory

An Alternative Futures Analysis (AFA) was conducted to evaluate tradeoffs between landscape design scenarios and ecological services for Farmington Bay, Great Salt Lake (GSL), wetlands. Model scenarios included both plan trend and conservation "futures" projected to 2030. Scena...

AN ALTERNATIVE FUTURES ANALYSIS OF FARMINGTON BAY WETLANDS IN THE GREAT SALT LAKE

EPA Science Inventory

An Alternative Futures Analysis (AFA) was conducted to evaluate tradeoffs between landscape design scenarios and ecological services for Farmington Bay, Great Salt Lake (GSL), wetlands. Model scenarios included plan trend and conservation "futures" scenarios projected to 2030. ...

Natural and human-induced terrestrial water storage change: A global analysis using hydrological models and GRACE

NASA Astrophysics Data System (ADS)

Felfelani, Farshid; Wada, Yoshihide; Longuevergne, Laurent; Pokhrel, Yadu N.

2017-10-01

Hydrological models and the data derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have been widely used to study the variations in terrestrial water storage (TWS) over large regions. However, both GRACE products and model results suffer from inherent uncertainties, calling for the need to make a combined use of GRACE and models to examine the variations in total TWS and their individual components, especially in relation to natural and human-induced changes in the terrestrial water cycle. In this study, we use the results from two state-of-the-art hydrological models and different GRACE spherical harmonic products to examine the variations in TWS and its individual components, and to attribute the changes to natural and human-induced factors over large global river basins. Analysis of the spatial patterns of the long-term trend in TWS from the two models and GRACE suggests that both models capture the GRACE-measured direction of change, but differ from GRACE as well as each other in terms of the magnitude over different regions. A detailed analysis of the seasonal cycle of TWS variations over 30 river basins shows notable differences not only between models and GRACE but also among different GRACE products and between the two models. Further, it is found that while one model performs well in highly-managed river basins, it fails to reproduce the GRACE-observed signal in snow-dominated regions, and vice versa. The isolation of natural and human-induced changes in TWS in some of the managed basins reveals a consistently declining TWS trend during 2002-2010, however; significant differences are again obvious both between GRACE and models and among different GRACE products and models. Results from the decomposition of the TWS signal into the general trend and seasonality indicate that both models do not adequately capture both the trend and seasonality in the managed or snow-dominated basins implying that the TWS variations from a single model cannot be reliably used for all global regions. It is also found that the uncertainties arising from climate forcing datasets can introduce significant additional uncertainties, making direct comparison of model results and GRACE products even more difficult. Our results highlight the need to further improve the representation of human land-water management and snow processes in large-scale models to enable a reliable use of models and GRACE to study the changes in freshwater systems in all global regions.

Statistical trend analysis and extreme distribution of significant wave height from 1958 to 1999 - an application to the Italian Seas

NASA Astrophysics Data System (ADS)

Martucci, G.; Carniel, S.; Chiggiato, J.; Sclavo, M.; Lionello, P.; Galati, M. B.

2010-06-01

The study is a statistical analysis of sea states timeseries derived using the wave model WAM forced by the ERA-40 dataset in selected areas near the Italian coasts. For the period 1 January 1958 to 31 December 1999 the analysis yields: (i) the existence of a negative trend in the annual- and winter-averaged sea state heights; (ii) the existence of a turning-point in late 80's in the annual-averaged trend of sea state heights at a site in the Northern Adriatic Sea; (iii) the overall absence of a significant trend in the annual-averaged mean durations of sea states over thresholds; (iv) the assessment of the extreme values on a time-scale of thousand years. The analysis uses two methods to obtain samples of extremes from the independent sea states: the r-largest annual maxima and the peak-over-threshold. The two methods show statistical differences in retrieving the return values and more generally in describing the significant wave field. The r-largest annual maxima method provides more reliable predictions of the extreme values especially for small return periods (<100 years). Finally, the study statistically proves the existence of decadal negative trends in the significant wave heights and by this it conveys useful information on the wave climatology of the Italian seas during the second half of the 20th century.

Analysis of soybean production and import trends and its import factors in Indonesia

NASA Astrophysics Data System (ADS)

Ningrum, I. H.; Irianto, H.; Riptanti, E. W.

2018-03-01

This study aims to analyze the factors affecting soybean imports in Indonesia and to know the trend and projection of Indonesian soybean production as well as the import in 2016-2020. The basic method used in this research is the description analysis method. The data used are secondary data in the form of time series data from 1979-2015. Methods of data analysis using simultaneous equations model with 2SLS (Two Stage Least Square) method and Trend analysis. The results showed that the factors affecting soybean imports in Indonesia are consumption and production. Consumption has positive effect while production is negatively affected. The percentage changed in soybean imports is greater than the percentage change in consumption and production of soybeans. Consumption is positively influenced by imports and production, while production is influenced positively by consumption and negative by imports. The production trend of soybean in 2016-2020 has a tendency to increase with a percentage of 11.18% per year. Production in 2016 is projected at 1.110.537 tons while in 2020 it will increase to 1,721,350 tons. The import trend in 2016-2020 has a tendency to increase with an average percentage of 4.13% per year. Import in 2016 is projected at 2.224.188 tons while in 2020 it will increase to 2.611.270 tons.

Sediment transport processes in the Pearl River Estuary as revealed by grain-size end-member modeling and sediment trend analysis

NASA Astrophysics Data System (ADS)

Li, Tao; Li, Tuan-Jie

2018-04-01

The analysis of grain-size distribution enables us to decipher sediment transport processes and understand the causal relations between dynamic processes and grain-size distributions. In the present study, grain sizes were measured from surface sediments collected in the Pearl River Estuary and its adjacent coastal areas. End-member modeling analysis attempts to unmix the grain sizes into geologically meaningful populations. Six grain-size end-members were identified. Their dominant modes are 0 Φ, 1.5 Φ, 2.75 Φ, 4.5 Φ, 7 Φ, and 8 Φ, corresponding to coarse sand, medium sand, fine sand, very coarse silt, silt, and clay, respectively. The spatial distributions of the six end-members are influenced by sediment transport and depositional processes. The two coarsest end-members (coarse sand and medium sand) may reflect relict sediments deposited during the last glacial period. The fine sand end-member would be difficult to transport under fair weather conditions, and likely indicates storm deposits. The three remaining fine-grained end-members (very coarse silt, silt, and clay) are recognized as suspended particles transported by saltwater intrusion via the flood tidal current, the Guangdong Coastal Current, and riverine outflow. The grain-size trend analysis shows distinct transport patterns for the three fine-grained end-members. The landward transport of the very coarse silt end-member occurs in the eastern part of the estuary, the seaward transport of the silt end-member occurs in the western part, and the east-west transport of the clay end-member occurs in the coastal areas. The results show that grain-size end-member modeling analysis in combination with sediment trend analysis help to better understand sediment transport patterns and the associated transport mechanisms.

The influence of ENSO, PDO and PNA on secular rainfall variations in Hawai`i

NASA Astrophysics Data System (ADS)

Frazier, Abby G.; Elison Timm, Oliver; Giambelluca, Thomas W.; Diaz, Henry F.

2017-11-01

Over the last century, significant declines in rainfall across the state of Hawai`i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for these long-term trends. Gridded seasonal rainfall from 1920 to 2012 is used to perform an empirical orthogonal function (EOF) analysis. The leading EOF components are correlated with three indices of natural climate variations (El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA)), and multiple linear regression (MLR) is used to model the leading components with climate indices. PNA is the dominant mode of wet season (November-April) variability, while ENSO is most significant in the dry season (May-October). To assess whether there is an anthropogenic influence on rainfall, two methods are used: a linear trend term is included in the MLR, and pattern correlation coefficients (PCC) are calculated between recent rainfall trends and future changes in rainfall projected by downscaling methods. PCC results indicate that recent observed rainfall trends in the wet season are positively correlated with future expected changes in rainfall, while dry season PCC results do not show a clear pattern. The MLR results, however, show that the trend term adds significantly to model skill only in the dry season. Overall, MLR and PCC results give weak and inconclusive evidence for detection of anthropogenic signals in the observed rainfall trends.

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.

Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China.

PubMed

Zhang, Zhiming; Ouyang, Zhiyun; Xiao, Yi; Xiao, Yang; Xu, Weihua

2017-06-01

Increasing exploitation of karst resources is causing severe environmental degradation because of the fragility and vulnerability of karst areas. By integrating principal component analysis (PCA) with annual seasonal trend analysis (ASTA), this study assessed karst rocky desertification (KRD) within a spatial context. We first produced fractional vegetation cover (FVC) data from a moderate-resolution imaging spectroradiometer normalized difference vegetation index using a dimidiate pixel model. Then, we generated three main components of the annual FVC data using PCA. Subsequently, we generated the slope image of the annual seasonal trends of FVC using median trend analysis. Finally, we combined the three PCA components and annual seasonal trends of FVC with the incidence of KRD for each type of carbonate rock to classify KRD into one of four categories based on K-means cluster analysis: high, moderate, low, and none. The results of accuracy assessments indicated that this combination approach produced greater accuracy and more reasonable KRD mapping than the average FVC based on the vegetation coverage standard. The KRD map for 2010 indicated that the total area of KRD was 78.76 × 10 3  km 2 , which constitutes about 4.06% of the eight southwest provinces of China. The largest KRD areas were found in Yunnan province. The combined PCA and ASTA approach was demonstrated to be an easily implemented, robust, and flexible method for the mapping and assessment of KRD, which can be used to enhance regional KRD management schemes or to address assessment of other environmental issues.

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.

Internal Variability-Generated Uncertainty in East Asian Climate Projections Estimated with 40 CCSM3 Ensembles.

PubMed

Yao, Shuai-Lei; Luo, Jing-Jia; Huang, Gang

2016-01-01

Regional climate projections are challenging because of large uncertainty particularly stemming from unpredictable, internal variability of the climate system. Here, we examine the internal variability-induced uncertainty in precipitation and surface air temperature (SAT) trends during 2005-2055 over East Asia based on 40 member ensemble projections of the Community Climate System Model Version 3 (CCSM3). The model ensembles are generated from a suite of different atmospheric initial conditions using the same SRES A1B greenhouse gas scenario. We find that projected precipitation trends are subject to considerably larger internal uncertainty and hence have lower confidence, compared to the projected SAT trends in both the boreal winter and summer. Projected SAT trends in winter have relatively higher uncertainty than those in summer. Besides, the lower-level atmospheric circulation has larger uncertainty than that in the mid-level. Based on k-means cluster analysis, we demonstrate that a substantial portion of internally-induced precipitation and SAT trends arises from internal large-scale atmospheric circulation variability. These results highlight the importance of internal climate variability in affecting regional climate projections on multi-decadal timescales.

Antarctic Climate Variability: Covariance of Ozone and Sea Ice in Atmosphere - Ocean Coupled Model Simulations

NASA Astrophysics Data System (ADS)

Jrrar, Amna; Abraham, N. Luke; Pyle, John A.; Holland, David

2014-05-01

Changes in sea ice significantly modulate climate change because of its high reflective and insulating nature. While Arctic Sea Ice Extent (SIE) shows a negative trend. Antarctic SIE shows a weak but positive trend, estimated at 0.127 x 106 km2 per decade. The trend results from large regional cancellations, more ice in the Weddell and the Ross seas, and less ice in the Amundsen - Bellingshausen seas. A number of studies had demonstrated that stratospheric ozone depletion has had a major impact on the atmospheric circulation, causing a positive trend in the Southern Annular Mode (SAM), which has been linked to the observed positive trend in autumn sea ice in the Ross Sea. However, other modelling studies show that models forced with prescribed ozone hole simulate decreased sea ice in all regions comparative to a control run. A recent study has also shown that stratospheric ozone recovery will mitigate Antarctic sea ice loss. To verify this assumed relationship, it is important first to investigate the covariance between ozone's natural (dynamical) variability and Antarctic sea ice distribution in pre-industrial climate, to estimate the trend due to natural variability. We investigate the relationship between anomalous Antarctic ozone years and the subsequent changes in Antarctic sea ice distribution in a multidecadal control simulation using the AO-UMUKCA model. The model has a horizontal resolution of 3.75 X 2.5 degrees in longitude and latitude; and 60 hybrid height levels in the vertical, from the surface up to a height of 84 km. The ocean component is the NEMO ocean model on the ORCA2 tripolar grid, and the sea ice model is CICE. We evaluate the model's performance in terms of sea ice distribution, and we calculate sea ice extent trends for composites of anomalously low versus anomalously high SH polar ozone column. We apply EOF analysis to the seasonal anomalies of sea ice concentration, MSLP, and Z 500, and identify the leading climate modes controlling the variability of Antarctic sea ice in each case, and study their relationship with SH polar ozone column.

The Impact of Three-Dimensional Computational Modeling on Student Understanding of Astronomical Concepts: A Quantitative Analysis

ERIC Educational Resources Information Center

Hansen, John; Barnett, Michael; MaKinster, James; Keating, Thomas

2004-01-01

The increased availability of computational modeling software has created opportunities for students to engage in scientific inquiry through constructing computer-based models of scientific phenomena. However, despite the growing trend of integrating technology into science curricula, educators need to understand what aspects of these technologies…

Development of the Texas revenue estimator and needs determination system (T.R.E.N.D.S.) model.

DOT National Transportation Integrated Search

2010-05-01

The original purpose of Project 0-6395-TI was to assess the usefulness and viability of the Joint Analysis : Using Combined Knowledge (J.A.C.K.) model as a planning and forecasting tool. What originally was : named the J.A.C.K. model was substantiall...

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

[Birth rates evolution in Spain. Birth trends in Spain from 1941 to 2010].

PubMed

Andrés de Llano, J M; Alberola López, S; Garmendia Leiza, J R; Quiñones Rubio, C; Cancho Candela, R; Ramalle-Gómara, E

2015-01-01

The aim of this study was to analyse trends of births in Spain and its Autonomous Communities (CCAA) over a 70 year period (1941-2010). The crude birth rates per 1,000 inhabitants/year were calculated by CCAA using Joinpoint regression models. Change points in trend and annual percentage of change (APC) were identified. The distribution of 38,160,305 births between 1941 and 2010 shows important changes in trends both nationally and among the CCAA. There is a general pattern for the whole country, with 5 turning points being identified with changes in trend and annual percentage change (APC). Differences are also found among regions. The analysis of trends in birth rates and the annual rates of change should enable public health authorities to properly plan pediatric care resources in our country. Copyright © 2014 Asociación Española de Pediatría. Published by Elsevier Espana. All rights reserved.

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.

Development of a robust analytical framework for assessing landbird trends, dynamics and relationships with environmental covariates in the North Coast and Cascades Network

USGS Publications Warehouse

Ray, Chris; Saracco, James; Jenkins, Kurt J.; Huff, Mark; Happe, Patricia J.; Ransom, Jason I.

2017-01-01

During 2015-2016, we completed development of a new analytical framework for landbird population monitoring data from the National Park Service (NPS) North Coast and Cascades Inventory and Monitoring Network (NCCN). This new tool for analysis combines several recent advances in modeling population status and trends using point-count data and is designed to supersede the approach previously slated for analysis of trends in the NCCN and other networks, including the Sierra Nevada Network (SIEN). Advances supported by the new model-based approach include 1) the use of combined data on distance and time of detection to estimate detection probability without assuming perfect detection at zero distance, 2) seamless accommodation of variation in sampling effort and missing data, and 3) straightforward estimation of the effects of downscaled climate and other local habitat characteristics on spatial and temporal trends in landbird populations. No changes in the current field protocol are necessary to facilitate the new analyses. We applied several versions of the new model to data from each of 39 species recorded in the three mountain parks of the NCCN, estimating trends and climate relationships for each species during 2005-2014. Our methods and results are also reported in a manuscript in revision for the journal Ecosphere (hereafter, Ray et al.). Here, we summarize the methods and results outlined in depth by Ray et al., discuss benefits of the new analytical framework, and provide recommendations for its application to synthetic analyses of long-term data from the NCCN and SIEN. All code necessary for implementing the new analyses is provided within the Appendices to this report, in the form of fully annotated scripts written in the open-access programming languages R and JAGS.

Precipitation collector bias and its effects on temporal trends and spatial variability in National Atmospheric Deposition Program/National Trends Network data

USGS Publications Warehouse

Wetherbee, Gregory A.

2017-01-01

Precipitation samples have been collected by the National Atmospheric Deposition Program's (NADP) National Trends Network (NTN) using the Aerochem Metrics Model 301 (ACM) collector since 1978. Approximately one-third of the NTN ACM collectors have been replaced with N-CON Systems, Inc. Model ADS 00-120 (NCON) collectors. Concurrent data were collected over 6 years at 12 NTN sites using colocated ACM and NCON collectors in various precipitation regimes. Linear regression models of the colocated data were used to adjust for relative bias between the collectors. Replacement of ACM collectors with NCON collectors resulted in shifts in 10-year seasonal precipitation-weighted mean concentration (PWMC) trend slopes for: cations (−0.001 to −0.007 mgL−1yr−1), anions (−0.009 to −0.028 mgL−1yr−1), and hydrogen ion (+0.689 meqL-1yr−1). Larger shifts in NO3− and SO4−2 seasonal PWMC trend slopes were observed in the Midwest and Northeast US, where concentrations are generally higher than in other regions. Geospatial analysis of interpolated concentration rasters indicated regions of accentuated variability introduced by incorporation of NCON collectors into the NTN.

Characterizing the urban temperature trend using seasonal unit root analysis: Hong Kong from 1970 to 2015

NASA Astrophysics Data System (ADS)

To, Wai-Ming; Yu, Tat-Wai

2016-12-01

This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169°C (10 yr)-1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data — much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865°C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.

Mining Twitter to Assess the Public Perception of the “Internet of Things”

PubMed Central

Yoshigoe, Kenji; Hicks, Amanda; Yuan, Jiawei; He, Zhe; Xie, Mengjun; Guo, Yi; Prosperi, Mattia; Salloum, Ramzi; Modave, François

2016-01-01

Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public’s attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public. PMID:27391760

Mining Twitter to Assess the Public Perception of the "Internet of Things".

PubMed

Bian, Jiang; Yoshigoe, Kenji; Hicks, Amanda; Yuan, Jiawei; He, Zhe; Xie, Mengjun; Guo, Yi; Prosperi, Mattia; Salloum, Ramzi; Modave, François

2016-01-01

Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public's attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.

Models for Threat Assessment in Networks

DTIC Science & Technology

2006-09-01

Software International and Command AntiVirus . [Online]. Available: http://www.commandsoftware.com/virus/newlove.html [38] C. Ng and P. Ferrie. (2000...28 2.3 False positive trends across all population sizes for r=0.7 and m=0.1 . . . . 33 2.4 False negative trends across all population...benefits analysis is often performed to determine the list of mitigation procedures. Traditionally, risk assessment has been done in part with software

Alcohol affordability and alcohol demand: cross-country trends and panel data estimates, 1975 to 2008.

PubMed

Nelson, Jon P

2014-04-01

Relatively little is known about cross-country differences in alcohol affordability or factors that determine differences in affordability over time. This information is potentially important for alcohol policy, especially policies that focus on higher taxes or prices to reduce total alcohol consumption. This study estimates cross-country alcohol consumption relationships using economic models incorporating income and prices and alternative models based on alcohol affordability. The data and analysis are restricted to higher income countries. Data for alcohol consumption per capita (ages 15+) are analyzed for 2 samples: first, 17 countries in the Organisation for Economic Co-operation and Development for the period 1975 to 2000; second, 22 countries in the European Union for the period from 2000 to 2008. Panel data models are utilized, with country and time fixed-effects to control for confounding influences. In economic demand models, covariates are real per capita income and real alcohol price indices. In affordability models, income is divided by prices to yield an index of alcohol affordability. Analysis of data trends reveals that much of the increase in affordability is due to rising real incomes, and not falling real prices. Economic models of demand perform slightly better statistically, but differences are not substantial as income and affordability are highly correlated. For both samples, exogenous rates of growth of alcohol consumption are negative. Price and income elasticities, on average, are within the range of prior estimates. Affordability elasticities are between 0.21 and 0.25. Although alcohol affordability is a valid concept statistically, its use in policy discussions tends to hide underlying causes of changes in affordability. A better approach is a comparison and analysis of trends and cross-country differences in real incomes and real alcohol prices together with the affordability index. Country-level analysis of income and price elasticities also is required. Copyright © 2014 by the Research Society on Alcoholism.

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.

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.

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.

Self-reinforcing feedback loop in financial markets with coupling of market impact and momentum traders

NASA Astrophysics Data System (ADS)

Zhong, Li-Xin; Xu, Wen-Juan; Chen, Rong-Da; Zhong, Chen-Yang; Qiu, Tian; Ren, Fei; He, Yun-Xing

2018-03-01

By incorporating market impact and momentum traders into an agent-based model, we investigate the conditions for the occurrence of self-reinforcing feedback loops and the coevolutionary mechanism of prices and strategies. For low market impact, the price fluctuations are originally large. The existence of momentum traders has little impact on the change of price fluctuations but destroys the equilibrium between the trend-following and trend-rejecting strategies. The trend-following herd behaviors become dominant. A self-reinforcing feedback loop exists. For high market impact, the existence of momentum traders leads to an increase in price fluctuations. The trend-following strategies of rational individuals are suppressed while the trend-following strategies of momentum traders are promoted. The crowd-anticrowd behaviors become dominant. A negative feedback loop exists. A theoretical analysis indicates that, for low market impact, the majority effect is beneficial for the trend-followers to earn more, which in turn promotes the trend-following strategies. For high market impact, the minority effect causes the trend-followers to suffer great losses, which in turn suppresses the trend-following strategies.

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.

Usage Analysis for the Identification of Research Trends in Digital Libraries; Keepers of the Crumbling Culture: What Digital Preservation Can Learn from Library History; Patterns of Journal Use by Scientists through Three Evolutionary Phases; Developing a Content Management System-Based Web Site; Exploring Charging Models for Digital Cultural Heritage in Europe; Visions: The Academic Library in 2012.

ERIC Educational Resources Information Center

Bollen, Johan; Vemulapalli, Soma Sekara; Xu, Weining; Luce, Rick; Marcum, Deanna; Friedlander, Amy; Tenopir, Carol; Grayson, Matt; Zhang, Yan; Ebuen, Mercy; King, Donald W.; Boyce, Peter; Rogers, Clare; Kirriemuir, John; Tanner, Simon; Deegan, Marilyn; Marcum, James W.

2003-01-01

Includes six articles that discuss use analysis and research trends in digital libraries; library history and digital preservation; journal use by scientists; a content management system-based Web site for higher education in the United Kingdom; cost studies for transitioning to digitized collections in European cultural institutions; and the…

Computational mechanics - Advances and trends; Proceedings of the Session - Future directions of Computational Mechanics of the ASME Winter Annual Meeting, Anaheim, CA, Dec. 7-12, 1986

NASA Technical Reports Server (NTRS)

Noor, Ahmed K. (Editor)

1986-01-01

The papers contained in this volume provide an overview of the advances made in a number of aspects of computational mechanics, identify some of the anticipated industry needs in this area, discuss the opportunities provided by new hardware and parallel algorithms, and outline some of the current government programs in computational mechanics. Papers are included on advances and trends in parallel algorithms, supercomputers for engineering analysis, material modeling in nonlinear finite-element analysis, the Navier-Stokes computer, and future finite-element software systems.

Regression model analysis of the decreasing trend of cesium-137 concentration in the atmosphere since the Fukushima accident.

PubMed

Kitayama, Kyo; Ohse, Kenji; Shima, Nagayoshi; Kawatsu, Kencho; Tsukada, Hirofumi

2016-11-01

The decreasing trend of the atmospheric 137 Cs concentration in two cities in Fukushima prefecture was analyzed by a regression model to clarify the relation between the parameter of the decrease in the model and the trend and to compare the trend with that after the Chernobyl accident. The 137 Cs particle concentration measurements were conducted in urban Fukushima and rural Date sites from September 2012 to June 2015. The 137 Cs particle concentrations were separated in two groups: particles of more than 1.1 μm aerodynamic diameters (coarse particles) and particles with aerodynamic diameter lower than 1.1 μm (fine particles). The averages of the measured concentrations were 0.1 mBq m -3 in Fukushima and Date sites. The measured concentrations were applied in the regression model which decomposed them into two components: trend and seasonal variation. The trend concentration included the parameters for the constant and the exponential decrease. The parameter for the constant was slightly different between the Fukushima and Date sites. The parameter for the exponential decrease was similar for all the cases, and much higher than the value of the physical radioactive decay except for the concentration in the fine particles at the Date site. The annual decreasing rates of the 137 Cs concentration evaluated by the trend concentration ranged from 44 to 53% y -1 with average and standard deviation of 49 ± 8% y -1 for all the cases in 2013. In the other years, the decreasing rates also varied slightly for all cases. These indicated that the decreasing trend of the 137 Cs concentration was nearly unchanged for the location and ground contamination level in the three years after the accident. The 137 Cs activity per aerosol particle mass also decreased with the same trend as the 137 Cs concentration in the atmosphere. The results indicated that the decreasing trend of the atmospheric 137 Cs concentration was related with the reduction of the 137 Cs concentration in resuspended particles. Copyright © 2016 Elsevier Ltd. All rights reserved.

A trend analysis of surgical operations under a global payment system in Tehran, Iran (2005–2015)

PubMed Central

Goudari, Faranak Behzadi; Rashidian, Arash; Arab, Mohammad; Mahmoudi, Mahmood

2018-01-01

Background Global payment system is a first example of per-case payment system that contains 60 commonly used surgical operations for which payment is based on the average cost per case in Iran. Objective The aim of the study was to determine the amount of reduction, increase or no change in the trend of global operations. Methods In this retrospective longitudinal study, data on the 60 primary global surgery codes was gathered from Tehran Health Insurance Organization within the ten-year period of 2005–2015 separately, for each month. Out of 60 surgery codes, only acceptable data for 46 codes were available based on the insurance documents sent by medical centers. A quantitative analysis of time series through Regression Analysis Model using STATA software v.11 was performed. Results Some global surgery codes had an upward trend and some were downwards. Of N Codes, N83, N20, N28, N63, and N93 had an upward trend (p<0.05) and N32, N43, N81 and N90 showed a significant downward trend (p<0.05). Similarly, all H Codes except for H18 had a significant upward trend (p<0.000). As such, K Codes including K45, K56 and K81 had an increasing movement. S Codes also experienced both increasing and decreasing trends. However, none of the O Codes changed according to time. Other global surgical codes like C61, E07, M51, L60, J98 (p<0.000), I84 (p<0.031) and I86 (p<0.000) shown upward and downward trends. Total global surgeries trend was significantly upwards (B=24.26109, p<0.000). Conclusion The varying trend of global surgeries can partly reflect the behavior of service providers in order to increase their profits and minimize their costs. PMID:29765576

Tropical cyclone genesis potential index over the western North Pacific simulated by CMIP5 models

NASA Astrophysics Data System (ADS)

Song, Yajuan; Wang, Lei; Lei, Xiaoyan; Wang, Xidong

2015-11-01

Tropical cyclone (TC) genesis over the western North Pacific (WNP) is analyzed using 23 CMIP5 (Coupled Model Intercomparison Project Phase 5) models and reanalysis datasets. The models are evaluated according to TC genesis potential index (GPI). The spatial and temporal variations of the GPI are first calculated using three atmospheric reanalysis datasets (ERA-Interim, NCEP/NCAR Reanalysis-1, and NCEP/DOE Reanalysis-2). Spatial distributions of July-October-mean TC frequency based on the GPI from ERA-interim are more consistent with observed ones derived from IBTrACS global TC data. So, the ERA-interim reanalysis dataset is used to examine the CMIP5 models in terms of reproducing GPI during the period 1982-2005. Although most models possess deficiencies in reproducing the spatial distribution of the GPI, their multimodel ensemble (MME) mean shows a reasonable climatological GPI pattern characterized by a high GPI zone along 20°N in the WNP. There was an upward trend of TC genesis frequency during 1982 to 1998, followed by a downward trend. Both MME results and reanalysis data can represent a robust increasing trend during 1982-1998, but the models cannot simulate the downward trend after 2000. Analysis based on future projection experiments shows that the GPI exhibits no significant change in the first half of the 21st century, and then starts to decrease at the end of the 21st century under the representative concentration pathway (RCP) 2.6 scenario. Under the RCP8.5 scenario, the GPI shows an increasing trend in the vicinity of 20°N, indicating more TCs could possibly be expected over the WNP under future global warming.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.

PubMed

Zhou, Tony; Dickson, Jennifer L; Geoffrey Chase, J

2018-01-01

Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

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

de Foy, Benjamin; Lu, Zifeng; Streets, David G.

The Ozone Monitoring Instrument (OMI) has been estimating NO2 columns from space for over 10 years, and these have been used to estimate emissions and emission trends for point and area sources all over the world. In this study we evaluate the trends in NO2 columns over 54 cities in the USA and Canada to identify the long term trends due to air quality policies, the impact of the Great Recession, and the weekday-weekend effect. A multiple linear regression model is used to fit annual, seasonal and weekly factors for individual swath retrievals along with the impact of temperature, windmore » speed and pixel size. For most cities, the correlation coefficients of the model fit ranges from 0.47 to 0.76. There have been strong reductions in NO2 columns, with annual decreases of up to 7% per year in most cities. During the years of the Great Recession, NO2 columns were as much as 30% lower than they would have been had they followed the linear annual trend. The analysis yielded insights into the timing of the reductions, with some cities in the northwest and in the east experiencing reductions in 2008 already, and most areas back to where they would have been based on the uniform trend by 2011. The analysis also finds that reductions in columns during the weekend vary significantly from city to city, with a range in reductions of 10%-30% on Saturdays, and 20%-50% on Sundays.« less

Analysis of European ozone trends in the period 1995-2014

NASA Astrophysics Data System (ADS)

Yan, Yingying; Pozzer, Andrea; Ojha, Narendra; Lin, Jintai; Lelieveld, Jos

2018-04-01

Surface-based measurements from the EMEP and Airbase networks are used to estimate the changes in surface ozone levels during the 1995-2014 period over Europe. We find significant ozone enhancements (0.20-0.59 µg m-3 yr-1 for the annual means; P-value < 0.01 according to an F-test) over the European suburban and urban stations during 1995-2012 based on the Airbase sites. For European background ozone observed at EMEP sites, it is shown that a significantly decreasing trend in the 95th percentile ozone concentrations has occurred, especially at noon (0.9 µg m-3 yr-1; P-value < 0.01), while the 5th percentile ozone concentrations continued to increase with a trend of 0.3 µg m-3 yr-1 (P-value < 0.01) during the study period. With the help of numerical simulations performed with the global chemistry-climate model EMAC, the importance of anthropogenic emissions changes in determining these changes over background sites are investigated. The EMAC model is found to successfully capture the observed temporal variability in mean ozone concentrations, as well as the contrast in the trends of 95th and 5th percentile ozone over Europe. Sensitivity simulations and statistical analysis show that a decrease in European anthropogenic emissions had contrasting effects on surface ozone trends between the 95th and 5th percentile levels and that background ozone levels have been influenced by hemispheric transport, while climate variability generally regulated the inter-annual variations of surface ozone in Europe.

Trends and projections of Southern Hemisphere baroclinicity: the role of external forcing and impact on Australian rainfall

NASA Astrophysics Data System (ADS)

Frederiksen, Carsten S.; Frederiksen, Jorgen S.; Sisson, Janice M.; Osbrough, Stacey L.

2017-05-01

Changes in the characteristics of Southern Hemisphere (SH) storms, in all seasons, during the second half of the twentieth century, have been related to changes in the annual cycle of SH baroclinic instability. In particular, significant negative trends in baroclinic instability, as measured by the Phillips Criterion, have been found in the region of the climatological storm tracks; a zonal band of significant positive trends occur further poleward. Corresponding to this decrease/increase in baroclinic instability there is a decrease/increase in the growth rate of storm formation at these latitudes over this period, and in some cases a preference for storm formation further poleward than normal. Based on model output from a multi-model ensemble (MME) of coupled atmosphere-ocean general circulation models, it is shown that these trends are the result of external radiative forcing, including anthropogenic greenhouse gases, ozone, aerosols and land-use change. The MME is used in an analysis of variance method to separate the internal (natural) variability in the Phillips Criterion from influences associated with anomalous external radiative forcing. In all seasons, the leading externally forced mode has a significant trend and a loading pattern highly correlated with the pattern of trends in the Phillips Criterion. The covariance between the externally forced component of SH rainfall and the leading external mode strongly resembles the MME pattern of SH rainfall trends. A comparison between similar analyses of MME simulations using the second half of the twenty-first century of the Representative Concentration Pathways (RCP) RCP8.5 and RCP4.5 scenarios show that trends in the Phillips Criterion and rainfall are projected to continue and intensify under increasing anthropogenic greenhouse gas concentrations.

Analysis and modeling of flicker noise in lateral asymmetric channel MOSFETs

NASA Astrophysics Data System (ADS)

Agarwal, Harshit; Kushwaha, Pragya; Gupta, Chetan; Khandelwal, Sourabh; Hu, Chenming; Chauhan, Yogesh Singh

2016-01-01

In this paper, flicker noise behavior of lateral non-uniformly doped MOSFET is studied using impedance field method. Our study shows that Klaassen Prins (KP) method, which forms the basis of noise model in MOSFETs, underestimates flicker noise in such devices. The same KP method overestimates thermal noise by 2-3 orders of magnitude in similar devices as demonstrated in Roy et al. (2007). This apparent discrepancy between thermal and flicker noise behavior lies in origin of these noises, which leads to opposite trend of local noise power spectral density vs doping. We have modeled the physics behind such behavior, which also explain the trends observed in the measurements (Agarwal et al., 2015).

Effect of Thermal Diffusivity on the Detectability of TNDE

NASA Technical Reports Server (NTRS)

Zhao, Junduo; Chu, Tsuchin; Russell, Samuel S.

2000-01-01

The effect of thermal diffusively on the defect detectability in Carbon/Epoxy composite panels by transient thermography is presented in this paper. A series of Finite Element Models were constructed and analyzed to simulate the transient heat transfer phenomenon during Thermographic Non-destructive Evaluation (TNDE) of composite panels with square defects. Six common carbon fibers were considered. The models were built for composites with various combinations of fibers and volumetric ratios. Finite Element Analysis of these models showed the trends of the detectable range and the maximum thermal contrast versus the thermal diffusivity of various composites. Additionally, the trends of defect size to depth ratio and the thermal contrast has been investigated.

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.

Nonlinear analysis of gait kinematics to track changes in oxygen consumption in prolonged load carriage walking: a pilot study.

PubMed

Schiffman, Jeffrey M; Chelidze, David; Adams, Albert; Segala, David B; Hasselquist, Leif

2009-09-18

Linking human mechanical work to physiological work for the purpose of developing a model of physical fatigue is a complex problem that cannot be solved easily by conventional biomechanical analysis. The purpose of the study was to determine if two nonlinear analysis methods can address the fundamental issue of utilizing kinematic data to track oxygen consumption from a prolonged walking trial: we evaluated the effectiveness of dynamical systems and fractal analysis in this study. Further, we selected, oxygen consumption as a measure to represent the underlying physiological measure of fatigue. Three male US Army Soldier volunteers (means: 23.3 yr; 1.80 m; 77.3 kg) walked for 120 min at 1.34 m/s with a 40-kg load on a level treadmill. Gait kinematic data and oxygen consumption (VO(2)) data were collected over the 120-min period. For the fractal analysis, utilizing stride interval data, we calculated fractal dimension. For the dynamical systems analysis, kinematic angle time series were used to estimate phase space warping based features at uniform time intervals: smooth orthogonal decomposition (SOD) was used to extract slowly time-varying trends from these features. Estimated fractal dimensions showed no apparent trend or correlation with independently measured VO(2). While inter-individual difference did exist in the VO(2) data, dominant SOD time trends tracked and correlated with the VO(2) for all volunteers. Thus, dynamical systems analysis using gait kinematics may be suitable to develop a model to predict physiologic fatigue based on biomechanical work.

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-

Genetic diversity trend in Indian rice varieties: an analysis using SSR markers.

PubMed

Singh, Nivedita; Choudhury, Debjani Roy; Tiwari, Gunjan; Singh, Amit Kumar; Kumar, Sundeep; Srinivasan, Kalyani; Tyagi, R K; Sharma, A D; Singh, N K; Singh, Rakesh

2016-09-05

The knowledge of the extent and pattern of diversity in the crop species is a prerequisite for any crop improvement as it helps breeders in deciding suitable breeding strategies for their future improvement. Rice is the main staple crop in India with the large number of varieties released every year. Studies based on the small set of rice genotypes have reported a loss in genetic diversity especially after green revolution. However, a detailed study of the trend of diversity in Indian rice varieties is lacking. SSR markers have proven to be a marker of choice for studying the genetic diversity. Therefore, the present study was undertaken with the aim to characterize and assess trends of genetic diversity in a large set of Indian rice varieties (released between 1940-2013), conserved in the National Gene Bank of India using SSR markers. A set of 729 Indian rice varieties were genotyped using 36 HvSSR markers to assess the genetic diversity and genetic relationship. A total of 112 alleles was amplified with an average of 3.11 alleles per locus with mean Polymorphic Information Content (PIC) value of 0.29. Cluster analysis grouped these varieties into two clusters whereas the model based population structure divided them into three populations. AMOVA study based on hierarchical cluster and model based approach showed 3 % and 11 % variation between the populations, respectively. Decadal analysis for gene diversity and PIC showed increasing trend from 1940 to 2005, thereafter values for both the parameters showed decreasing trend between years 2006-2013. In contrast to this, allele number demonstrated increasing trend in these varieties released and notified between1940 to 1985, it remained nearly constant during 1986 to 2005 and again showed an increasing trend. Our results demonstrated that the Indian rice varieties harbors huge amount of genetic diversity. However, the trait based improvement program in the last decades forced breeders to rely on few parents, which resulted in loss of gene diversity during 2006 to 2013. The present study indicates the need for broadening the genetic base of Indian rice varieties through the use of diverse parents in the current breeding program.

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 metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

PubMed

Wiback, Sharon J; Mahadevan, Radhakrishnan; Palsson, Bernhard Ø

2004-05-05

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques. Copyright 2004 Wiley Periodicals, Inc.

Annual minimum temperature variations in early 21st century in Punjab, Pakistan

NASA Astrophysics Data System (ADS)

Jahangir, Misbah; Maria Ali, Syeda; Khalid, Bushra

2016-01-01

Climate change is a key emerging threat to the global environment. It imposes long lasting impacts both at regional and national level. In the recent era, global warming and extreme temperatures have drawn great interest to the scientific community. As in a past century considerable increase in global surface temperatures have been observed and predictions revealed that it will continue in the future. In this regard, current study mainly focused on analysis of regional climatic change (annual minimum temperature trends and its correlation with land surface temperatures in the early 21st century in Punjab) for a period of 1979-2013. The projected model data European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) has been used for eight Tehsils of Punjab i.e., annual minimum temperatures and annual seasonal temperatures. Trend analysis of annual minimum and annual seasonal temperature in (Khushab, Noorpur, Sargodha, Bhalwal, Sahiwal, Shahpur, Sillanwali and Chinoit) tehsils of Punjab was carried out by Regression analysis and Mann-Kendall test. Landsat 5 Thematic Mapper (TM) data was used in comparison with Model data for the month of May from the years 2000, 2009 and 2010. Results showed that no significant trends were observed in annual minimum temperature. A significant change was observed in Noorpur, Bhalwal, Shahpur, Sillanwali, Sahiwal, Chinoit and Sargodha tehsils during spring season, which indicated that this particular season was a transient period of time.

The Fusion of Financial Analysis and Seismology: Statistical Methods from Financial Market Analysis Applied to Earthquake Data

NASA Astrophysics Data System (ADS)

Ohyanagi, S.; Dileonardo, C.

2013-12-01

As a natural phenomenon earthquake occurrence is difficult to predict. Statistical analysis of earthquake data was performed using candlestick chart and Bollinger Band methods. These statistical methods, commonly used in the financial world to analyze market trends were tested against earthquake data. Earthquakes above Mw 4.0 located on shore of Sanriku (37.75°N ~ 41.00°N, 143.00°E ~ 144.50°E) from February 1973 to May 2013 were selected for analysis. Two specific patterns in earthquake occurrence were recognized through the analysis. One is a spread of candlestick prior to the occurrence of events greater than Mw 6.0. A second pattern shows convergence in the Bollinger Band, which implies a positive or negative change in the trend of earthquakes. Both patterns match general models for the buildup and release of strain through the earthquake cycle, and agree with both the characteristics of the candlestick chart and Bollinger Band analysis. These results show there is a high correlation between patterns in earthquake occurrence and trend analysis by these two statistical methods. The results of this study agree with the appropriateness of the application of these financial analysis methods to the analysis of earthquake occurrence.

Regional aerosol trends over the North Atlantic Ocean since 2002: identifying and attributing using satellite, surface, and model datasets

NASA Astrophysics Data System (ADS)

Jongeward, A.; Li, Z.

2017-12-01

Aerosols from natural and anthropogenic sources can influence atmospheric variability and alter Earth's radiative balance through direct and indirect processes. Recently, policies targeting anthropogenic species (e.g. the Clean Air Act) have seen success in improving air quality. The anthropogenic contributions to the total aerosol loading and its spatiotemporal pattern/trend are anticipated to be altered. In this work the aerosol loading and trend over the North Atlantic Ocean since 2002 are examined, a period of significant change due to anthropogenic emissions control measures within the U.S. Monthly mean data from satellite (MODIS), ground (AERONET, IMPROVE), and model (GOCART, MERRA) sources are employed. Two annual trends in aerosol optical depth (AOD) observed by MODIS are present: a -0.020 decade-1 trend in the mid-latitudes and a 0.015 decade-1 trend in the sub-tropics. Trends in GOCART species AOD reveal anthropogenic (natural) species as the likely driver of the mid-latitude (sub-tropical) trend. AERONET AOD trends confirm negative AOD trends at three upwind sites in the Eastern U.S. and IMPROVE particulate matter (PM) observations identifies the role of decreasing ammonium sulfate in the overall PM decrease. Meanwhile, an increasing AOD trend seen during summertime in the eastern sub-tropics is associated with dust aerosol from North Africa. A dust parameterization from Kaufman et al. (2005) allows for changes in the flux transport across the sub-tropics to be calculated and analyzed. Using MERRA reanalysis fields, it is hypothesized that amplified warming and increases in baroclinic instability over the Saharan desert may lead to increased dust mobilization and export from North Africa to the sub-tropical Atlantic. This study provides updated analysis through 2016.

Properties of some statistics for AR-ARCH model with application to technical analysis

NASA Astrophysics Data System (ADS)

Huang, Xudong; Liu, Wei

2009-03-01

In this paper, we investigate some popular technical analysis indexes for AR-ARCH model as real stock market. Under the given conditions, we show that the corresponding statistics are asymptotically stationary and the law of large numbers hold for frequencies of the stock prices falling out normal scope of these technical analysis indexes under AR-ARCH, and give the rate of convergence in the case of nonstationary initial values, which give a mathematical rationale for these methods of technical analysis in supervising the security trends.

Periodic Properties and Inquiry: Student Mental Models Observed during a Periodic Table Puzzle Activity

ERIC Educational Resources Information Center

Larson, Kathleen G.; Long, George R.; Briggs, Michael W.

2012-01-01

The mental models of both novice and advanced chemistry students were observed while the students performed a periodic table activity. The mental model framework seems to be an effective way of analyzing student behavior during learning activities. The analysis suggests that students do not recognize periodic trends through the examination of…

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.

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.

Effects of climate change on Salmonella infections.

PubMed

Akil, Luma; Ahmad, H Anwar; Reddy, Remata S

2014-12-01

Climate change and global warming have been reported to increase spread of foodborne pathogens. To understand these effects on Salmonella infections, modeling approaches such as regression analysis and neural network (NN) were used. Monthly data for Salmonella outbreaks in Mississippi (MS), Tennessee (TN), and Alabama (AL) were analyzed from 2002 to 2011 using analysis of variance and time series analysis. Meteorological data were collected and the correlation with salmonellosis was examined using regression analysis and NN. A seasonal trend in Salmonella infections was observed (p<0.001). Strong positive correlation was found between high temperature and Salmonella infections in MS and for the combined states (MS, TN, AL) models (R(2)=0.554; R(2)=0.415, respectively). NN models showed a strong effect of rise in temperature on the Salmonella outbreaks. In this study, an increase of 1°F was shown to result in four cases increase of Salmonella in MS. However, no correlation between monthly average precipitation rate and Salmonella infections was observed. There is consistent evidence that gastrointestinal infection with bacterial pathogens is positively correlated with ambient temperature, as warmer temperatures enable more rapid replication. Warming trends in the United States and specifically in the southern states may increase rates of Salmonella infections.

Weakened stratospheric quasibiennial oscillation driven by increased tropical mean upwelling.

PubMed

Kawatani, Yoshio; Hamilton, Kevin

2013-05-23

The zonal wind in the tropical stratosphere switches between prevailing easterlies and westerlies with a period of about 28 months. In the lowermost stratosphere, the vertical structure of this quasibiennial oscillation (QBO) is linked to the mean upwelling, which itself is a key factor in determining stratospheric composition. Evidence for changes in the QBO have until now been equivocal, raising questions as to the extent of stratospheric circulation changes in a global warming context. Here we report an analysis of near-equatorial radiosonde observations for 1953-2012, and reveal a long-term trend of weakening amplitude in the zonal wind QBO in the tropical lower stratosphere. The trend is particularly notable at the 70-hectopascal pressure level (an altitude of about 19 kilometres), where the QBO amplitudes dropped by roughly one-third over the period. This trend is also apparent in the global warming simulations of the four models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) that realistically simulate the QBO. The weakening is most reasonably explained as resulting from a trend of increased mean tropical upwelling in the lower stratosphere. Almost all comprehensive climate models have projected an intensifying tropical upwelling in global warming scenarios, but attempts to estimate changes in the upwelling by using observational data have yielded ambiguous, inconclusive or contradictory results. Our discovery of a weakening trend in the lower-stratosphere QBO amplitude provides strong support for the existence of a long-term trend of enhanced upwelling near the tropical tropopause.

Analysis of Patent Activity in the Field of Quantum Information Processing

NASA Astrophysics Data System (ADS)

Winiarczyk, Ryszard; Gawron, Piotr; Miszczak, Jarosław Adam; Pawela, Łukasz; Puchała, Zbigniew

2013-03-01

This paper provides an analysis of patent activity in the field of quantum information processing. Data from the PatentScope database from the years 1993-2011 was used. In order to predict the future trends in the number of filed patents time series models were used.

Trends and Patterns in Cultural Resource Significance: An Historical Perspective and Annotated Bibliography

DTIC Science & Technology

1997-04-01

to tracing historical trends in archaeological method and theory ). The literature sum- marized here is extensive and is not accessible widely to the...of new signifi- cance assessment models. The more specific objectives in undertaking this literary review and interpretive analysis of archaeological...method and theory characteristic of the ’New Archaeology’ of the late 1960s. Once these ideas had made their way into the early literature on

Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China).

PubMed

Cui, Lifang; Wang, Lunche; Singh, Ramesh P; Lai, Zhongping; Jiang, Liangliang; Yao, Rui

2018-05-23

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years.

Degradation trend estimation of slewing bearing based on LSSVM model

NASA Astrophysics Data System (ADS)

Lu, Chao; Chen, Jie; Hong, Rongjing; Feng, Yang; Li, Yuanyuan

2016-08-01

A novel prediction method is proposed based on least squares support vector machine (LSSVM) to estimate the slewing bearing's degradation trend with small sample data. This method chooses the vibration signal which contains rich state information as the object of the study. Principal component analysis (PCA) was applied to fuse multi-feature vectors which could reflect the health state of slewing bearing, such as root mean square, kurtosis, wavelet energy entropy, and intrinsic mode function (IMF) energy. The degradation indicator fused by PCA can reflect the degradation more comprehensively and effectively. Then the degradation trend of slewing bearing was predicted by using the LSSVM model optimized by particle swarm optimization (PSO). The proposed method was demonstrated to be more accurate and effective by the whole life experiment of slewing bearing. Therefore, it can be applied in engineering practice.

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…

Developing Public Education Policy through Policy-Impact Analysis.

ERIC Educational Resources Information Center

Hackett, E. Raymond; And Others

A model for analyzing policy impacts is presented that will assist state-level policy makers in education. The model comprises four stages: (1) monitoring, which includes the identification of relevant trends and issues and the development of a data base; (2) forecasting, which uses quantitative and qualitative techniques developed in futures…

Landsat analysis of tropical forest succession employing a terrain model

NASA Technical Reports Server (NTRS)

Barringer, T. H.; Robinson, V. B.; Coiner, J. C.; Bruce, R. C.

1980-01-01

Landsat multispectral scanner (MSS) data have yielded a dual classification of rain forest and shadow in an analysis of a semi-deciduous forest on Mindonoro Island, Philippines. Both a spatial terrain model, using a fifth side polynomial trend surface analysis for quantitatively estimating the general spatial variation in the data set, and a spectral terrain model, based on the MSS data, have been set up. A discriminant analysis, using both sets of data, has suggested that shadowing effects may be due primarily to local variations in the spectral regions and can therefore be compensated for through the decomposition of the spatial variation in both elevation and MSS data.

Reevaluation of Stratospheric Ozone Trends From SAGE II Data Using a Simultaneous Temporal and Spatial Analysis

NASA Technical Reports Server (NTRS)

Damadeo, R. P.; Zawodny, J. M.; Thomason, L. W.

2014-01-01

This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data is prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.

Bayes Factor based on the Trend Test Incorporating Hardy-Weinberg Disequilibrium: More Powerful to Detect Genetic Association

PubMed Central

Xu, Jinfeng; Yuan, Ao; Zheng, Gang

2012-01-01

Summary In the analysis of case-control genetic association, the trend test and Pearson’s test are the two most commonly used tests. In genome-wide association studies (GWAS), Bayes factor is a useful tool to support significant p-values, and a better measure than p-value when results are compared across studies with different sample sizes. When reporting the p-value of the trend test, we propose a Bayes factor directly based on the trend test. To improve the power to detect association under recessive or dominant genetic models, we propose a Bayes factor based on the trend test and incorporating Hardy-Weinberg disequilibrium in cases. When the true model is unknown, or both the trend test and Pearson’s test or other robust tests are applied in genome-wide scans, we propose a joint Bayes factor, combining the previous two Bayes factors. All three Bayes factors studied in this paper have closed forms and are easy to compute without integrations, so they can be reported along with p-values, especially in GWAS. We discuss how to use each of them and how to specify priors. Simulation studies and applications to three GWAS are provided to illustrate their usefulness to detect non-additive gene susceptibility in practice. PMID:22607017

The trend of the multi-scale temporal variability of precipitation in Colorado River Basin

NASA Astrophysics Data System (ADS)

Jiang, P.; Yu, Z.

2011-12-01

Hydrological problems like estimation of flood and drought frequencies under future climate change are not well addressed as a result of the disability of current climate models to provide reliable prediction (especially for precipitation) shorter than 1 month. In order to assess the possible impacts that multi-scale temporal distribution of precipitation may have on the hydrological processes in Colorado River Basin (CRB), a comparative analysis of multi-scale temporal variability of precipitation as well as the trend of extreme precipitation is conducted in four regions controlled by different climate systems. Multi-scale precipitation variability including within-storm patterns and intra-annual, inter-annual and decadal variabilities will be analyzed to explore the possible trends of storm durations, inter-storm periods, average storm precipitation intensities and extremes under both long-term natural climate variability and human-induced warming. Further more, we will examine the ability of current climate models to simulate the multi-scale temporal variability and extremes of precipitation. On the basis of these analyses, a statistical downscaling method will be developed to disaggregate the future precipitation scenarios which will provide a more reliable and finer temporal scale precipitation time series for hydrological modeling. Analysis results and downscaling results will be presented.

Advances and trends in the development of computational models for tires

NASA Technical Reports Server (NTRS)

Noor, A. K.; Tanner, J. A.

1985-01-01

Status and some recent developments of computational models for tires are summarized. Discussion focuses on a number of aspects of tire modeling and analysis including: tire materials and their characterization; evolution of tire models; characteristics of effective finite element models for analyzing tires; analysis needs for tires; and impact of the advances made in finite element technology, computational algorithms, and new computing systems on tire modeling and analysis. An initial set of benchmark problems has been proposed in concert with the U.S. tire industry. Extensive sets of experimental data will be collected for these problems and used for evaluating and validating different tire models. Also, the new Aircraft Landing Dynamics Facility (ALDF) at NASA Langley Research Center is described.

Trends in abuse and misuse of prescription opioids among older adults.

PubMed

West, Nancy A; Severtson, Stevan G; Green, Jody L; Dart, Richard C

2015-04-01

Dramatic increases in the prescriptive use of opioid analgesics during the past two decades have been paralleled by alarming increases in rates of the abuse and intentional misuse of these drugs. We examined recent trends in the abuse and misuse and associated fatal outcomes among older adults (60+ years) and compared these to trends among younger adults (20-59 years). Trend analysis using linear regression models was used to analyze 184,136 cases and 1149 deaths associated with abuse and misuse of the prescription opioids oxycodone, fentanyl, hydrocodone, morphine, oxymorphone, hydromorphone, methadone, buprenorphine, tramadol, and tapentadol that were reported to participating U.S. Poison Centers of the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS(®)) System between 2006-Q1 and 2013-Q4. Rates of abuse and misuse of prescription opioids were lower for older adults than for younger adults; however, mortality rates among the older ages followed an increasing linear trend (P < 0.0001) and surpassed rates for younger adults in 2012 and 2013. In contrast, mortality rates among younger adults rose and fell during the period, with recent rates trending downward (P = 0.0003 for quadratic trend). Sub-analysis revealed an increasing linear trend among older adults specifically for suicidal intent (P < 0.0001), whereas these rates increased and then decreased among younger adults (P < 0.0001 for quadratic trend). Recent linear increases in rates of death and use of prescription opioids with suicidal intent among older adults have important implications as the U.S. undergoes a rapid expansion of its elderly population. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Seasonal responses of terrestrial ecosystem water-use efficiency to climate change.

PubMed

Huang, Mengtian; Piao, Shilong; Zeng, Zhenzhong; Peng, Shushi; Ciais, Philippe; Cheng, Lei; Mao, Jiafu; Poulter, Ben; Shi, Xiaoying; Yao, Yitong; Yang, Hui; Wang, Yingping

2016-06-01

Ecosystem water-use efficiency (EWUE) is an indicator of carbon-water interactions and is defined as the ratio of carbon assimilation (GPP) to evapotranspiration (ET). Previous research suggests an increasing long-term trend in annual EWUE over many regions and is largely attributed to the physiological effects of rising CO2 . The seasonal trends in EWUE, however, have not yet been analyzed. In this study, we investigate seasonal EWUE trends and responses to various drivers during 1982-2008. The seasonal cycle for two variants of EWUE, water-use efficiency (WUE, GPP/ET), and transpiration-based WUE (WUEt , the ratio of GPP and transpiration), is analyzed from 0.5° gridded fields from four process-based models and satellite-based products, as well as a network of 63 local flux tower observations. WUE derived from flux tower observations shows moderate seasonal variation for most latitude bands, which is in agreement with satellite-based products. In contrast, the seasonal EWUE trends are not well captured by the same satellite-based products. Trend analysis, based on process-model factorial simulations separating effects of climate, CO2 , and nitrogen deposition (NDEP), further suggests that the seasonal EWUE trends are mainly associated with seasonal trends of climate, whereas CO2 and NDEP do not show obvious seasonal difference in EWUE trends. About 66% grid cells show positive annual WUE trends, mainly over mid- and high northern latitudes. In these regions, spring climate change has amplified the effect of CO2 in increasing WUE by more than 0.005 gC m(-2)  mm(-1)  yr(-1) for 41% pixels. Multiple regression analysis further shows that the increase in springtime WUE in the northern hemisphere is the result of GPP increasing faster than ET because of the higher temperature sensitivity of GPP relative to ET. The partitioning of annual EWUE to seasonal components provides new insight into the relative sensitivities of GPP and ET to climate, CO2, and NDEP. © 2015 John Wiley & Sons Ltd.

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.

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…

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends

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

Mao, Jiafu; Shi, Xiaoying; Ricciuto, Daniel M.

Here, we examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982-2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increased trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO 2 ranked second in these models after the predominant climatic influences, and yielded a decreasing trend in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increased nitrogen deposition slightly amplifiedmore » global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.« less

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends

DOE PAGES

Mao, Jiafu; Shi, Xiaoying; Ricciuto, Daniel M.; ...

2015-09-08

Here, we examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982-2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increased trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO 2 ranked second in these models after the predominant climatic influences, and yielded a decreasing trend in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increased nitrogen deposition slightly amplifiedmore » global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.« less

Potential influences of neglecting aerosol effects on the NCEP GFS precipitation forecast

NASA Astrophysics Data System (ADS)

Jiang, Mengjiao; Feng, Jinqin; Li, Zhanqing; Sun, Ruiyu; Hou, Yu-Tai; Zhu, Yuejian; Wan, Bingcheng; Guo, Jianping; Cribb, Maureen

2017-11-01

Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using ground-based and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m-2. All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.

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.

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.

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.

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.

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.

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.

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.

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

Methodology for the Assessment of 3D Conduction Effects in an Aerothermal Wind Tunnel Test

NASA Technical Reports Server (NTRS)

Oliver, Anthony Brandon

2010-01-01

This slide presentation reviews a method for the assessment of three-dimensional conduction effects during test in a Aerothermal Wind Tunnel. The test objectives were to duplicate and extend tests that were performed during the 1960's on thermal conduction on proturberance on a flat plate. Slides review the 1D versus 3D conduction data reduction error, the analysis process, CFD-based analysis, loose coupling method that simulates a wind tunnel test run, verification of the CFD solution, Grid convergence, Mach number trend, size trends, and a Sumary of the CFD conduction analysis. Other slides show comparisons to pretest CFD at Mach 1.5 and 2.16 and the geometries of the models and grids.

Content Analysis of "School Psychology International", 1990-2011: An Analysis of Trends and Compatibility with the NASP Practice Model

ERIC Educational Resources Information Center

Little, Steven G.; Akin-Little, Angeleque; Lloyd, Keryn

2011-01-01

Formal analysis of research publications serves as one indicator of the current status of a profession or a journal. Content analyses provide both practitioners and academicians with information on the status of research in the profession. These types of analyses can also provide information on the concordance between published research and what…

Cretaceous combined structure in eastern Sichuan Basin, China

NASA Astrophysics Data System (ADS)

Wang, P.; Liu, S.

2009-12-01

Eastern Sichuan Basin is confined by two thin-skinned fold-thrust belt, NW-trending Southern Daba Shan (Shan=Mountain) (SDB) in the northeast and NNE- or NE-trending Western XueFeng Shan (WXF) in the southeast, which constitute two convergent salients convex to the inner basin respectively. Although many factors can lead to the formation of fold-thrust belt salients, the eastern Sichuan salients would be attributed to the combined structure (firstly nominated by Chinese geologist, Li Siguang), which means the interaction of two structural belts in the same period. By field surveying and geological map interpreting, we found that WXF deformation began in Late Jurassic along the eastern side of structral belt, where the synclines cored by Upper-Middle Jurassic rock. The initial time of SDB deformation remains poorly determined, however our palaeocurrent data of Lower Cretaceous rock in adjecent foreland basin indicate the provenance from northeast or east. Hence we considered the two fold-thrust belt started interactive in Late Jurassic and mainly combined during Cretaceous. In Early Cretaceous, the front belt of WXF salient arrived near KaiXian where NEE-trending arc-shape folds converged with the NWW-trending arc-shape folds of SDB.The two salients shaped like an westward "open mouth", east of which EW-trending folds of two structural belts juxtaposed. Particularly in the middle belt of WXF (FengJie - WuFeng) the earlier NEE-trending folds were refolded by later NNE-trending folds. We interpret the NEE-trending folds as the front belt of earlier (maybe Late Jurassic) WXF salient. When the two combined fold belts propagated westward together, the original NNE-trending front belt of WXF constrained by the front belt of SDB and formed the curved fold trend lines convex to NNW. Then as WXF deformation continued but SDB gradually terminated, the consequent NNE-trending folds could not be curved and would superpose on the earlier NEE-trending folds.In Late Cretaceous, WXF still propagated westward but without combination with SDB, and formed three NNE-trending parallel anticlines flanking the central Sichuan Basin. These anticlines dominated by steep dips and west-vergent thrust faults, which suggests the eastward back pushing force. We suppose that the pre-existing deep fault obstructed the WXF westward propagation. In addition, thermochronolgy analysis proved that SDB underwent tectonic sequence in Late Cretaceous. Thus the convergent salients broke up with only NNE-trending parallel fold being present in the front belt of WXF. We also use a finite-element model (FEM) to illustrate the maximum horizontal compressive stress (SHmax) under the combined structure in ABAQUSTM software. A 2D plane stress model with realistic mechanical properties for whole Sichuan Basin was built based on the Late Jurassic paleogeographic boundaries. The model consists of 5,400 elements, providing a resolution of 0.1° in both latitude and longitude. In general, FEM analysis result shows the SHmax direction well perpendicular to the arc-shape folds trend lines in eastern Sichuan Basin when pressure loaded on the SDB and WXF boundaries. The SHmax contours reflect two convergent salients incorporating the gradually decreased stress value from the boundaries to inner basin.

Detection of the spatiotemporal trends of mercury in Lake Erie fish communities: a Bayesian approach.

PubMed

Azim, M Ekram; Kumarappah, Ananthavalli; Bhavsar, Satyendra P; Backus, Sean M; Arhonditsis, George

2011-03-15

The temporal trends of total mercury (THg) in four fish species in Lake Erie were evaluated based on 35 years of fish contaminant data. Our Bayesian statistical approach consists of three steps aiming to address different questions. First, we used the exponential and mixed-order decay models to assess the declining rates in four intensively sampled fish species, i.e., walleye (Stizostedion vitreum), yellow perch (Perca flavescens), smallmouth bass (Micropterus dolomieui), and white bass (Morone chrysops). Because the two models postulate monotonic decrease of the THg levels, we included first- and second-order random walk terms in our statistical formulations to accommodate nonmonotonic patterns in the data time series. Our analysis identified a recent increase in the THg concentrations, particularly after the mid-1990s. In the second step, we used double exponential models to quantify the relative magnitude of the THg trends depending on the type of data used (skinless-boneless fillet versus whole fish data) and the fish species examined. The observed THg concentrations were significantly higher in skinless boneless fillet than in whole fish portions, while the whole fish portions of walleye exhibited faster decline rates and slower rates of increase relative to the skinless boneless fillet data. Our analysis also shows lower decline rates and higher rates of increase in walleye relative to the other three fish species examined. The food web structural shifts induced by the invasive species (dreissenid mussels and round goby) may be associated with the recent THg trends in Lake Erie fish.

Advances and trends in structures and dynamics; Proceedings of the Symposium, Washington, DC, October 22-25, 1984

NASA Technical Reports Server (NTRS)

Noor, A. K. (Editor); Hayduk, R. J. (Editor)

1985-01-01

Among the topics discussed are developments in structural engineering hardware and software, computation for fracture mechanics, trends in numerical analysis and parallel algorithms, mechanics of materials, advances in finite element methods, composite materials and structures, determinations of random motion and dynamic response, optimization theory, automotive tire modeling methods and contact problems, the damping and control of aircraft structures, and advanced structural applications. Specific topics covered include structural design expert systems, the evaluation of finite element system architectures, systolic arrays for finite element analyses, nonlinear finite element computations, hierarchical boundary elements, adaptive substructuring techniques in elastoplastic finite element analyses, automatic tracking of crack propagation, a theory of rate-dependent plasticity, the torsional stability of nonlinear eccentric structures, a computation method for fluid-structure interaction, the seismic analysis of three-dimensional soil-structure interaction, a stress analysis for a composite sandwich panel, toughness criterion identification for unidirectional composite laminates, the modeling of submerged cable dynamics, and damping synthesis for flexible spacecraft structures.

Forecast and analysis of the ratio of electric energy to terminal energy consumption for global energy internet

NASA Astrophysics Data System (ADS)

Wang, Wei; Zhong, Ming; Cheng, Ling; Jin, Lu; Shen, Si

2018-02-01

In the background of building global energy internet, it has both theoretical and realistic significance for forecasting and analysing the ratio of electric energy to terminal energy consumption. This paper firstly analysed the influencing factors of the ratio of electric energy to terminal energy and then used combination method to forecast and analyse the global proportion of electric energy. And then, construct the cointegration model for the proportion of electric energy by using influence factor such as electricity price index, GDP, economic structure, energy use efficiency and total population level. At last, this paper got prediction map of the proportion of electric energy by using the combination-forecasting model based on multiple linear regression method, trend analysis method, and variance-covariance method. This map describes the development trend of the proportion of electric energy in 2017-2050 and the proportion of electric energy in 2050 was analysed in detail using scenario analysis.

[Trends in hospital care].

PubMed

Vecina Neto, Gonzalo; Malik, Ana Maria

2007-01-01

This paper analyses trends in the delivery of hospital services in Brazil, considering the setting, the current situation and its challenges, examining what still remains to be done. The variables studied for the analysis of the setting are: demography, epidemiological profile, human resources, technology, medicalization, costs, review of the role of the citizen, legislation, equity, hospital-centricity and regionalization, care fractioning and bed availability. The Brazilian setting was studied through the supplementary healthcare model, financing and the healthcare area production chain. The observations of the current situation present external evaluation models, outsourcing, public-private relationships, de-hospitalization and financing. The analysis of the challenges examines the need for long range planning, the quest for new legal models for the 'business', the use of information and information systems, cost controls and the need for enhanced efficiency and compliance with legal directives, guaranteed universal access to full healthcare facilities, the inclusion of primary prevention in healthcare procedures, integrating the public and private sectors and engaging physicians in solving problems.

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.

Drivers of annual to decadal streamflow variability in the lower Colorado River Basin

NASA Astrophysics Data System (ADS)

Lambeth-Beagles, R. S.; Troch, P. A.

2010-12-01

The Colorado River is the main water supply to the southwest region. As demand reaches the limit of supply in the southwest it becomes increasingly important to understand the dynamics of streamflow in the Colorado River and in particular the tributaries to the lower Colorado River. Climate change may pose an additional threat to the already-scarce water supply in the southwest. Due to the narrowing margin for error, water managers are keen on extending their ability to predict streamflow volumes on a mid-range to decadal scale. Before a predictive streamflow model can be developed, an understanding of the physical drivers of annual to decadal streamflow variability in the lower Colorado River Basin is needed. This research addresses this need by applying multiple statistical methods to identify trends, patterns and relationships present in streamflow, precipitation and temperature over the past century in four contributing watersheds to the lower Colorado River. The four watersheds selected were the Paria, Little Colorado, Virgin/Muddy, and Bill Williams. Time series data over a common period from 1906-2007 for streamflow, precipitation and temperature were used for the initial analysis. Through statistical analysis the following questions were addressed: 1) are there observable trends and patterns in these variables during the past century and 2) if there are trends or patterns, how are they related to each other? The Mann-Kendall test was used to identify trends in the three variables. Assumptions regarding autocorrelation and persistence in the data were taken into consideration. Kendall’s tau-b test was used to establish association between any found trends in the data. Initial results suggest there are two primary processes occurring. First, statistical analysis reveals significant upward trends in temperatures and downward trends in streamflow. However, there appears to be no trend in precipitation data. These trends in streamflow and temperature speak to increasing evaporation and transpiration processes. Second, annual variability in streamflow is not statistically correlated with annual temperature variability but appears to be highly correlated with annual precipitation variability. This implies that on a year-to-year basis, changes in streamflow volumes are directly affected by precipitation and not temperature. Future development of a predictive streamflow model will need to take into consideration these two processes to obtain accurate results. In order to extend predictive skill to the multi-year scale relationships between precipitation, temperature and persistent climate indices such as the Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation and El Nino/Southern Oscillation will need to be examined.

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

NASA Technical Reports Server (NTRS)

Mueller, Thomas; Tucker, Compton J.; Dressler, Gunnar; Pinzon, Jorge E.; Leimgruber, Peter; Dubayah, Ralph O.; Hurtt, George C.; Boehning-Gaese, Katrin; Fagan, William F.

2014-01-01

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

Trends in pesticide concentrations in urban streams in the United States, 1992-2008

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.; Martin, Jeffrey D.; Gilliom, Robert J.

2010-01-01

Pesticide concentration trends in streams dominated by urban land use were assessed using data from 27 urban streams sampled as part of the U.S. Geological Survey National Water-Quality Assessment Program. The sites were divided into four regions, Northeast, South, Midwest, and West, to examine possible regional patterns. Three partially overlapping 9-year periods (1992-2000, 1996-2004, and 2000-2008) were examined for eight herbicides and one degradation product (simazine, prometon, atrazine, deethylatrazine, metolachlor, trifluralin, pendimethalin, tebuthiuron, and Dacthal), and five insecticides and two degradation products (chlorpyrifos, malathion, diazinon, fipronil, fipronil sulfide, desulfinylfipronil, and carbaryl). The data were analyzed for trends in concentration using a parametric regression model with seasonality, flow-related variability, and trend, called SEAWAVE-Q. The SEAWAVE-Q model also was used to generate estimated daily concentration percentiles for each analysis period to provide a summary of concentration magnitudes. For herbicides, the largest 90th percentiles of estimated concentrations for simazine were in the South, prometon at some sites in all of the regions, atrazine and deethylatrazine in the South and Midwest, metolachlor in the Midwest and a few sites in the South, pendimethalin at scattered sites in all of the regions, and tebuthiuron in the South and a few sites in the Midwest and West. For insecticides, the largest 90th percentiles of estimated concentrations for diazinon and carbaryl were distributed among various sites in all regions (especially during 1996-2004), and fipronil at isolated sites in all of the regions during 2000-2008. Trend analysis results for the herbicides indicated many significant trends, both upward and downward, with varying patterns depending on period, region, and herbicide. Overall, deethylatrazine showed the most consistent pattern of upward trends, especially in the Northeast (2000-2008), South (1996-2004 and 2000-2008), and Midwest (1996-2004 and 2000-2008). Other herbicides showed less consistent upward trends, including simazine in the South (1996-2004), prometon in the Midwest (2000-2008), and atrazine in the South (1996-2004). The most consistent downward trends were for simazine in the Northeast and Midwest (1996-2004), prometon in the Northeast and Midwest (1996-2004) and West (1996-2004 and 2000-2008), and tebuthiuron in the South (1996-2004 and 2000-2008) and West (2000-2008). Strong similarity existed between the trends for atrazine and deethylatrazine during 1996-2004. During 2000-2008, however, there were mixed upward and downward trends in atrazine and predominantly upward trends in deethylatrazine. Ten sites with a downward trend in atrazine were paired with an upward trend in deethylatrazine and for three of these sites (1 in the South and 2 in the Midwest) both opposing trends were significant. Opposing trends showing a decrease in atrazine and an increase in deethylatrazine may indicate that decreases in atrazine from surface runoff are being offset in some cases by increases in deethylatrazine from groundwater for the latter analysis period. Trend results for insecticides indicated widespread significant downward trends for chlorpyrifos (especially 1996-2004), diazinon (1996-2004 and 2000-2008), and malathion (especially 1996-2004); widespread significant upward trends for fipronil and its degradation products (2000-2008); and mostly nonsignificant trends for carbaryl (1996-2004 and 2000-2008). The downward trends for chlorpyrifos and diazinon were consistent with the regulatory phaseout of residential uses of these insecticides and the upward trends for fipronil and its degradation products were consistent with its introduction in 1996 and subsequent increasing use as a possible substitute for chlorpyrifos and diazinon. The downward trends in malathion may be caused by voluntary substitution of pyrethroids or fipronil for malathio

Proposed Method for Disaggregation of Secondary Data: The Model for External Reliance of Localities in the Coastal Management Zone (MERLIN-CMZ)

EPA Science Inventory

The Model for External Reliance of Localities In (MERLIN) Coastal Management Zones is a proposed solution to allow scaling of variables to smaller, nested geographies. Utilizing a Principal Components Analysis and data normalization techniques, smaller scale trends are linked to ...

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.

Trends in total column ozone measurements

NASA Technical Reports Server (NTRS)

Rowland, F. S.; Angell, J.; Attmannspacher, W.; Bloomfield, P.; Bojkov, R. D.; Harris, N.; Komhyr, W.; Mcfarland, M.; Mcpeters, R.; Stolarski, R. S.

1989-01-01

It is important to ensure the best available data are used in any determination of possible trends in total ozone in order to have the most accurate estimates of any trends and the associated uncertainties. Accordingly, the existing total ozone records were examined in considerable detail. Once the best data set has been produced, the statistical analysis must examine the data for any effects that might indicate changes in the behavior of global total ozone. The changes at any individual measuring station could be local in nature, and herein, particular attention was paid to the seasonal and latitudinal variations of total ozone, because two dimensional photochemical models indicate that any changes in total ozone would be most pronounced at high latitudes during the winter months. The conclusions derived from this detailed examination of available total ozone can be split into two categories, one concerning the quality and the other the statistical analysis of the total ozone record.

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.

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.

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

NASA Astrophysics Data System (ADS)

Yi, Yonghong; Kimball, John S.; Chen, Richard H.; Moghaddam, Mahta; Reichle, Rolf H.; Mishra, Umakant; Zona, Donatella; Oechel, Walter C.

2018-01-01

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ˜ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higher-permafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32±1.18 cm yr-1) and much larger increases (> 3 cm yr-1) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.

Analysis of spatial and temporal rainfall trends in Sicily during the 1921-2012 period

NASA Astrophysics Data System (ADS)

Liuzzo, Lorena; Bono, Enrico; Sammartano, Vincenzo; Freni, Gabriele

2016-10-01

Precipitation patterns worldwide are changing under the effects of global warming. The impacts of these changes could dramatically affect the hydrological cycle and, consequently, the availability of water resources. In order to improve the quality and reliability of forecasting models, it is important to analyse historical precipitation data to account for possible future changes. For these reasons, a large number of studies have recently been carried out with the aim of investigating the existence of statistically significant trends in precipitation at different spatial and temporal scales. In this paper, the existence of statistically significant trends in rainfall from observational datasets, which were measured by 245 rain gauges over Sicily (Italy) during the 1921-2012 period, was investigated. Annual, seasonal and monthly time series were examined using the Mann-Kendall non-parametric statistical test to detect statistically significant trends at local and regional scales, and their significance levels were assessed. Prior to the application of the Mann-Kendall test, the historical dataset was completed using a geostatistical spatial interpolation technique, the residual ordinary kriging, and then processed to remove the influence of serial correlation on the test results, applying the procedure of trend-free pre-whitening. Once the trends at each site were identified, the spatial patterns of the detected trends were examined using spatial interpolation techniques. Furthermore, focusing on the 30 years from 1981 to 2012, the trend analysis was repeated with the aim of detecting short-term trends or possible changes in the direction of the trends. Finally, the effect of climate change on the seasonal distribution of rainfall during the year was investigated by analysing the trend in the precipitation concentration index. The application of the Mann-Kendall test to the rainfall data provided evidence of a general decrease in precipitation in Sicily during the 1921-2012 period. Downward trends frequently occurred during the autumn and winter months. However, an increase in total annual precipitation was detected during the period from 1981 to 2012.

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

A reassessment of the Archean-Mesoproterozoic tectonic development of the southeastern Chhattisgarh Basin, Central India through detailed aeromagnetic analysis

NASA Astrophysics Data System (ADS)

Sridhar, M.; Ramesh Babu, V.; Markandeyulu, A.; Raju, B. V. S. N.; Chaturvedi, A. K.; Roy, M. K.

2017-08-01

We constrained the geological framework over polydeformed Paleoproterozoic Sonakhan Greenstone Belt and addressed the tectonic evolution of Singhora basin in the fringes of Bastar Craton, central India by utilizing aeromagnetic data interpretation, 2.5D forward modelling and 3D magnetic susceptibility inversions. The Sonakhan Greenstone Belt exposes volcano-sedimentary sequences of the Sonakhan Group within NNW-SSE to NW-SE trending linear belts surrounded by granite gneisses, which are unconformably overlain by sedimentary rocks of Chhattisgarh Basin. The orientations of aeromagnetic anomalies are coincident with geological trends and appear to correlate with lithology and geologic structure. Regional magnetic anomalies and lineaments reveal both NNW-SSE and NE-SW trends. Prominent E-W trending linear, high amplitude magnetic anomalies are interpreted as the Trans-Chhattisgarh Aeromagnetic Lineament (TCAL). NW-SE trending aeromagnetic signatures related to Sonakhan Greenstone Belt extends below the Singhora sedimentary rocks and forms the basement in the west. The analysis suggests that TCAL is a block fault with northern block down-thrown and affected the basement rocks comprising the Sonakhan Greenstone Belt and Samblapur Granitoids. The episode of faulting represented by the TCAL is pre-Singhora sedimentation and played a vital role in basin evolution. The basement configuration image generated by estimates of depth to magnetic basement suggests a complex pattern of NNE-SSW to NE-SW trending depressions separated by a linear N-S trending basement ridge. It is inferred from the 3D magnetic susceptibility inversion that the thickness of sediments is more towards the eastern basin margin and the N-S ridge is a manifestation of post sedimentary faulting. Results of 2.5D modelling of a WNW-ESE profile across the Singhora Basin combined with results from 3D inversion suggest suggests the basin subsidence was controlled by NE-SW trending regional faults in an active system. The basin geometry evolved by E-W block faulting overprinted by NE-SW trending pre- to syn-depositional normal faults generating NE-SW depression, which are affected by N-S trend post-sedimentary faulting. Though the present work relates the basin evolution with the initiation of rift basin, it warrants further work to establish the deformation within the basin pertaining to the proximal thrust and uplift along the craton fringe.

Study Variability of Seasonal Soil Moisture in Ensemble of CMIP5 Models Over South Asia During 1950-2005

NASA Astrophysics Data System (ADS)

Fahim, A. M.; Shen, R.; Yue, Z.; Di, W.; Mushtaq Shah, S.

2015-12-01

Moisture in the upper most layer of soil column from 14 different models under Coupled Model Intercomparison Project Phase-5 (CMIP5) project were analyzed for four seasons of the year. Aim of this study was to explore variability in soil moisture over south Asia using multi model ensemble and relationship between summer rainfall and soil moisture for spring and summer season. GLDAS (Global Land Data Assimilation System) dataset set was used for comparing CMIP5 ensemble mean soil moisture in different season. Ensemble mean represents soil moisture well in accordance with the geographical features; prominent arid regions are indicated profoundly. Empirical Orthogonal Function (EOF) analysis was applied to study the variability. First component of EOF explains 17%, 16%, 11% and 11% variability for spring, summer, autumn and winter season respectively. Analysis reveal increasing trend in soil moisture over most parts of Afghanistan, Central and north western parts of Pakistan, northern India and eastern to south eastern parts of China, in spring season. During summer, south western part of India exhibits highest negative trend while rest of the study area show minute trend (increasing or decreasing). In autumn, south west of India is under highest negative loadings. During winter season, north western parts of study area show decreasing trend. Summer rainfall has very week (negative or positive) spatial correlation, with spring soil moisture, while possess higher correlation with summer soil moisture. Our studies have significant contribution to understand complex nature of land - atmosphere interactions, as soil moisture prediction plays an important role in the cycle of sink and source of many air pollutants. Next level of research should be on filling the gaps between accurately measuring the soil moisture using satellite remote sensing and land surface modelling. Impact of soil moisture in tracking down different types of pollutant will also be studied.

ANALYSIS OF TRENDS IN LIFE EXPECTANCIES AND PER CAPITA GROSS DOMESTIC PRODUCT AS WELL AS PHARMACEUTICAL AND NON-PHARMACEUTICAL HEALTHCARE EXPENDITURES.

PubMed

Hermanowski, Tomasz; Bystrov, Victor; Staszewska-Bystrova, Anna; Szafraniec-Buryło, Sylwia I; Rabczenko, Daniel; Kolasa, Katarzyna; Orlewska, Ewa

2015-01-01

Life expectancy is a common measure of population health. Macro-perspective based on aggregated data makes it possible to approximate the impact of different levels of pharmaceutical expenditure on general population health status and is often used in cross-country comparisons. The aim of the study was to determine whether there are long-run relations between life expectancy, total healthcare expenditures, and pharmaceutical expenditures in OECD countries. Common trends in per capita gross domestic products (GDPs) (excluding healthcare expenditures), per capita healthcare expenditures (excluding pharmaceutical expenditures), per capita pharmaceutical expenditures, and life expectancies of women and men aged 60 and 65 were analyzed across OECD countries. Short-term effect of pharmaceutical expenditure onto life expectancy was also estimated by regressing the deviations of life expectancies from their long-term trends onto the deviations of pharmaceutical and non-pharmaceutical health expenditures, as well as GDP from their trends. The dataset was created on the basis of OECD Health Data for 34 countries and the years 1991-2010. Life expectancy variables were used as proxies for the health outcomes, whereas the pharmaceutical and healthcare expenditures represented drug and healthcare consumption, respectively. In general, both expenditures and life expectancies tended to increase in all of the analyzed countries; however, the growth rates differed across the countries. The analysis of common trends indicated the existence of common long-term trends in life expectancies and per capita GDP as well as pharmaceutical and non-pharmaceutical healthcare expenditures. However, there was no evidence that pharmaceutical expenditures provided additional information about the long-term trends in life expectancies beyond that contained in the GDP series. The analysis based on the deviations of variables from their long-term trends allowed concluding that pharmaceutical expenditures significantly influenced life expectancies in the short run. Non-pharmaceutical healthcare expenditures were found to be significant in one out of four models (for life expectancy of women aged 65), while GDPs were found to be insignificant in all four models. The results of the study indicate that there are common long-term trends in life expectancies and per capita GDP as well as pharmaceutical and non-pharmaceutical healthcare expenditures. The available data did not reveal any cause- effect relationship. Other factors, for which the systematic data were not available, may have determined the increase in life expectancy in OECD countries. Significant positive short-term relations between pharmaceutical expenditures and life expectancies in OECD countries were found. The significant short-term effect of pharmaceutical expenditures onto life expectancy means that an increase of pharmaceutical expenditures above long-term trends would lead to a temporary increase in life expectancy above its corresponding long-term trend. However, this effect would not persist as pharmaceutical expenditures and life expectancy would converge to levels determined by the long-term trends.

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.

Analysis of model output and science data in the Virtual Model Repository (VMR).

NASA Astrophysics Data System (ADS)

De Zeeuw, D.; Ridley, A. J.

2014-12-01

Big scientific data not only includes large repositories of data from scientific platforms like satelites and ground observation, but also the vast output of numerical models. The Virtual Model Repository (VMR) provides scientific analysis and visualization tools for a many numerical models of the Earth-Sun system. Individual runs can be analyzed in the VMR and compared to relevant data through relevant metadata, but larger collections of runs can also now be studied and statistics generated on the accuracy and tendancies of model output. The vast model repository at the CCMC with over 1000 simulations of the Earth's magnetosphere was used to look at overall trends in accuracy when compared to satelites such as GOES, Geotail, and Cluster. Methodology for this analysis as well as case studies will be presented.

Trends in radiology and experimental research.

PubMed

Sardanelli, Francesco

2017-01-01

European Radiology Experimental , the new journal launched by the European Society of Radiology, is placed in the context of three general and seven radiology-specific trends. After describing the impact of population aging, personalized/precision medicine, and information technology development, the article considers the following trends: the tension between subspecialties and the unity of the discipline; attention to patient safety; the challenge of reproducibility for quantitative imaging; standardized and structured reporting; search for higher levels of evidence in radiology (from diagnostic performance to patient outcome); the increasing relevance of interventional radiology; and continuous technological evolution. The new journal will publish not only studies on phantoms, cells, or animal models but also those describing development steps of imaging biomarkers or those exploring secondary end-points of large clinical trials. Moreover, consideration will be given to studies regarding: computer modelling and computer aided detection and diagnosis; contrast materials, tracers, and theranostics; advanced image analysis; optical, molecular, hybrid and fusion imaging; radiomics and radiogenomics; three-dimensional printing, information technology, image reconstruction and post-processing, big data analysis, teleradiology, clinical decision support systems; radiobiology; radioprotection; and physics in radiology. The journal aims to establish a forum for basic science, computer and information technology, radiology, and other medical subspecialties.

Analysis of Simulated Temporal Illumination at the Lunar PSRs

NASA Astrophysics Data System (ADS)

Thompson, T. J.; Mahanti, P.

2018-04-01

Illumination on the Moon is modeled temporally for permanently shadowed regions to lighting trends. Crater topography is used to generate viewfactor maps, which show which areas contribute most to scattered light into the primary shadows.

[Trend in inequalities in mortality due to external causes among the municipalities of Antioquia (Colombia)].

PubMed

Caicedo-Velásquez, Beatriz; Álvarez-Castaño, Luz Stella; Marí-Dell'Olmo, Marc; Borrell, Carme

2016-01-01

To analyse the trend in inequalities in mortality due to external causes among municipalities in Antioquia, department of Colombia, from 2000 to 2010, and its association with socioeconomic conditions. External causes included violent deaths, such as homicides, suicides and traffic accidents, among others. Ecological design of mortality trends, with the 125 municipalities of Antioquia as the unit of analysis. The age-adjusted smoothed standardized mortality ratio (SMR) was estimated for each of the municipalities by using an empirical Bayesian model. Differences in the SMR between the poorest and least poor municipalities were estimated by using a two-level hierarchical model (level-1: year, level-2: municipality). Mortality due to external causes showed a downward trend in the department in the period under review, although the situation was not similar in all municipalities. The findings showed that the risk of death from external causes significantly increased in poor and underdeveloped municipalities. Intervention is required through policies that take into account local differences in mortality due to external causes. Copyright © 2016 SESPAS. Published by Elsevier Espana. All rights reserved.

Analysis of Water and Energy Budgets and Trends Using the NLDAS Monthly Data Sets

NASA Technical Reports Server (NTRS)

Vollmer, Bruce E.; Rui, Hualan; Mocko, David M.; Teng, William L.; Lei, Guang-Dih

2012-01-01

The North American Land Data Assimilation System (NLDAS) is a collaborative project between NASA GSFC, NOAA, Princeton University, and the University of Washington. NLDAS has created surface meteorological forcing data sets using the best-available observations and reanalyses. The forcing data sets are used to drive four separate land-surface models (LSMs), Mosaic, Noah, VIC, and SAC, to produce data sets of soil moisture, snow, runoff, and surface fluxes. NLDAS hourly data, accessible from the NASA GES DISC Hydrology Data Holdings Portal, http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings, are widely used by various user communities in modeling, research, and applications, such as drought and flood monitoring, watershed and water quality management, and case studies of extreme events. More information is available at http://ldas.gsfc.nasa.gov/. To further facilitate analysis of water and energy budgets and trends, NLDAS monthly data sets have been recently released by NASA GES DISC.

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.;

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

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.

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

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.

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.

Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections.

PubMed

Ishida, K; Gorguner, M; Ercan, A; Trinh, T; Kavvas, M L

2017-08-15

The impacts of climate change on watershed-scale precipitation through the 21st century were investigated over eight study watersheds in Northern California based on dynamically downscaled CMIP5 future climate projections from three GCMs (CCSM4, HadGEM2-ES, and MIROC5) under the RCP4.5 and RCP8.5 future climate scenarios. After evaluating the modeling capability of the WRF model, the six future climate projections were dynamically downscaled by means of the WRF model over Northern California at 9km grid resolution and hourly temporal resolution during a 94-year period (2006-2100). The biases in the model simulations were corrected, and basin-average precipitation over the eight study watersheds was calculated from the dynamically downscaled precipitation data. Based on the dynamically downscaled basin-average precipitation, trends in annual depth and annual peaks of basin-average precipitation during the 21st century were analyzed over the eight study watersheds. The analyses in this study indicate that there may be differences between trends of annual depths and annual peaks of watershed-scale precipitation during the 21st century. Furthermore, trends in watershed-scale precipitation under future climate conditions may be different for different watersheds depending on their location and topography even if they are in the same region. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Impacts of land cover changes on climate trends in Jiangxi province China.

PubMed

Wang, Qi; Riemann, Dirk; Vogt, Steffen; Glaser, Rüdiger

2014-07-01

Land-use/land-cover (LULC) change is an important climatic force, and is also affected by climate change. In the present study, we aimed to assess the regional scale impact of LULC on climate change using Jiangxi Province, China, as a case study. To obtain reliable climate trends, we applied the standard normal homogeneity test (SNHT) to surface air temperature and precipitation data for the period 1951-1999. We also compared the temperature trends computed from Global Historical Climatology Network (GHCN) datasets and from our analysis. To examine the regional impacts of land surface types on surface air temperature and precipitation change integrating regional topography, we used the observation minus reanalysis (OMR) method. Precipitation series were found to be homogeneous. Comparison of GHCN and our analysis on adjusted temperatures indicated that the resulting climate trends varied slightly from dataset to dataset. OMR trends associated with surface vegetation types revealed a strong surface warming response to land barrenness and weak warming response to land greenness. A total of 81.1% of the surface warming over vegetation index areas (0-0.2) was attributed to surface vegetation type change and regional topography. The contribution of surface vegetation type change decreases as land cover greenness increases. The OMR precipitation trend has a weak dependence on surface vegetation type change. We suggest that LULC integrating regional topography should be considered as a force in regional climate modeling.

Business cycles and fertility dynamics in the United States: a vector autoregressive model.

PubMed

Mocan, N H

1990-01-01

"Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt

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.

Changing competition in health care marketing: a method for analysis and strategic planning.

PubMed

Ellis, B; Brockman, B K

1993-01-01

As the cost and importance of healthcare continue to increase, competition in the medical industry is taking new forms and becoming more intense. The driving trends behind this competition are analyzed in the framework of Porter's Five Forces of Competition Model. The authors then discuss how the widely accepted strategies of cost, differentiation, focus, and domestication can be utilized to counter the implications of these trends and how to capitalize on opportunities in medical practice in the 1990's.

Effects of Climate Change on Salmonella Infections

PubMed Central

Akil, Luma; Reddy, Remata S.

2014-01-01

Abstract Background: Climate change and global warming have been reported to increase spread of foodborne pathogens. To understand these effects on Salmonella infections, modeling approaches such as regression analysis and neural network (NN) were used. Methods: Monthly data for Salmonella outbreaks in Mississippi (MS), Tennessee (TN), and Alabama (AL) were analyzed from 2002 to 2011 using analysis of variance and time series analysis. Meteorological data were collected and the correlation with salmonellosis was examined using regression analysis and NN. Results: A seasonal trend in Salmonella infections was observed (p<0.001). Strong positive correlation was found between high temperature and Salmonella infections in MS and for the combined states (MS, TN, AL) models (R2=0.554; R2=0.415, respectively). NN models showed a strong effect of rise in temperature on the Salmonella outbreaks. In this study, an increase of 1°F was shown to result in four cases increase of Salmonella in MS. However, no correlation between monthly average precipitation rate and Salmonella infections was observed. Conclusion: There is consistent evidence that gastrointestinal infection with bacterial pathogens is positively correlated with ambient temperature, as warmer temperatures enable more rapid replication. Warming trends in the United States and specifically in the southern states may increase rates of Salmonella infections. PMID:25496072

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.

A multi-approach and multi-scale study on water quantity and quality changes in the Tapajós River basin, Amazon

NASA Astrophysics Data System (ADS)

Bezerra Nóbrega, Rodolfo Luiz; Lamparter, Gabriele; Hughes, Harold; Chenjerayi Guzha, Alphonce; Santos Silva Amorim, Ricardo; Gerold, Gerhard

2018-04-01

We analyzed changes in water quantity and quality at different spatial scales within the Tapajós River basin (Amazon) based on experimental fieldwork, hydrological modelling, and statistical time-trend analysis. At a small scale, we compared the river discharge (Q) and suspended-sediment concentrations (SSC) of two adjacent micro-catchments ( < 1 km2) with similar characteristics but contrasting land uses (forest vs. pasture) using empirical data from field measurements. At an intermediary scale, we simulated the hydrological responses of a sub-basin of the Tapajós (Jamanxim River basin, 37 400 km2), using a hydrological model (SWAT) and land-use change scenario in order to quantify the changes in the water balance components due to deforestation. At the Tapajós' River basin scale, we investigated trends in Q, sediments, hydrochemistry, and geochemistry in the river using available data from the HYBAM Observation Service. The results in the micro-catchments showed a higher runoff coefficient in the pasture (0.67) than in the forest catchment (0.28). At this scale, the SSC were also significantly greater during stormflows in the pasture than in the forest catchment. At the Jamanxim watershed scale, the hydrological modelling results showed a 2 % increase in Q and a 5 % reduction of baseflow contribution to total Q after a conversion of 22 % of forest to pasture. In the Tapajós River, however, trend analysis did not show any significant trend in discharge and sediment concentration. However, we found upward trends in dissolved organic carbon and NO3- over the last 20 years. Although the magnitude of anthropogenic impact has shown be scale-dependent, we were able to find changes in the Tapajós River basin in streamflow, sediment concentration, and water quality across all studied scales.

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.

Spatial analysis of relative humidity during ungauged periods in a mountainous region

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Kim, Yeonjoo

2017-08-01

Although atmospheric humidity influences environmental and agricultural conditions, thereby influencing plant growth, human health, and air pollution, efforts to develop spatial maps of atmospheric humidity using statistical approaches have thus far been limited. This study therefore aims to develop statistical approaches for inferring the spatial distribution of relative humidity (RH) for a mountainous island, for which data are not uniformly available across the region. A multiple regression analysis based on various mathematical models was used to identify the optimal model for estimating monthly RH by incorporating not only temperature but also location and elevation. Based on the regression analysis, we extended the monthly RH data from weather stations to cover the ungauged periods when no RH observations were available. Then, two different types of station-based data, the observational data and the data extended via the regression model, were used to form grid-based data with a resolution of 100 m. The grid-based data that used the extended station-based data captured the increasing RH trend along an elevation gradient. Furthermore, annual RH values averaged over the regions were examined. Decreasing temporal trends were found in most cases, with magnitudes varying based on the season and region.

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

Impacts of changes in precursor emissions from the San Francisco Bay Area on ozone in the North Central Coast and San Joaquin Valley air basins. Final report

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

Douglas, S.G.; Stoeckenius, T.E.; Austin, B.S.

1991-02-01

The study examined the effect of emissions reductions in the San Francisco Bay Area (SFBA) on ozone levels in the North Central Coast (NCC) and San Joaquin Valley (SJV) air basins. It included an emissions trends analysis for the SFBA, NCC, and SJV air basins; identification of possible transport days and an analysis of ozone trends in both the source and receptor basins on transport and no-transport days; and calculation of interbasin pollutant fluxes using air-quality modeling results. The emissions trends analysis indicated that the SFBA achieved large decreases in emissions of reactive organic gases (ROG) and oxides of nitrogenmore » (NOx) between 1979 and 1988. Despite the large decreases in emissions no significant ozone trends were observed on either transport or no-transport days. Ozone concentrations at the downwind monitors were higher on transport days. Results of the flux plane calculations indicate that elimination of SFBA emissions would significantly reduce ozone concentrations in the NCC and SJV during meteorological conditions conducive to transport and that the lower concentrations in the downwind air basins would be due primarily to a reduction in the amount of precursor pollutants that are transport from the SFBA to the receptor basins.« less

Dose-dependent protective effect of breast-feeding against breast cancer among ever-lactated women in Korea.

PubMed

Kim, Yeonju; Choi, Ji-Yeob; Lee, Kyoung-Mu; Park, Sue Kyung; Ahn, Sei-Hyun; Noh, Dong-Young; Hong, Yun-Chul; Kang, Daehee; Yoo, Keun-Young

2007-04-01

Lactation might have a crucial role in an extraordinary increase in breast cancer incidence in Korea, as the proportion of mothers who practised breast-feeding fell dramatically. This hospital-based case-control analysis has been carried out since 1997 to evaluate whether lactation is associated with breast cancer risk in Korean women. Among the eligible study participants, a total of 753 histologically confirmed incident cases and an equal number of controls were included in the analysis. The risk was estimated using unconditional logistic regression models. Family history, older at menopause, more full-term pregnancies increased the risk of breast cancer. Breast cancer risk decreased according to the total months of breast-feeding (P for trend=0.03). Average duration of breast-feeding of 11-12 months reduced risk of breast cancer by 54% compared with the duration of 1-4 months (odds ratio, 0.46; 95% confidence interval, 0.30-0.70). The decreasing risk trend according to average months of breast-feeding was also statistically significant (P for trend=0.02). Moreover, a reduced risk of breast cancer was apparent when analysis was restricted to the first breast-fed child (P for trend=0.006). This study confirms that lactation has an apparent dose-dependent protective effect against breast cancer in Korean women.

Forecasting selected specific age mortality rate of Malaysia by using Lee-Carter model

NASA Astrophysics Data System (ADS)

Shukri Kamaruddin, Halim; Ismail, Noriszura

2018-03-01

Observing mortality pattern and trend is an important subject for any country to maintain a good social-economy in the next projection years. The declining in mortality trend gives a good impression of what a government has done towards macro citizen in one nation. Selecting a particular mortality model can be a tricky based on the approached method adapting. Lee-Carter model is adapted because of its simplicity and reliability of the outcome results with approach of regression. Implementation of Lee-Carter in finding a fitted model and hence its projection has been used worldwide in most of mortality research in developed countries. This paper studies the mortality pattern of Malaysia in the past by using original model of Lee-Carter (1992) and hence its cross-sectional observation for a single age. The data is indexed by age of death and year of death from 1984 to 2012, in which are supplied by Department of Statistics Malaysia. The results are modelled by using RStudio and the keen analysis will focus on the trend and projection of mortality rate and age specific mortality rate in the future. This paper can be extended to different variants extensions of Lee-Carter or any stochastic mortality tool by using Malaysia mortality experience as a centre of the main issue.

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.

Spatial and temporal analysis of fatal off-piste and backcountry avalanche accidents in Austria with a comparison of results in Switzerland, France, Italy and the US

NASA Astrophysics Data System (ADS)

Pfeifer, Christian; Höller, Peter; Zeileis, Achim

2018-02-01

In this article we analyzed spatial and temporal patterns of fatal Austrian avalanche accidents caused by backcountry and off-piste skiers and snowboarders within the winter periods 1967/1968-2015/2016. The data were based on reports of the Austrian Board for Alpine Safety and reports of the information services of the federal states. Using the date and the location of the recorded avalanche accidents, we were able to carry out spatial and temporal analyses applying generalized additive models and Markov random-field models. As a result of the trend analysis we noticed an increasing trend of backcountry and off-piste avalanche fatalities within the winter periods 1967/1968-2015/2016 (although slightly decreasing in recent years), which is in contradiction to the widespread opinion in Austria that the number of fatalities is constant over time. Additionally, we compared Austrian results with results of Switzerland, France, Italy and the US based on data from the International Commission of Alpine Rescue (ICAR). As a result of the spatial analysis, we noticed two hot spots of avalanche fatalities (Arlberg-Silvretta and Sölden). Because of the increasing trend and the rather narrow regional distribution of the fatalities, initiatives aimed at preventing avalanche accidents were highly recommended.

[Spatiotemporal variation characteristics and related affecting factors of actual evapotranspiration in the Hun-Taizi River Basin, Northeast China].

PubMed

Feng, Xue; Cai, Yan-Cong; Guan, De-Xin; Jin, Chang-Jie; Wang, An-Zhi; Wu, Jia-Bing; Yuan, Feng-Hui

2014-10-01

Based on the meteorological and hydrological data from 1970 to 2006, the advection-aridity (AA) model with calibrated parameters was used to calculate evapotranspiration in the Hun-Taizi River Basin in Northeast China. The original parameter of the AA model was tuned according to the water balance method and then four subbasins were selected to validate. Spatiotemporal variation characteristics of evapotranspiration and related affecting factors were analyzed using the methods of linear trend analysis, moving average, kriging interpolation and sensitivity analysis. The results showed that the empirical parameter value of 0.75 of AA model was suitable for the Hun-Taizi River Basin with an error of 11.4%. In the Hun-Taizi River Basin, the average annual actual evapotranspiration was 347.4 mm, which had a slightly upward trend with a rate of 1.58 mm · (10 a(-1)), but did not change significantly. It also indicated that the annual actual evapotranspiration presented a single-peaked pattern and its peak value occurred in July; the evapotranspiration in summer was higher than in spring and autumn, and it was the smallest in winter. The annual average evapotranspiration showed a decreasing trend from the northwest to the southeast in the Hun-Taizi River Basin from 1970 to 2006 with minor differences. Net radiation was largely responsible for the change of actual evapotranspiration in the Hun-Taizi River Basin.

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.

The Behavior Analysis Follow Through Evaluation Strategy: A Multifaceted Approach.

ERIC Educational Resources Information Center

Green, Dan S.; And Others

The Behavior Analysis (BA) approach to Project Follow Through, a federally funded education intervention program, has reversed the trend of academic failure of poor children by improving the educational experience of poor children from 12 communities in the urban East, Midwest, rural South, and on Indian reservations in the West. The BA model is…

Heuristics for Understanding the Concepts of Interaction, Polynomial Trend, and the General Linear Model.

ERIC Educational Resources Information Center

Thompson, Bruce

The relationship between analysis of variance (ANOVA) methods and their analogs (analysis of covariance and multiple analyses of variance and covariance--collectively referred to as OVA methods) and the more general analytic case is explored. A small heuristic data set is used, with a hypothetical sample of 20 subjects, randomly assigned to five…

A Multilevel Analysis of Japanese Middle School Student and School Socioeconomic Status Influence on Mathematics Achievement

ERIC Educational Resources Information Center

Takashiro, Naomi

2017-01-01

The author examined the simultaneous influence of Japanese middle school student and school socioeconomic status (SES) on student math achievement with two-level multilevel analysis models by utilizing the Trends in International Mathematics and Science Study (TIMSS) Japan data sets. The theoretical framework used in this study was…

Automatic Promotion and Student Dropout: Evidence from Uganda, Using Propensity Score in Difference in Differences Model

ERIC Educational Resources Information Center

Okurut, Jeje Moses

2018-01-01

The impact of automatic promotion practice on students dropping out of Uganda's primary education was assessed using propensity score in difference in differences analysis technique. The analysis strategy was instrumental in addressing the selection bias problem, as well as biases arising from common trends over time, and permanent latent…

Trend Analysis of Betel Nut-associated Oral Cancer 
and Health Burden in China.

PubMed

Hu, Yan Jia; Chen, Jie; Zhong, Wai Sheng; Ling, Tian You; Jian, Xin Chun; Lu, Ruo Huang; Tang, Zhan Gui; Tao, Lin

To forecast the future trend of betel nut-associated oral cancer and the resulting burden on health based on historical oral cancer patient data in Hunan province, China. Oral cancer patient data in five hospitals in Changsha (the capital city of Hunan province) were collected for the past 12 years. Three methods were used to analyse the data; Microsoft Excel Forecast Sheet, Excel Trendline, and the Logistic growth model. A combination of these three methods was used to forecast the future trend of betel nut-associated oral cancer and the resulting burden on health. Betel nut-associated oral cancer cases have been increasing rapidly in the past 12  years in Changsha. As of 2016, betel nuts had caused 8,222 cases of oral cancer in Changsha and close to 25,000 cases in Hunan, resulting in about ¥5 billion in accumulated financial loss. The combined trend analysis predicts that by 2030, betel nuts will cause more than 100,000 cases of oral cancer in Changsha and more than 300,000 cases in Hunan, and more than ¥64 billion in accumulated financial loss in medical expenses. The trend analysis of oral cancer patient data predicts that the growing betel nut industry in Hunan province will cause a humanitarian catastrophe with massive loss of human life and national resources. To prevent this catastrophe, China should ban betel nuts and provide early oral cancer screening for betel nut consumers as soon as possible.

Partition of genetic trends by origin in Landrace and Large-White pigs.

PubMed

Škorput, D; Gorjanc, G; Kasap, A; Luković, Z

2015-10-01

The objective of this study was to analyse the effectiveness of genetic improvement via domestic selection and import for backfat thickness and time on test in a conventional pig breeding programme for Landrace (L) and Large-White (LW) breeds. Phenotype data was available for 25 553 L and 10 432 LW pigs born between 2002 and 2012 from four large-scale farms and 72 family farms. Pedigree information indicated whether each animal was born and registered within the domestic breeding programme or has been imported. This information was used for defining the genetic groups of unknown parents in a pedigree and the partitioning analysis. Breeding values were estimated using a Bayesian analysis of an animal model with and without genetic groups. Such analysis enabled full Bayesian inference of the genetic trends and their partitioning by the origin of germplasm. Estimates of genetic group indicated that imported germplasm was overall better than domestic and substantial changes in estimates of breeding values was observed when genetic group were fitted. The estimated genetic trends in L were favourable and significantly different from zero by the end of the analysed period. Overall, the genetic trends in LW were not different from zero. The relative contribution of imported germplasm to genetic trends was large, especially towards the end of analysed period with 78% and 67% in L and from 50% to 67% in LW. The analyses suggest that domestic breeding activities and sources of imported animals need to be re-evaluated, in particular in LW breed.

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.

Seasonality in twin birth rates, Denmark, 1936-84.

PubMed

Bonnelykke, B; Søgaard, J; Nielsen, J

1987-12-01

A study was made of seasonality in twin birth rate in Denmark between 1977 and 1984. We studied all twin births (N = 45,550) in all deliveries (N = 3,679,932) during that period. Statistical analysis using a simple harmonic sinusoidal model provided no evidence for seasonality. However, sequential polynomial analysis disclosed a significant fit to a fifth order polynomial curve with peaks in twin birth rates in May-June and December, along with troughs in February and September. A falling trend in twinning rate broke off in Denmark around 1970, and from 1970 to 1984 an increasing trend was found. The results are discussed in terms of possible environmental influences on twinning.

Trends in Nuclear Explosion Monitoring Research & Development - A Physics Perspective

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

Maceira, Monica; Blom, Philip Stephen; MacCarthy, Jonathan K.

This document entitled “Trends in Nuclear Explosion Monitoring Research and Development – A Physics Perspective” reviews the accessible literature, as it relates to nuclear explosion monitoring and the Comprehensive Nuclear-Test-Ban Treaty (CTBT, 1996), for four research areas: source physics (understanding signal generation), signal propagation (accounting for changes through physical media), sensors (recording the signals), and signal analysis (processing the signal). Over 40 trends are addressed, such as moving from 1D to 3D earth models, from pick-based seismic event processing to full waveform processing, and from separate treatment of mechanical waves in different media to combined analyses. Highlighted in the documentmore » for each trend are the value and benefit to the monitoring mission, key papers that advanced the science, and promising research and development for the future.« less

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.

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.

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.

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Earlier warning: a multi-indicator approach to monitoring trends in the illicit use of medicines.

PubMed

Mounteney, Jane; Haugland, Siren

2009-03-01

The availability of medicines on the illicit drug market is currently high on the international policy agenda, linked to adverse health consequences including addiction, drug related overdoses and injection related problems. Continuous surveillance of illicit use of medicines allows for earlier identification and reporting of emerging trends and increased possibilities for earlier intervention to prevent spread of use and drug related harm. This paper aims to identify data sources capable of monitoring the illicit use of medicines; present trend findings for Rohypnol and Subutex using a multi-indicator monitoring approach; and consider the relevance of such models for policy makers. Data collection and analysis were undertaken in Bergen, Norway, using the Bergen Earlier Warning System (BEWS), a multi-indicator drug monitoring system. Data were gathered at six monthly intervals from April 2002 to September 2006. Drug indicator data from seizures, treatment, pharmacy sales, helplines, key informants and media monitoring were triangulated and an aggregated differential was used to plot trends. Results for the 4-year period showed a decline in the illicit use of Rohypnol and an increase in the illicit use of Subutex. Multi-indicator surveillance models can play a strategic role in the earlier identification and reporting of emerging trends in illicit use of medicines.

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.;

Simulated trends of extreme climate indices for the Carpathian basin using outputs of different regional climate models

NASA Astrophysics Data System (ADS)

Pongracz, R.; Bartholy, J.; Szabo, P.; Pieczka, I.; Torma, C. S.

2009-04-01

Regional climatological effects of global warming may be recognized not only in shifts of mean temperature and precipitation, but in the frequency or intensity changes of different climate extremes. Several climate extreme indices are analyzed and compared for the Carpathian basin (located in Central/Eastern Europe) following the guidelines suggested by the joint WMO-CCl/CLIVAR Working Group on climate change detection. Our statistical trend analysis includes the evaluation of several extreme temperature and precipitation indices, e.g., the numbers of severe cold days, winter days, frost days, cold days, warm days, summer days, hot days, extremely hot days, cold nights, warm nights, the intra-annual extreme temperature range, the heat wave duration, the growing season length, the number of wet days (using several threshold values defining extremes), the maximum number of consecutive dry days, the highest 1-day precipitation amount, the greatest 5-day rainfall total, the annual fraction due to extreme precipitation events, etc. In order to evaluate the future trends (2071-2100) in the Carpathian basin, daily values of meteorological variables are obtained from the outputs of various regional climate model (RCM) experiments accomplished in the frame of the completed EU-project PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects). Horizontal resolution of the applied RCMs is 50 km. Both scenarios A2 and B2 are used to compare past and future trends of the extreme climate indices for the Carpathian basin. Furthermore, fine-resolution climate experiments of two additional RCMs adapted and run at the Department of Meteorology, Eotvos Lorand University are used to extend the trend analysis of climate extremes for the Carpathian basin. (1) Model PRECIS (run at 25 km horizontal resolution) was developed at the UK Met Office, Hadley Centre, and it uses the boundary conditions from the HadCM3 GCM. (2) Model RegCM3 (run at 10 km horizontal resolution) was developed by Giorgi et al. and it is available from the ICTP (International Centre for Theoretical Physics). Analysis of the simulated daily temperature datasets suggests that the detected regional warming is expected to continue in the 21st century. Cold temperature extremes are projected to decrease while warm extremes tend to increase significantly. Expected changes of annual precipitation indices are small, but generally consistent with the detected trends of the 20th century. Based on the simulations, extreme precipitation events are expected to become more intense and more frequent in winter, while a general decrease of extreme precipitation indices is expected in summer.

Atmospheric transport and wet deposition of ammonium in North Carolina

NASA Astrophysics Data System (ADS)

Walker, John T.; Aneja, Viney P.; Dickey, David A.

Wet deposition and transport analysis has been performed for ammonium (NH 4+) in North Carolina, USA. Multiple regression analysis is employed to model the temporal trend and seasonality in monthly volume-weighted mean NH 4+ concentrations in precipitation from 1983 to 1996 at six National Atmospheric Deposition Program/National Trends Network (NADP/NTN) sites. A significant ( p<0.01) increasing trend beginning in 1990, which corresponds to an annual concentration increase of approximately 9.5%, is detected at the rural Sampson County site (NC35), which is located within a densely populated network of swine and poultry operations. This trend is positively correlated with increasing ammonia (NH 3) emissions related to the vigorous growth of North Carolina's swine population since 1990, particularly in the state's Coastal Plain region. A source-receptor regression model, which utilizes weekly NH 4+ concentrations in precipitation in conjunction with boundary layer air mass back trajectories, is developed to statistically test for the influence of a particular NH 3 source region on NH 4+ concentrations at surrounding NADP/NTN sites for the years 1995-1996. NH 3 emissions from this source region, primarily evolving from swine and poultry operations, are found to increase NH 4+ concentration in precipitation at sites up to ≈80 km away. At the Scotland County (NC36) and Wake County (NC41) sites, mean NH 4+ concentrations show increases of at least 44% for weeks during which 25% or more back trajectories are influenced by this source region.

Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China - Part 2: The roles of anthropogenic emissions and climate variability

NASA Astrophysics Data System (ADS)

Xu, Wanyun; Xu, Xiaobin; Lin, Meiyun; Lin, Weili; Tarasick, David; Tang, Jie; Ma, Jianzhong; Zheng, Xiangdong

2018-01-01

Inter-annual variability and long-term trends in tropospheric ozone are both environmental and climate concerns. Ozone measured at Mt Waliguan Observatory (WLG, 3816 m a.s.l.) on the Tibetan Plateau over the period of 1994-2013 has increased significantly by 0.2-0.3 ppbv yr-1 during spring and autumn but shows a much smaller trend in winter and no significant trend in summer. Here we explore the factors driving the observed ozone changes at WLG using backward trajectory analysis, chemistry-climate model hindcast simulations (GFDL AM3), a trajectory-mapped ozonesonde data set, and several climate indices. A stratospheric ozone tracer implemented in GFDL AM3 indicates that stratosphere-to-troposphere transport (STT) can explain ˜ 60 % of the simulated springtime ozone increase at WLG, consistent with an increase in the NW air-mass frequency inferred from the trajectory analysis. Enhanced STT associated with the strengthening of the mid-latitude jet stream contributes to the observed high ozone anomalies at WLG during the springs of 1999 and 2012. During autumn, observations at WLG are more heavily influenced by polluted air masses originating from South East Asia than in the other seasons. Rising Asian anthropogenic emissions of ozone precursors are the key driver of increasing autumnal ozone observed at WLG, as supported by the GFDL AM3 model with time-varying emissions, which captures the observed ozone increase (0.26 ± 0.11 ppbv yr-1). AM3 simulates a greater ozone increase of 0.38 ± 0.11 ppbv yr-1 at WLG in autumn under conditions with strong transport from South East Asia and shows no significant ozone trend in autumn when anthropogenic emissions are held constant in time. During summer, WLG is mostly influenced by easterly air masses, but these trajectories do not extend to the polluted regions of eastern China and have decreased significantly over the last 2 decades, which likely explains why summertime ozone measured at WLG shows no significant trend despite ozone increases in eastern China. Analysis of the Trajectory-mapped Ozonesonde data set for the Stratosphere and Troposphere (TOST) and trajectory residence time reveals increases in direct ozone transport from the eastern sector during autumn, which adds to the autumnal ozone increase. We further examine the links of ozone variability at WLG to the quasi-biennial oscillation (QBO), the East Asian summer monsoon (EASM), and the sunspot cycle. Our results suggest that the 2-3-, 3-7-, and 11-year periodicities are linked to the QBO, EASM index, and sunspot cycle, respectively. A multivariate regression analysis is performed to quantify the relative contributions of various factors to surface ozone concentrations at WLG. Through an observational and modelling analysis, this study demonstrates the complex relationships between surface ozone at remote locations and its dynamical and chemical influencing factors.

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.

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

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

Impact of State Public Health Spending on Disease Incidence in the United States from 1980 to 2009.

PubMed

Verma, Reetu; Clark, Samantha; Leider, Jonathon; Bishai, David

2017-02-01

To understand the relationship between state-level spending by public health departments and the incidence of three vaccine preventable diseases (VPDs): mumps, pertussis, and rubella in the United States from 1980 to 2009. This study uses state-level public health spending data from The Census Bureau and annual mumps, pertussis, and rubella incidence counts from the University of Pittsburgh's project Tycho. Ordinary least squares (OLS), fixed effects, and random effects regression models were tested, with results indicating that a fixed effects model would be most appropriate model for this analysis. Model output suggests a statistically significant, negative relationship between public health spending and mumps and rubella incidence. Lagging outcome variables indicate that public health spending actually has the greatest impact on VPD incidence in subsequent years, rather than the year in which the spending occurred. Results were robust to models with lagged spending variables, national time trends, and state time trends, as well as models with and without Medicaid and hospital spending. Our analysis indicates that there is evidence of a significant, negative relationship between a state's public health spending and the incidence of two VPDs, mumps and rubella, in the United States. © Health Research and Educational Trust.

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.

Assessing floods and droughts in the Mékrou River basin (West Africa): a combined household survey and climatic trends analysis approach

NASA Astrophysics Data System (ADS)

Markantonis, Vasileios; Farinosi, Fabio; Dondeynaz, Celine; Ameztoy, Iban; Pastori, Marco; Marletta, Luca; Ali, Abdou; Carmona Moreno, Cesar

2018-05-01

The assessment of natural hazards such as floods and droughts is a complex issue that demands integrated approaches and high-quality data. Especially in African developing countries, where information is limited, the assessment of floods and droughts, though an overarching issue that influences economic and social development, is even more challenging. This paper presents an integrated approach to assessing crucial aspects of floods and droughts in the transboundary Mékrou River basin (a portion of the Niger River basin in West Africa), combining climatic trends analysis and the findings of a household survey. The multivariable trend analysis estimates, at the biophysical level, the climate variability and the occurrence of floods and droughts. These results are coupled with an analysis of household survey data that reveals the behaviour and opinions of local residents regarding the observed climate variability and occurrence of flood and drought events, household mitigation measures, and the impacts of floods and droughts. Based on survey data analysis, the paper provides a per-household cost estimation of floods and droughts that occurred over a 2-year period (2014-2015). Furthermore, two econometric models are set up to identify the factors that influence the costs of floods and droughts to impacted households.

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.

Role of the Tropical Pacific in recent Antarctic Sea-Ice Trends

NASA Astrophysics Data System (ADS)

Codron, F.; Bardet, D.; Allouache, C.; Gastineau, G.; Friedman, A. R.; Douville, H.; Voldoire, A.

2017-12-01

The recent (up to 2016) trends in Antarctic sea-ice cover - a global increase masking a dipole between the Ross and Bellingshausen-Weddel seas - are still not well understood, and not reproduced by CMIP5 coupled climate models. We here explore the potential role of atmospheric circulation changes around the Amundsen Sea, themselves possibly forced by tropical SSTs, an explanation that has been recently advanced. As a first check on this hypothesis, we compare the atmospheric circulation trends simulated by atmospheric GCMs coupled with an ocean or with imposed SSTs (AMIP experiment from CMIP5); the latter being in theory able to reproduce changes caused by natural SST variability. While coupled models simulate in aggregate trends that project on the SAM structure, strongest in summer, the AMIP simulations add in the winter season a pronounced Amundsen Sea Low signature (and a PNA signature in the northern hemisphere) both consistent with a Niña-like trend in the tropical Pacific. We then use a specific coupled GCM setup, in which surface wind anomalies over the tropical Pacific are strongly nudged towards the observed ones, including their interannual variability, but the model is free to evolve elsewhere. The two GCMs used then simulate a deepening trend in the Amundsen-Sea Low in winter, and are able to reproduce a dipole in sea-ice cover. Further analysis shows that the sea-ice dipole is partially forced by surface heat flux anomalies in early winter - the extent varying with the region and GCM used. The turbulent heat fluxes then act to damp the anomalies in late winter, which may however be maintained by ice-albedo feedbacks.

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

Malignant Lymphatic and Hematopoietic Neoplasms Mortality in Serbia, 1991–2010: A Joinpoint Regression Analysis

PubMed Central

Ilic, Milena; Ilic, Irena

2014-01-01

Background Limited data on mortality from malignant lymphatic and hematopoietic neoplasms have been published for Serbia. Methods The study covered population of Serbia during the 1991–2010 period. Mortality trends were assessed using the joinpoint regression analysis. Results Trend for overall death rates from malignant lymphoid and haematopoietic neoplasms significantly decreased: by −2.16% per year from 1991 through 1998, and then significantly increased by +2.20% per year for the 1998–2010 period. The growth during the entire period was on average +0.8% per year (95% CI 0.3 to 1.3). Mortality was higher among males than among females in all age groups. According to the comparability test, mortality trends from malignant lymphoid and haematopoietic neoplasms in men and women were parallel (final selected model failed to reject parallelism, P = 0.232). Among younger Serbian population (0–44 years old) in both sexes: trends significantly declined in males for the entire period, while in females 15–44 years of age mortality rates significantly declined only from 2003 onwards. Mortality trend significantly increased in elderly in both genders (by +1.7% in males and +1.5% in females in the 60–69 age group, and +3.8% in males and +3.6% in females in the 70+ age group). According to the comparability test, mortality trend for Hodgkin's lymphoma differed significantly from mortality trends for all other types of malignant lymphoid and haematopoietic neoplasms (P<0.05). Conclusion Unfavourable mortality trend in Serbia requires targeted intervention for risk factors control, early diagnosis and modern therapy. PMID:25333862

Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model

NASA Astrophysics Data System (ADS)

Rahman, Md. Rejaur; Lateh, Habibah

2017-04-01

In this paper, temperature and rainfall data series were analysed from 34 meteorological stations distributed throughout Bangladesh over a 40-year period (1971 to 2010) in order to evaluate the magnitude of these changes statistically and spatially. Linear regression, coefficient of variation, inverse distance weighted interpolation techniques and geographical information systems were performed to analyse the trends, variability and spatial patterns of temperature and rainfall. Autoregressive integrated moving average time series model was used to simulate the temperature and rainfall data. The results confirm a particularly strong and recent climate change in Bangladesh with a 0.20 °C per decade upward trend of mean temperature. The highest upward trend in minimum temperature (range of 0.80-2.4 °C) was observed in the northern, northwestern, northeastern, central and central southern parts while greatest warming in the maximum temperature (range of 1.20-2.48 °C) was found in the southern, southeastern and northeastern parts during 1971-2010. An upward trend of annual rainfall (+7.13 mm per year) and downward pre-monsoon (-0.75 mm per year) and post-monsoon rainfall (-0.55 mm per year) trends were observed during this period. Rainfall was erratic in pre-monsoon season and even more so during the post-monsoon season (variability of 44.84 and 85.25 % per year, respectively). The mean forecasted temperature exhibited an increase of 0.018 °C per year in 2011-2020, and if this trend continues, this would lead to approximately 1.0 °C warmer temperatures in Bangladesh by 2020, compared to that of 1971. A greater rise is projected for the mean minimum (0.20 °C) than the mean maximum (0.16 °C) temperature. Annual rainfall is projected to decline 153 mm from 2011 to 2020, and a drying condition will persist in the northwestern, western and southwestern parts of the country during the pre- and post-monsoonal seasons.

Estimating Water Levels with Google Earth Engine

NASA Astrophysics Data System (ADS)

Lucero, E.; Russo, T. A.; Zentner, M.; May, J.; Nguy-Robertson, A. L.

2016-12-01

Reservoirs serve multiple functions and are vital for storage, electricity generation, and flood control. For many areas, traditional ground-based reservoir measurements may not be available or data dissemination may be problematic. Consistent monitoring of reservoir levels in data-poor areas can be achieved through remote sensing, providing information to researchers and the international community. Estimates of trends and relative reservoir volume can be used to identify water supply vulnerability, anticipate low power generation, and predict flood risk. Image processing with automated cloud computing provides opportunities to study multiple geographic areas in near real-time. We demonstrate the prediction capability of a cloud environment for identifying water trends at reservoirs in the US, and then apply the method to data-poor areas in North Korea, Iran, Azerbaijan, Zambia, and India. The Google Earth Engine cloud platform hosts remote sensing data and can be used to automate reservoir level estimation with multispectral imagery. We combine automated cloud-based analysis from Landsat image classification to identify reservoir surface area trends and radar altimetry to identify reservoir level trends. The study estimates water level trends using three years of data from four domestic reservoirs to validate the remote sensing method, and five foreign reservoirs to demonstrate the method application. We report correlations between ground-based reservoir level measurements in the US and our remote sensing methods, and correlations between the cloud analysis and altimetry data for reservoirs in data-poor areas. The availability of regular satellite imagery and an automated, near real-time application method provides the necessary datasets for further temporal analysis, reservoir modeling, and flood forecasting. All statements of fact, analysis, or opinion are those of the author and do not reflect the official policy or position of the Department of Defense or any of its components or the U.S. Government

Indian Ocean warming during 1958-2004 simulated by a climate system model and its mechanism

NASA Astrophysics Data System (ADS)

Dong, Lu; Zhou, Tianjun; Wu, Bo

2014-01-01

The mechanism responsible for Indian Ocean Sea surface temperature (SST) basin-wide warming trend during 1958-2004 is studied based on both observational data analysis and numerical experiments with a climate system model FGOALS-gl. To quantitatively estimate the relative contributions of external forcing (anthropogenic and natural forcing) and internal variability, three sets of numerical experiments are conducted, viz. an all forcing run forced by both anthropogenic forcing (greenhouse gases and sulfate aerosols) and natural forcing (solar constant and volcanic aerosols), a natural forcing run driven by only natural forcing, and a pre-industrial control run. The model results are compared to the observations. The results show that the observed warming trend during 1958-2004 (0.5 K (47-year)-1) is largely attributed to the external forcing (more than 90 % of the total trend), while the residual is attributed to the internal variability. Model results indicate that the anthropogenic forcing accounts for approximately 98.8 % contribution of the external forcing trend. Heat budget analysis shows that the surface latent heat flux due to atmosphere and surface longwave radiation, which are mainly associated with anthropogenic forcing, are in favor of the basin-wide warming trend. The basin-wide warming is not spatially uniform, but with an equatorial IOD-like pattern in climate model. The atmospheric processes, oceanic processes and climatological latent heat flux together form an equatorial IOD-like warming pattern, and the oceanic process is the most important in forming the zonal dipole pattern. Both the anthropogenic forcing and natural forcing result in easterly wind anomalies over the equator, which reduce the wind speed, thereby lead to less evaporation and warmer SST in the equatorial western basin. Based on Bjerknes feedback, the easterly wind anomalies uplift the thermocline, which is unfavorable to SST warming in the eastern basin, and contribute to SST warming via deeper thermocline in the western basin. The easterly anomalies also drive westward anomalous equatorial currents, against the eastward climatology currents, which is in favor of the SST warming in the western basin via anomalous warm advection. Therefore, both the atmospheric and oceanic processes are in favor of the IOD-like warming pattern formation over the equator.

Analysis of 1970-1995 Trends in Tropospheric Ozone at Northern Hemisphere Midlatitudes with the GEOS-CHEM Model

NASA Technical Reports Server (NTRS)

Fusco, Andrew C.; Logan, Jennifer A.

2004-01-01

I ] The causes of trends in tropospheric ozone at Northern Hemisphere midlatitudes from 1970 to 1995 are investigated with the GEOS-CHEM model, a global three-dimensional model of the troposphere driven by assimilated meteorological observations from the Goddard Earth Observing System (GEOS). This model is used to investigate the sensitivity of tropospheric ozone with respect to (1) changes in the anthropogenic emission of nitrogen oxides and nonmethane hydrocarbons, (2) increases in methane concentrations, (3) variations in the stratospheric source of ozone, (4) changes in solar radiation resulting from stratospheric ozone depletion, and ( 5 ) increases in tropospheric temperatures. Model results indicate that local increases in NO, emissions have caused most of the increases seen in lower tropospheric ozone over Europe and Japan. Increases in methane are responsible for roughly one fifth of the anthropogenically induced increase in tropospheric ozone at northern midlatitudes. However, changes in ozone precursors do not adequately explain either the spatial differences in observed ozone trends across midlatitudes or the observed decreases in ozone over Canada throughout the troposphere. We argue that ozone depletion in the lowermost stratosphere is likely to have reduced the stratospheric source by as much as 30% from the early 1970s to the mid 1990s. Model simulations that account for such a reduction along with reported changes in anthropogenic emissions show steep declines of ozone in the upper troposphere and variable increases in the lower troposphere that are more consistent with observations. Differential temperature trends in summer between North America and Europe may account for at least some of the remaining spatial variation in tropospheric ozone trends. Increases in ultraviolet (UV) radiation due to stratospheric ozone depletion do not appear to significantly reduce tropospheric ozone, except at midlatitudes in the Southern Hemisphere following the breakup of the ozone hole.

Phase shifts in the stoichiometry of rifamycin B fermentation and correlation with the trends in the parameters measured online.

PubMed

Bapat, Prashant M; Das, Debasish; Dave, Nishant N; Wangikar, Pramod P

2006-12-15

Antibiotic fermentation processes are raw material cost intensive and the profitability is greatly dependent on the product yield per unit substrate consumed. In order to reduce costs, industrial processes use organic nitrogen substrates (ONS) such as corn steep liquor and yeast extract. Thus, although the stoichiometric analysis is the first logical step in process development, it is often difficult to achieve due to the ill-defined nature of the medium. Here, we present a black-box stoichiometric model for rifamycin B production via Amycolatopsis mediterranei S699 fermentation in complex multi-substrate medium. The stoichiometric coefficients have been experimentally evaluated for nine different media compositions. The ONS was quantified in terms of the amino acid content that it provides. Note that the black box stoichiometric model is an overall result of the metabolic reactions that occur during growth. Hence, the observed stoichiometric coefficients are liable to change during the batch cycle. To capture the shifts in stoichiometry, we carried out the stoichiometric analysis over short intervals of 8-16 h in a batch cycle of 100-200 h. An error analysis shows that there are no systematic errors in the measurements and that there are no unaccounted products in the process. The growth stoichiometry shows a shift from one substrate combination to another during the batch cycle. The shifts were observed to correlate well with the shifts in the trends of pH and exit carbon dioxide profiles. To exemplify, the ammonia uptake and nitrate uptake phases were marked by a decreasing pH trend and an increasing pH trend, respectively. Further, we find the product yield per unit carbon substrate to be greatly dependent on the nature of the nitrogen substrate. The analysis presented here can be readily applied to other fermentation systems that employ multi-substrate complex media.

Adjustment of Pesticide Concentrations for Temporal Changes in Analytical Recovery, 1992-2006

USGS Publications Warehouse

Martin, Jeffrey D.; Stone, Wesley W.; Wydoski, Duane S.; Sandstrom, Mark W.

2009-01-01

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 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 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 examines temporal changes in 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 to 2006 by gas chromatography/mass spectrometry. Recovery was measured by analysis of pesticide-spiked QC water samples. Temporal changes in pesticide recovery were investigated by calculating robust, locally weighted scatterplot smooths (lowess smooths) for the time series of pesticide recoveries in 5,132 laboratory reagent spikes; 1,234 stream-water matrix spikes; and 863 groundwater matrix spikes. A 10-percent smoothing window was selected to show broad, 6- to 12-month time scale changes in recovery for most of the 52 pesticides. Temporal patterns in recovery were similar (in phase) for laboratory reagent spikes and for matrix spikes for most pesticides. In-phase temporal changes among spike types support the hypothesis that temporal change in method performance is the primary cause of temporal change in recovery. Although temporal patterns of recovery were in phase for most pesticides, recovery in matrix spikes was greater than recovery in reagent spikes for nearly every pesticide. Models of recovery based on matrix spikes are deemed more appropriate for adjusting concentrations of pesticides measured in groundwater and stream-water samples than models based on laboratory reagent spikes because (1) matrix spikes are expected to more closely match the matrix of environmental water samples than are reagent spikes and (2) method performance is often matrix dependent, as was shown by higher recovery in matrix spikes for most of the pesticides. Models of recovery, based on 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.

Global trends in the awareness of sepsis: insights from search engine data between 2012 and 2017.

PubMed

Jabaley, Craig S; Blum, James M; Groff, Robert F; O'Reilly-Shah, Vikas N

2018-01-17

Sepsis is an established global health priority with high mortality that can be curtailed through early recognition and intervention; as such, efforts to raise awareness are potentially impactful and increasingly common. We sought to characterize trends in the awareness of sepsis by examining temporal, geographic, and other changes in search engine utilization for sepsis information-seeking online. Using time series analyses and mixed descriptive methods, we retrospectively analyzed publicly available global usage data reported by Google Trends (Google, Palo Alto, CA, USA) concerning web searches for the topic of sepsis between 24 June 2012 and 24 June 2017. Google Trends reports aggregated and de-identified usage data for its search products, including interest over time, interest by region, and details concerning the popularity of related queries where applicable. Outlying epochs of search activity were identified using autoregressive integrated moving average modeling with transfer functions. We then identified awareness campaigns and news media coverage that correlated with epochs of significantly heightened search activity. A second-order autoregressive model with transfer functions was specified following preliminary outlier analysis. Nineteen significant outlying epochs above the modeled baseline were identified in the final analysis that correlated with 14 awareness and news media events. Our model demonstrated that the baseline level of search activity increased in a nonlinear fashion. A recurrent cyclic increase in search volume beginning in 2012 was observed that correlates with World Sepsis Day. Numerous other awareness and media events were correlated with outlying epochs. The average worldwide search volume for sepsis was less than that of influenza, myocardial infarction, and stroke. Analyzing aggregate search engine utilization data has promise as a mechanism to measure the impact of awareness efforts. Heightened information-seeking about sepsis occurs in close proximity to awareness events and relevant news media coverage. Future work should focus on validating this approach in other contexts and comparing its results to traditional methods of awareness campaign evaluation.

Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

NASA Astrophysics Data System (ADS)

Dekker, Iris N.; Houweling, Sander; Aben, Ilse; Röckmann, Thomas; Krol, Maarten; Martínez-Alonso, Sara; Deeter, Merritt N.; Worden, Helen M.

2017-12-01

The growth of mega-cities leads to air quality problems directly affecting the citizens. Satellite measurements are becoming of higher quality and quantity, which leads to more accurate satellite retrievals of enhanced air pollutant concentrations over large cities. In this paper, we compare and discuss both an existing and a new method for estimating urban-scale trends in CO emissions using multi-year retrievals from the MOPITT satellite instrument. The first method is mainly based on satellite data, and has the advantage of fewer assumptions, but also comes with uncertainties and limitations as shown in this paper. To improve the reliability of urban-to-regional scale emission trend estimation, we simulate MOPITT retrievals using the Weather Research and Forecast model with chemistry core (WRF-Chem). The difference between model and retrieval is used to optimize CO emissions in WRF-Chem, focusing on the city of Madrid, Spain. This method has the advantage over the existing method in that it allows both a trend analysis of CO concentrations and a quantification of CO emissions. Our analysis confirms that MOPITT is capable of detecting CO enhancements over Madrid, although significant differences remain between the yearly averaged model output and satellite measurements (R2 = 0.75) over the city. After optimization, we find Madrid CO emissions to be lower by 48 % for 2002 and by 17 % for 2006 compared with the EdgarV4.2 emission inventory. The MOPITT-derived emission adjustments lead to better agreement with the European emission inventory TNO-MAC-III for both years. This suggests that the downward trend in CO emissions over Madrid is overestimated in EdgarV4.2 and more realistically represented in TNO-MACC-III. However, our satellite and model based emission estimates have large uncertainties, around 20 % for 2002 and 50 % for 2006.

Analyzing the contribution of climate change to long-term variations in sediment nitrogen sources for reservoirs/lakes.

PubMed

Xia, Xinghui; Wu, Qiong; Zhu, Baotong; Zhao, Pujun; Zhang, Shangwei; Yang, Lingyan

2015-08-01

We applied a mixing model based on stable isotopic δ(13)C, δ(15)N, and C:N ratios to estimate the contributions of multiple sources to sediment nitrogen. We also developed a conceptual model describing and analyzing the impacts of climate change on nitrogen enrichment. These two models were conducted in Miyun Reservoir to analyze the contribution of climate change to the variations in sediment nitrogen sources based on two (210)Pb and (137)Cs dated sediment cores. The results showed that during the past 50years, average contributions of soil and fertilizer, submerged macrophytes, N2-fixing phytoplankton, and non-N2-fixing phytoplankton were 40.7%, 40.3%, 11.8%, and 7.2%, respectively. In addition, total nitrogen (TN) contents in sediment showed significant increasing trends from 1960 to 2010, and sediment nitrogen of both submerged macrophytes and phytoplankton sources exhibited significant increasing trends during the past 50years. In contrast, soil and fertilizer sources showed a significant decreasing trend from 1990 to 2010. According to the changing trend of N2-fixing phytoplankton, changes of temperature and sunshine duration accounted for at least 43% of the trend in the sediment nitrogen enrichment over the past 50years. Regression analysis of the climatic factors on nitrogen sources showed that the contributions of precipitation, temperature, and sunshine duration to the variations in sediment nitrogen sources ranged from 18.5% to 60.3%. The study demonstrates that the mixing model provides a robust method for calculating the contribution of multiple nitrogen sources in sediment, and this study also suggests that N2-fixing phytoplankton could be regarded as an important response factor for assessing the impacts of climate change on nitrogen enrichment. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

20th-Century Climate Change over Africa: Seasonal Variation in Hydroclimate Trends and Sahara Desert Extent

NASA Astrophysics Data System (ADS)

Nigam, S.; Thomas, N. P.

2017-12-01

Twentieth-century trends in seasonal temperature and precipitation over the African continent are analyzed from observational data sets and historical climate simulations. Given the agricultural economy of the continent, a seasonal perspective is adopted as it is more pertinent than an annual-average one which can mask off-setting but agriculturally-sensitive seasonal hydroclimate variations. Examination of linear trends in seasonal surface air temperature (SAT) shows that heat stress has increased in several regions, including Sudan and Northern Africa where largest SAT trends occur in the warm season. Broadly speaking, the northern continent has warmed more than the southern one in all seasons. Precipitation trends are varied but notable declining trends are found in the countries along the Gulf of Guinea, especially in the source region of Niger river in West Africa, and in the Congo river basin. Rainfall over the African Great Lakes - one of the largest freshwater repositories - has however increased. We show that the Sahara Desert has expanded significantly over the 20th century - by 12-20% depending on the season. The desert expanded southward in summer, reflecting retreat of the northern edge of the Sahel rainfall belt; and to the north in winter, indicating potential impact of the widening of the Tropics. Specific mechanisms driving the expansion in each season are investigated. Finally, this observational analysis is used to evaluate the state-of-the-art climate models from a comparison of the 20th-century hydroclimate trends with those manifest in historical climate simulations. The evaluation shows that modeling regional hydroclimate change over the Africa continent remains challenging.

Sulfate Aerosol Control of Tropical Atlantic Climate over the Twentieth Century

NASA Technical Reports Server (NTRS)

Chang, C.-Y.; Chiang, J. C. H.; Wehner, M. F.; Friedman, A. R.; Ruedy, R.

2011-01-01

The tropical Atlantic interhemispheric gradient in sea surface temperature significantly influences the rainfall climate of the tropical Atlantic sector, including droughts over West Africa and Northeast Brazil. This gradient exhibits a secular trend from the beginning of the twentieth century until the 1980s, with stronger warming in the south relative to the north. This trend behavior is on top of a multi-decadal variation associated with the Atlantic multi-decadal oscillation. A similar long-term forced trend is found in a multimodel ensemble of forced twentieth-century climate simulations. Through examining the distribution of the trend slopes in the multimodel twentieth-century and preindustrial models, the authors conclude that the observed trend in the gradient is unlikely to arise purely from natural variations; this study suggests that at least half the observed trend is a forced response to twentieth-century climate forcings. Further analysis using twentieth-century single-forcing runs indicates that sulfate aerosol forcing is the predominant cause of the multimodel trend. The authors conclude that anthropogenic sulfate aerosol emissions, originating predominantly from the Northern Hemisphere, may have significantly altered the tropical Atlantic rainfall climate over the twentieth century

Trends in hydrometeorological conditions and stream water organic carbon in boreal forested catchments.

PubMed

Sarkkola, Sakari; Koivusalo, Harri; Laurén, Ari; Kortelainen, Pirkko; Mattsson, Tuija; Palviainen, Marjo; Piirainen, Sirpa; Starr, Mike; Finér, Leena

2009-12-15

Temporal trends in stream water total organic carbon (TOC) concentration and export were studied in 8 forested headwater catchments situated in eastern Finland. The Seasonal Kendall test was conducted to identify the trends and a mixed model regression analysis was used to describe how catchment characteristics and hydrometeorological variables (e.g. precipitation, air and stream water temperatures, and atmospheric deposition) related to the variation in the concentration and export of stream water TOC. The 8 catchments varied in size from 29 to 494 ha and in the proportion of peatland they contained, from 8 to 70%. Runoff and TOC concentration were monitored for 15-29 years (1979-2006). Trends and variation in TOC levels were analysed from annual and seasonal time series. Mean annual TOC concentration increased significantly in seven of the eight catchments. The trends were the strongest in spring and most apparent during the last decade of the study period. The slopes of the trends were generally smaller than the variation in TOC concentration between years and seasons and between catchments. The annual TOC export showed no clear trends and values were largely determined by the temporal variability in runoff. Annual runoff showed a decreasing trend in two of the eight catchments. Mean annual air and stream water temperatures showed increasing trends, most clearly seen in the summer and autumn series. According to our modeling results, stream water temperature, precipitation and peatland percentage were the most important variables explaining annual and most seasonal TOC concentrations. The atmospheric deposition of SO4, NH4, and NO3 decreased significantly over the study period, but no significant link with TOC concentration was found. Precipitation was the main hydrometeorological driver of the TOC export. We concluded that stream water TOC concentrations and exports are mainly driven by catchment characteristics and hydrometeorological factors rather than trends in atmospheric acid deposition.

Structural interpretation of aeromagnetic data for the Wadi El Natrun area, northwestern desert, Egypt

NASA Astrophysics Data System (ADS)

Ibraheem, Ismael M.; Elawadi, Eslam A.; El-Qady, Gad M.

2018-03-01

The Wadi El Natrun area in Egypt is located west of the Nile Delta on both sides of the Cairo-Alexandria desert road, between 30°00‧ and 30°40‧N latitude, and 29°40‧ and 30°40‧E longitude. The name refers to the NW-SE trending depression located in the area and containing lakes that produce natron salt. In spite of the area is promising for oil and gas exploration as well as agricultural projects, Geophysical studies carried out in the area is limited to the regional seismic surveys accomplished by oil companies. This study presents the interpretation of the airborne magnetic data to map the structure architecture and depth to the basement of the study area. This interpretation was facilitated by applying different data enhancement and processing techniques. These techniques included filters (regional-residual separation), derivatives and depth estimation using spectral analysis and Euler deconvolution. The results were refined using 2-D forward modeling along three profiles. Based on the depth estimation techniques, the estimated depth to the basement surface, ranges from 2.25 km to 5.43 km while results of the two-dimensional forward modeling show that the depth of the basement surface ranges from 2.2 km to 4.8 km. The dominant tectonic trends in the study area at deep levels are NW (Suez Trend), NNW, NE, and ENE (Syrian Arc System trend). The older ENE trend, which dominates the northwestern desert is overprinted in the study area by relatively recent NW and NE trends, whereas the tectonic trends at shallow levels are NW, ENE, NNE (Aqaba Trend), and NE. The predominant structure trend for both deep and shallow structures is the NW trend. The results of this study can be used to better understand deep-seated basement structures and to support decisions with regard to the development of agriculture, industrial areas, as well as oil and gas exploration in northern Egypt.

External validation of a forest inventory and analysis volume equation and comparisons with estimates from multiple stem-profile models

Treesearch

Christopher M. Oswalt; Adam M. Saunders

2009-01-01

Sound estimation procedures are desideratum for generating credible population estimates to evaluate the status and trends in resource conditions. As such, volume estimation is an integral component of the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) program's reporting. In effect, reliable volume estimation procedures are...

A hierarchical model for estimating change in American Woodcock populations

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.; Kendall, W.L.; Kelley, J.R.; Niven, D.K.

2008-01-01

The Singing-Ground Survey (SGS) is a primary source of information on population change for American woodcock (Scolopax minor). We analyzed the SGS using a hierarchical log-linear model and compared the estimates of change and annual indices of abundance to a route regression analysis of SGS data. We also grouped SGS routes into Bird Conservation Regions (BCRs) and estimated population change and annual indices using BCRs within states and provinces as strata. Based on the hierarchical model?based estimates, we concluded that woodcock populations were declining in North America between 1968 and 2006 (trend = -0.9%/yr, 95% credible interval: -1.2, -0.5). Singing-Ground Survey results are generally similar between analytical approaches, but the hierarchical model has several important advantages over the route regression. Hierarchical models better accommodate changes in survey efficiency over time and space by treating strata, years, and observers as random effects in the context of a log-linear model, providing trend estimates that are derived directly from the annual indices. We also conducted a hierarchical model analysis of woodcock data from the Christmas Bird Count and the North American Breeding Bird Survey. All surveys showed general consistency in patterns of population change, but the SGS had the shortest credible intervals. We suggest that population management and conservation planning for woodcock involving interpretation of the SGS use estimates provided by the hierarchical model.

Trends of Obesity in Iranian Adults from 1990s to late 2000s; a Systematic Review and Meta-analysis.

PubMed

Mirzazadeh, Ali; Salimzadeh, Hamideh; Arabi, Minoo; Navadeh, Soodabeh; Hajarizadeh, Behzad; Haghdoost, Ali Akbar

2013-07-01

Obesity is currently emerging as a global epidemic, affecting 10% of adultpopulation worldwide. The primary objective of the current systematic reviewis to describe the trend of overall prevalence of obesity in Iranian women andmenthrough a meta-analysis. We searched the medical literature published from 1990 to 2007 in Medline(PubMed), EMBASE database, and the Iranian digital library. All publishedreports of research projects, papers in relevant congresses, unpublished crudedata analysis, proceedings, books and dissertations were reviewed. Data fromeligible papers that fulfilled the qualification criteria entered meta-analysis(Random Model). Data from 209,166 individuals were analyzed. The overall prevalence ofobesity in adults was 18.5% (95%CI: 15.1-21.8), respectively. The prevalenceof obesity in men and women was 12.9% (95%CI: 10.9-14.9) and 26.2%(95%CI: 21.3-30.5), respectively. The trend of obesity was similar in both genders;women had almost a constantly higher risk of obesity than men duringthe recent two decades. Data from 209,166 individuals were analyzed. The overall prevalence ofobesity in adults was 18.5% (95%CI: 15.1-21.8), respectively. The prevalenceof obesity in men and women was 12.9% (95%CI: 10.9-14.9) and 26.2%(95%CI: 21.3-30.5), respectively. The trend of obesity was similar in both genders;women had almost a constantly higher risk of obesity than men duringthe recent two decades.

Contribution of H. pylori and smoking trends to US incidence of intestinal-type noncardia gastric adenocarcinoma: a microsimulation model.

PubMed

Yeh, Jennifer M; Hur, Chin; Schrag, Deb; Kuntz, Karen M; Ezzati, Majid; Stout, Natasha; Ward, Zachary; Goldie, Sue J

2013-01-01

Although gastric cancer has declined dramatically in the US, the disease remains the second leading cause of cancer mortality worldwide. A better understanding of reasons for the decline can provide important insights into effective preventive strategies. We sought to estimate the contribution of risk factor trends on past and future intestinal-type noncardia gastric adenocarcinoma (NCGA) incidence. We developed a population-based microsimulation model of intestinal-type NCGA and calibrated it to US epidemiologic data on precancerous lesions and cancer. The model explicitly incorporated the impact of Helicobacter pylori and smoking on disease natural history, for which birth cohort-specific trends were derived from the National Health and Nutrition Examination Survey (NHANES) and National Health Interview Survey (NHIS). Between 1978 and 2008, the model estimated that intestinal-type NCGA incidence declined 60% from 11.0 to 4.4 per 100,000 men, <3% discrepancy from national statistics. H. pylori and smoking trends combined accounted for 47% (range = 30%-58%) of the observed decline. With no tobacco control, incidence would have declined only 56%, suggesting that lower smoking initiation and higher cessation rates observed after the 1960s accelerated the relative decline in cancer incidence by 7% (range = 0%-21%). With continued risk factor trends, incidence is projected to decline an additional 47% between 2008 and 2040, the majority of which will be attributable to H. pylori and smoking (81%; range = 61%-100%). Limitations include assuming all other risk factors influenced gastric carcinogenesis as one factor and restricting the analysis to men. Trends in modifiable risk factors explain a significant proportion of the decline of intestinal-type NCGA incidence in the US, and are projected to continue. Although past tobacco control efforts have hastened the decline, full benefits will take decades to be realized, and further discouragement of smoking and reduction of H. pylori should be priorities for gastric cancer control efforts.

Glaucoma progression detection: agreement, sensitivity, and specificity of expert visual field evaluation, event analysis, and trend analysis.

PubMed

Antón, Alfonso; Pazos, Marta; Martín, Belén; Navero, José Manuel; Ayala, Miriam Eleonora; Castany, Marta; Martínez, Patricia; Bardavío, Javier

2013-01-01

To assess sensitivity, specificity, and agreement among automated event analysis, automated trend analysis, and expert evaluation to detect glaucoma progression. This was a prospective study that included 37 eyes with a follow-up of 36 months. All had glaucomatous disks and fields and performed reliable visual fields every 6 months. Each series of fields was assessed with 3 different methods: subjective assessment by 2 independent teams of glaucoma experts, glaucoma/guided progression analysis (GPA) event analysis, and GPA (visual field index-based) trend analysis. Kappa agreement coefficient between methods and sensitivity and specificity for each method using expert opinion as gold standard were calculated. The incidence of glaucoma progression was 16% to 18% in 3 years but only 3 cases showed progression with all 3 methods. Kappa agreement coefficient was high (k=0.82) between subjective expert assessment and GPA event analysis, and only moderate between these two and GPA trend analysis (k=0.57). Sensitivity and specificity for GPA event and GPA trend analysis were 71% and 96%, and 57% and 93%, respectively. The 3 methods detected similar numbers of progressing cases. The GPA event analysis and expert subjective assessment showed high agreement between them and moderate agreement with GPA trend analysis. In a period of 3 years, both methods of GPA analysis offered high specificity, event analysis showed 83% sensitivity, and trend analysis had a 66% sensitivity.

Analysis of 3D Modeling Software Usage Patterns for K-12 Students

ERIC Educational Resources Information Center

Wu, Yi-Chieh; Liao, Wen-Hung; Chi, Ming-Te; Li, Tsai-Yen

2016-01-01

In response to the recent trend in maker movement, teachers are learning 3D techniques actively and bringing 3D printing into the classroom to enhance variety and creativity in designing lectures. This study investigates the usage pattern of a 3D modeling software, Qmodel Creator, which is targeted at K-12 students. User logs containing…

It's in the Name: A Synthetic Inquiry of the Knowledge Is Power Program [KIPP

ERIC Educational Resources Information Center

Ellison, Scott

2012-01-01

The task of this article is to interrogate the Knowledge Is Power Program (KIPP) model to develop a more robust understanding of a prominent trend in the charter school movement and education policy more generally. To accomplish this task, this article details the findings of a synthetic analysis that examines the KIPP model as a Hegelian whole…

Effects of Whole-Body Motion Simulation on Flight Skill Development.

DTIC Science & Technology

1981-10-01

computation requirements, compared to the implementation allowing for a deviate internal model, provided further motivation for assuming a correct...We are left with two more likely explanations for the apparent trends: (1) subjects were motivated differently by the different task configurations...because of modeling constraints. The notion of task-related motivational differences are explored in Appendix E. Sensitivity analysis performed with

Exploring Temporal Frameworks for Constructing Longitudinal Instance-Specific Models from Clinical Data

ERIC Educational Resources Information Center

Watt, Emily

2012-01-01

The prevalence of the EMR in biomedical research is growing, the EMR being regarded as a source of contextually rich, longitudinal data for computation and statistical/trend analysis. However, models trained with data abstracted from the EMR often (1) do not capture all features needed to accurately predict the patient's future state and to…

Recent tree die-off has little effect on streamflow in contrast to expected increases from historical studies

NASA Astrophysics Data System (ADS)

Biederman, Joel A.; Somor, Andrew J.; Harpold, Adrian A.; Gutmann, Ethan D.; Breshears, David D.; Troch, Peter A.; Gochis, David J.; Scott, Russell L.; Meddens, Arjan J. H.; Brooks, Paul D.

2015-12-01

Recent bark beetle epidemics have caused regional-scale tree mortality in many snowmelt-dominated headwater catchments of western North America. Initial expectations of increased streamflow have not been supported by observations, and the basin-scale response of annual streamflow is largely unknown. Here we quantified annual streamflow responses during the decade following tree die-off in eight infested catchments in the Colorado River headwaters and one nearby control catchment. We employed three alternative empirical methods: (i) double-mass comparison between impacted and control catchments, (ii) runoff ratio comparison before and after die-off, and (iii) time-trend analysis using climate-driven linear models. In contrast to streamflow increases predicted by historical paired catchment studies and recent modeling, we did not detect streamflow changes in most basins following die-off, while one basin consistently showed decreased streamflow. The three analysis methods produced generally consistent results, with time-trend analysis showing precipitation was the strongest predictor of streamflow variability (R2 = 74-96%). Time-trend analysis revealed post-die-off streamflow decreased in three catchments by 11-29%, with no change in the other five catchments. Although counter to initial expectations, these results are consistent with increased transpiration by surviving vegetation and the growing body of literature documenting increased snow sublimation and evaporation from the subcanopy following die-off in water-limited, snow-dominated forests. The observations presented here challenge the widespread expectation that streamflow will increase following beetle-induced forest die-off and highlight the need to better understand the processes driving hydrologic response to forest disturbance.

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.

Significance of northeast-trending features in Canada Basin, Arctic Ocean

USGS Publications Warehouse

Hutchinson, Deborah; Jackson, H.R.; Houseknecht, David W.; Li, Q.; Shimeld, J.W.; Mosher, D.C.; Chian, D.; Saltus, Richard; Oakey, G.N.

2017-01-01

Synthesis of seismic velocity, potential field, and geological data from Canada Basin and its surrounding continental margins suggests that a northeast-trending structural fabric has influenced the origin, evolution, and current tectonics of the basin. This structural fabric has a crustal origin, based on the persistence of these trends in upward continuation of total magnetic intensity data and vertical derivative analysis of free-air gravity data. Three subparallel northeast-trending features are described. Northwind Escarpment, bounding the east side of the Chukchi Borderland, extends ∼600 km and separates continental crust of Northwind Ridge from high-velocity transitional crust in Canada Basin. A second, shorter northeast-trending zone extends ∼300 km in northern Canada Basin and separates inferred continental crust of Sever Spur from magmatically intruded crust of the High Arctic Large Igneous Province. A third northeast-trending feature, here called the Alaska-Prince Patrick magnetic lineament (APPL) is inferred from magnetic data and its larger regional geologic setting. Analysis of these three features suggests strike slip or transtensional deformation played a role in the opening of Canada Basin. These features can be explained by initial Jurassic-Early Cretaceous strike slip deformation (phase 1) followed in the Early Cretaceous (∼134 to ∼124 Ma) by rotation of Arctic Alaska with seafloor spreading orthogonal to the fossil spreading axis preserved in the central Canada Basin (phase 2). In this model, the Chukchi Borderland is part of Arctic Alaska.

Prostate cancer mortality in Serbia, 1991-2010: a joinpoint regression analysis.

PubMed

Ilic, Milena; Ilic, Irena

2016-06-01

The aim of this descriptive epidemiological study was to analyze the mortality trend of prostate cancer in Serbia (excluding the Kosovo and Metohia) from 1991 to 2010. The age-standardized prostate cancer mortality rates (per 100 000) were calculated by direct standardization, using the World Standard Population. Average annual percentage of change (AAPC) and the corresponding 95% confidence interval (CI) was computed for trend using the joinpoint regression analysis. Significantly increased trend in prostate cancer mortality was recorded in Serbia continuously from 1991 to 2010 (AAPC = +2.2, 95% CI = 1.6-2.9). Mortality rates for prostate cancer showed a significant upward trend in all men aged 50 and over: AAPC (95% CI) was +1.9% (0.1-3.8) in aged 50-59 years, +1.7% (0.9-2.6) in aged 60-69 years, +2.0% (1.2-2.9) in aged 70-79 years and +3.5% (2.4-4.6) in aged 80 years and over. According to comparability test, prostate cancer mortality trends in majority of age groups were parallel (final selected model failed to reject parallelism, P > 0.05). The increasing prostate cancer mortality trend implies the need for more effective measures of prevention, screening and early diagnosis, as well as prostate cancer treatment in Serbia. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Significance of Northeast-Trending Features in Canada Basin, Arctic Ocean

NASA Astrophysics Data System (ADS)

Hutchinson, D. R.; Jackson, H. R.; Houseknecht, D. W.; Li, Q.; Shimeld, J. W.; Mosher, D. C.; Chian, D.; Saltus, R. W.; Oakey, G. N.

2017-11-01

Synthesis of seismic velocity, potential field, and geological data from Canada Basin and its surrounding continental margins suggests that a northeast-trending structural fabric has influenced the origin, evolution, and current tectonics of the basin. This structural fabric has a crustal origin, based on the persistence of these trends in upward continuation of total magnetic intensity data and vertical derivative analysis of free-air gravity data. Three subparallel northeast-trending features are described. Northwind Escarpment, bounding the east side of the Chukchi Borderland, extends ˜600 km and separates continental crust of Northwind Ridge from high-velocity transitional crust in Canada Basin. A second, shorter northeast-trending zone extends ˜300 km in northern Canada Basin and separates inferred continental crust of Sever Spur from magmatically intruded crust of the High Arctic Large Igneous Province. A third northeast-trending feature, here called the Alaska-Prince Patrick magnetic lineament (APPL) is inferred from magnetic data and its larger regional geologic setting. Analysis of these three features suggests strike slip or transtensional deformation played a role in the opening of Canada Basin. These features can be explained by initial Jurassic-Early Cretaceous strike slip deformation (phase 1) followed in the Early Cretaceous (˜134 to ˜124 Ma) by rotation of Arctic Alaska with seafloor spreading orthogonal to the fossil spreading axis preserved in the central Canada Basin (phase 2). In this model, the Chukchi Borderland is part of Arctic Alaska.

Changes in Extratropical Storm Track Cloudiness 1983-2008: Observational Support for a Poleward Shift

NASA Technical Reports Server (NTRS)

Bender, Frida A-M.; Rananathan, V.; Tselioudis, G.

2012-01-01

Climate model simulations suggest that the extratropical storm tracks will shift poleward as a consequence of global warming. In this study the northern and southern hemisphere storm tracks over the Pacific and Atlantic ocean basins are studied using observational data, primarily from the International Satellite Cloud Climatology Project, ISCCP. Potential shifts in the storm tracks are examined using the observed cloud structures as proxies for cyclone activity. Different data analysis methods are employed, with the objective to address difficulties and uncertainties in using ISCCP data for regional trend analysis. In particular, three data filtering techniques are explored; excluding specific problematic regions from the analysis, regressing out a spurious viewing geometry effect, and excluding specific cloud types from the analysis. These adjustments all, to varying degree, moderate the cloud trends in the original data but leave the qualitative aspects of those trends largely unaffected. Therefore, our analysis suggests that ISCCP data can be used to interpret regional trends in cloudiness, provided that data and instrumental artefacts are recognized and accounted for. The variation in magnitude between trends emerging from application of different data correction methods, allows us to estimate possible ranges for the observational changes. It is found that the storm tracks, here represented by the extent of the midlatitude-centered band of maximum cloud cover over the studied ocean basins, experience a poleward shift as well as a narrowing over the 25 year period covered by ISCCP. The observed magnitudes of these effects are larger than in current generation climate models (CMIP3). The magnitude of the shift is particularly large in the northern hemisphere Atlantic. This is also the one of the four regions in which imperfect data primarily prevents us from drawing firm conclusions. The shifted path and reduced extent of the storm track cloudiness is accompanied by a regional reduction in total cloud cover. This decrease in cloudiness can primarily be ascribed to low level clouds, whereas the upper level cloud fraction actually increases, according to ISCCP. Independent satellite observations of radiative fluxes at the top of the atmosphere are consistent with the changes in total cloud cover. The shift in cloudiness is also supported by a shift in central position of the mid-troposphere meridional temperature gradient. We do not find support for aerosols playing a significant role in the satellite observed changes in cloudiness. The observed changes in storm track cloudiness can be related to local cloud-induced changes in radiative forcing, using ERBE and CERES radiative fluxes. The shortwave and the longwave components are found to act together, leading to a positive (warming) net radiative effect in response to the cloud changes in the storm track regions, indicative of positive cloud feedback. Among the CMIP3 models that simulate poleward shifts in all four storm track areas, all but one show decreasing cloud amount on a global mean scale in response to increased CO2 forcing, further consistent with positive cloud feedback. Models with low equilibrium climate sensitivity to a lesser extent than higher-sensitivity models simulate a poleward shift of the storm tracks.

A Energy Balance Analysis of the Climate Sensitivity to Variations in the Rate of Upwelling in the World Oceans.

NASA Astrophysics Data System (ADS)

Morantine, Michael Creighton

The climate system of the Earth has been under investigation for many years, and the "Green-House Effect" has introduced a sense of urgency into the effort. The globally averaged temperature of the Earth undergoes what is commonly referred to as natural fluctuations in the climate signal. One effort of climate modellers is to isolate the responses of particular climate forcings in order to better understand each effect. The use of energy balance climate models (EBM's) has been one of the major tools in this respect. Studies conducted on the response of the environment to the "Green-House Effect" predict a warming trend. After experiencing such a trend in the early 1900's, however, the globally averaged temperature of the Earth began to decrease in the 1940's and continued this trend for approximately 20 years before resuming its trend of increase. It will be shown that a reduction of ~10% in the upwelling rate in the oceans could produce a decrease in the globally averaged temperature sufficient to explain this departure from the expected trend. The analysis of paleoclimatic indicators has produced strong evidence that the orbital forcing with periods of approximately 21000, 41000 and 93000 years predicted by the Milankovitch Theory is the primary cause of the glacial cycles known to have occurred on the Earth. However, there is a dynamic interaction between the environment and the ice caps that is not completely understood at this time. The paleoclimatic indicators available for the last deglaciation are abundant and well preserved (relative to the evidence of previous glacial periods), and analysis of the evidence indicates that during the most recent deglaciation a pulsation in the polar front occurred on such a small time scale that Milankovitch forcing is ruled out as a possible cause. It will be shown that an abrupt shutdown in the deep-water formation process which feeds the upwelling in the oceans could produce an influence of appropriate magnitude and time-scale to be the source of the dynamic interaction responsible for this abrupt climatic event. The process employed in the dimension reduction used in the formulation of lower-order EBM's will be illustrated through the development of the equations, pointing out the inherent assumptions which must be made when developing one- and two-dimensional models as they are required. One -, two- and three-dimensional energy balance models will be analyzed and the results of climate sensitivity to upwelling variations will be presented graphically for each case.

Total ozone variations at Reykjavik since 1957

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

Bjarnason, G.G.; Rognvaldsson, O.E.; Sigfusson, T.I.

1993-12-01

Total ozone measurements using a Dobson spectrophotometer have been performed on a regular basis at Reykjavik (65 deg 08 min N, 21 deg 54 min W), Iceland, since 1957. The data set for the entire period of observations has been critically examined. Due to problems related to the calibration of the instrument the data record of ozone observations is divided into two periods in the following analysis (1957-1977 and 1977-1990). A statistical model was developed to fit the data and estimate long-term changes in total ozone. The model includes seasonal variations, solar cycle influences, quasi-biennial oscillation (QBO) effects, and linearmore » trends. Some variants of the model are applied to investigate to what extent the estimated trends depend on the form of the model. Trend analysis of the revised data reveals a statistically significant linear decrease of 0.11 +/- 0.07% per year in the annual total ozone amount during the earlier period and 0.30 +/- 0.11% during the latter. The annual total ozone decline since 1977 is caused by a 0.47 +/- 0.14% decrease per year during the summer with no significant change during the winter or fall. On an annual basis, ozone varies by 3.5 +/- 0.8% over a solar cycle and by 2.1 +/- 0.6% over a QBO for the whole observation period. The effect of the 11-year solar cycle is particularly strong in the data during the early months of the year and in the westerly phase of the QBO. The data also suggest a strong response of total ozone to major solar proton events.« less

Response of Antarctic ice shelf melt to SAM trend and possible feedbacks with the ice-dynamics

NASA Astrophysics Data System (ADS)

Donat-Magnin, Marion; Jourdain, Nicolas C.; Gallée, Hubert; Spence, Paul; Cornford, Stephen L.; Le Sommer, Julien; Durand, Gaël

2017-04-01

The observed positive trend in the Southern Annular Mode (SAM) may warm the Southern Ocean sub-surface through decreased Ekman downward pumping. Subsequent change in ice-shelves melt has been suggested to trigger glacier acceleration in West Antarctica. Here we use a regional ocean model configuration of the Amundsen Sea that includes interactive ice-shelf cavities. Our results show that the inclusion of ice-shelves changes the ocean response to the projected SAM trend, i.e. it typically inhibits a part of the SAM-induced subsurface warming. Heat budget analysis has been used to propose responsible mechanisms. Regarding Thwaites and Pine Island, sub ice-shelf melt increases above 400m by approximately 40% for Thwaites and 10% for Pine Island and decreases by up to 10% below in response to ocean temperature changes driven by the projected SAM trend. The melt sensitivity to poleward shifting winds is nonetheless small compared to the sensitivity to an ice-sheet instability, i.e. to a projected change in the shape of ice-shelf cavities. For instance, the sub ice-shelf melt are doubled near the grounding line of some glaciers in response to the largest grounding line retreat projected for 2100. Large increase in basal melt close to the grounding line could largely impact instability and glacier acceleration. Our work suggests the need for including ice shelves into ocean models, and to couple ocean models to ice-sheet models in climate projections.

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

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

A Global Look at Future Trends in the Renewable Energy Resource

NASA Astrophysics Data System (ADS)

Chen, S.; Freedman, J. M.; Kirk-Davidoff, D. B.; Brower, M.

2017-12-01

With the aggressive deployment of utility-scale and distributed generation of wind and solar energy systems, an accurate estimate of the uncertainty associated with future resource trends and plant performance is crucial in maintaining financial integrity in the renewable energy markets. With continuing concerns regarding climate change, the move towards energy resiliency, and the cost-competitiveness of renewables, a rapidly expanding fleet of utility-scale wind and solar power facilities and distributed generation of both resources is now being incorporated into the electric distribution grid. Although solar and wind account for about 3% of global power production, renewable energy is now and will continue to be the world's fastest-growing energy source. With deeper penetration of renewables, confidence in future power production output on a spectrum of temporal and spatial scales is crucial to grid stability for long-term planning and achieving national and international targets in the reduction of greenhouse gas emissions. Here, we use output from a diverse subset of Earth System Models (Climate Model Inter-comparison Project-Phase 5 members) to produce projected trends and uncertainties in regional and global seasonal and inter-annual wind and solar power production and respective capacity factors through the end of the 21st century. Our trends and uncertainty analysis focuses on the Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 scenarios. For wind and solar energy production estimates, we extract surface layer wind (extrapolated to hub height), irradiance, cloud fraction, and temperature (air temperature affects density [hence wind power production] and the efficiency of photovoltaic [PV] systems), output from the CMIP5 ensemble mean fields for the period 2020 - 2099 and an historical baseline for POR of 1986 - 2005 (compared with long-term observations and the ERA-Interim Reanalysis). Results include representative statistics such as the standard deviation (as determined from the slopes of the trend lines for individual CMIP5 members), means, medians (e.g. P50 values) and percent change, trends analysis on time series for each variable, and creation of global maps of trends (% change per year) and changes in capacity factors for both estimated solar and wind power production.

Anatomy of Human Interventions on the Alteration of Drought Risk over the Conterminous US

NASA Astrophysics Data System (ADS)

He, X.; Wada, Y.; Wanders, N.; Sheffield, J.

2017-12-01

Drought attribution focusing on anthropogenic climate change has received wide attentions. However, human interventions (HIs), such as irrigation, reservoir operation, and water use, are less well known. In this study, using the large-scale water resources model PCR-GLOBWB, we perform a suite of high-resolution ( 10 km) simulations over the conterminous US (CONUS) in order to disentangle the fingerprints of individual HI elements on changes of hydrological drought. The results show significant trend differences between scenarios with and without HIs in certain regions of the CONUS. HIs cause increased trends in drought severity for the High Plains, California and Mid-Atlantic region, whereas decreased trend emerges in the California Central Valley, lower Mississippi basin and Pacific Northwest. The mechanism of altered drought severity can be broken down into three individual parts, with irrigation increasing the trend in the High Plains and Central Valley, reservoir operation decreasing the trend in Western US and water use amplifying the trend in the urban areas. Besides the trend analysis, we show the relative contribution of water abstraction and return flows to explain how each HI contributes to enhancing or mitigating drought. Results demonstrate that return flows from agricultural irrigation increase recharge and therefore can alleviate hydrological drought (e.g., by 60-80% in Mississippi embayment). Further examination of the water sources indicates that in these drought alleviation hotspots, non-fossil groundwater dominates the total water abstraction. However, for the hotspots of drought intensification (e.g., southern High Plains), extensive irrigational pumping causes severe depletion of fossil groundwater, which reduces the interaction between baseflow and channel flow, and therefore reduces the total streamflow. Return level analysis is further applied to quantify how different types of HIs could alter the probability of occurrence of recent major drought events. This integrated hydrological modeling framework enables attribution of different HI impacts to probabilistic risk assessment, which in turn helps policy-makers better evaluate their long-term policy development for assessing potential water infrastructure investments in mitigating drought.

Alliance Building in the Information and Online Database Industry.

ERIC Educational Resources Information Center

Alexander, Johanna Olson

2001-01-01

Presents an analysis of information industry alliance formation using environmental scanning methods. Highlights include why libraries and academic institutions should be interested; a literature review; historical context; industry and market structures; commercial and academic models; trends; and implications for information providers,…

How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125

Marital age homogamy in China: A reversal of trend in the reform era?*

PubMed Central

Mu, Zheng; Xie, Yu

2014-01-01

This paper reports on a study of trends in marital age homogamy in China from 1960 to 2005 that uses data from the China 2005 1% Population Inter-census Survey. Instead of a consistent increase in age homogamy, as expected, results show an inverted U-shaped trend. One plausible explanation is that intensified economic pressure, rising consumerism, and a shrinking gender gap in education during the post-1990s reform era have acted to increase women's desire to marry men who are more economically established, and thus usually older, than less financially secure men. We argue that age hypergamy maintains status hypergamy, a deeply rooted norm for couples in China. An auxiliary analysis based on the human capital model for earnings supports this interpretation. A continued trend in age hypergamy implies a future “marriage squeeze” for men of low socioeconomic status. PMID:24468440

Changing incidence of psychotic disorders among the young in Zurich.

PubMed

Ajdacic-Gross, Vladeta; Lauber, Christoph; Warnke, Inge; Haker, Helene; Murray, Robin M; Rössler, Wulf

2007-09-01

There is controversy over whether the incidence rates of schizophrenia and psychotic disorders have changed in recent decades. To detect deviations from trends in incidence, we analysed admission data of patients with an ICD-8/9/10 diagnosis of psychotic disorders in the Canton Zurich / Switzerland, for the period 1977-2005. The data was derived from the central psychiatric register of the Canton Zurich. Ex-post forecasting with ARIMA (Autoregressive Integrated Moving Average) models was used to assess departures from existing trends. In addition, age-period-cohort analysis was applied to determine hidden birth cohort effects. First admission rates of patients with psychotic disorders were constant in men and showed a downward trend in women. However, the rates in the youngest age groups showed a strong increase in the second half of the 1990's. The trend reversal among the youngest age groups coincides with the increased use of cannabis among young Swiss in the 1990's.

Analysis and forecast of railway coal transportation volume based on BP neural network combined forecasting model

NASA Astrophysics Data System (ADS)

Xu, Yongbin; Xie, Haihong; Wu, Liuyi

2018-05-01

The share of coal transportation in the total railway freight volume is about 50%. As is widely acknowledged, coal industry is vulnerable to the economic situation and national policies. Coal transportation volume fluctuates significantly under the new economic normal. Grasp the overall development trend of railway coal transportation market, have important reference and guidance significance to the railway and coal industry decision-making. By analyzing the economic indicators and policy implications, this paper expounds the trend of the coal transportation volume, and further combines the economic indicators with the high correlation with the coal transportation volume with the traditional traffic prediction model to establish a combined forecasting model based on the back propagation neural network. The error of the prediction results is tested, which proves that the method has higher accuracy and has practical application.

Identification of rice supply chain risk to DKI Jakarta through Cipinang primary rice market

NASA Astrophysics Data System (ADS)

Sugiarto, D.; Ariwibowo, A.; Mardianto, I.; Surjasa, D.

2018-01-01

This paper identifies several sources of risks in DKI Jakarta rice supply chain that through Cipinang Primary Rice Market (CPRM). Secondary data from several sources were collected and analysed using pareto chart and time series analysis. Based on the pareto analysis, it was known that there was a change in the order of suppliers whereas in 2011, 80% of the supply came only from Cirebon, Karawang and Bandung (West Java Province). While in 2015 the main source of supply changed to Cirebon, Central Java and Karawang. Linear trend equation using decomposition model for Cirebon and Karawang showed trend of decreasing monthly supply while Central Java had a positive trend. Harvest area of wetland paddy in Cirebon and Karawang showed a negative trend in the last 6 years. The data also showed that West Java Province was the province with the largest rice crop area affected by plant organism attack and drought disaster in 2015. DKI Jakarta had several potential supply chain risks from rice supply, drought risk and pests risk where the province of West Java, which previously could become a major supplier began to require supply assistance from other provinces, especially Central Java.

Human Influence on Tropical Cyclone Intensity

NASA Technical Reports Server (NTRS)

Sobel, Adam H.; Camargo, Suzana J.; Hall, Timothy M.; Lee, Chia-Ying; Tippett, Michael K.; Wing, Allison A.

2016-01-01

Recent assessments agree that tropical cyclone intensity should increase as the climate warms. Less agreement exists on the detection of recent historical trends in tropical cyclone intensity.We interpret future and recent historical trends by using the theory of potential intensity, which predicts the maximum intensity achievable by a tropical cyclone in a given local environment. Although greenhouse gas-driven warming increases potential intensity, climate model simulations suggest that aerosol cooling has largely canceled that effect over the historical record. Large natural variability complicates analysis of trends, as do poleward shifts in the latitude of maximum intensity. In the absence of strong reductions in greenhouse gas emissions, future greenhouse gas forcing of potential intensity will increasingly dominate over aerosol forcing, leading to substantially larger increases in tropical cyclone intensities.

Relationship of gas hydrate concentration to porosity and reflection amplitude in a research well, Mackenzie Delta, Canada

USGS Publications Warehouse

Jin, Y.K.; Lee, M.W.; Collett, T.S.

2002-01-01

Well logs acquired at the Mallik 2L-38 gas hydrate research well. Mackenzie Delta, Canada, reveal a distinct trend showing that the resistivity of gas-hydrate-bearing sediments increases with increases in density porosities. This trend, opposite to the general trend of decrease in resistivity with porosity, implies that gas hydrates are more concentrated in the higher porosity. Using the Mallik 2L-38 well data, a proportional gas hydrate concentration (PGHC) model, which states that the gas hydrate concentration in the sediment's pore space is linearly proportional to porosity, is proposed for the general habitat of gas hydrate in sediments. Anomalous data (less than 6% of the total data) outside the dominant observed trend can be explained by local geological characteristics. The anomalous data analysis indicates that highly concentrated gas-hydrate-bearing layers would be expected where sediments have high proportions of gravel and coarse sand. Using the parameters in the PGHC model determined from resistivity-porosity logs, it is possible to qualitatively predict the degree of reflection amplitude variations in seismic profiles. Moderate-to-strong reflections are expected for the Mallik 2L-38 well. ?? 2002 Elsevier Science Ltd. All rights reserved.

Topic Time Series Analysis of Microblogs

DTIC Science & Technology

2014-10-01

network, may be closer to a media distribution site, where the media is user produced [14]. Analysis of the text content includes both general models as...is generated by Instagram . Topic 80, Distance: 143.2101 Top words: 1. rawr 2. ˆ0ˆ 3. kill 4. jurassic 5. dinosaur Analysis: This topic is quite...data, lack of reliable event information, hidden temporal trends, and the vastly diverse nature of content . In the present work, we examine spatio

Comparison of Earthquake Damage Patterns and Shallow-Depth Vs Structure Across the Napa Valley, Inferred From Multichannel Analysis of Surface Waves (MASW) and Multichannel Analysis of Love Waves (MALW) Modeling of Basin-Wide Seismic Profiles

NASA Astrophysics Data System (ADS)

Chan, J. H.; Catchings, R.; Strayer, L. M.; Goldman, M.; Criley, C.; Sickler, R. R.; Boatwright, J.

2017-12-01

We conducted an active-source seismic investigation across the Napa Valley (Napa Valley Seismic Investigation-16) in September of 2016 consisting of two basin-wide seismic profiles; one profile was 20 km long and N-S-trending (338°), and the other 15 km long and E-W-trending (80°) (see Catchings et al., 2017). Data from the NVSI-16 seismic investigation were recorded using a total of 666 vertical- and horizontal-component seismographs, spaced 100 m apart on both seismic profiles. Seismic sources were generated by a total of 36 buried explosions spaced 1 km apart. The two seismic profiles intersected in downtown Napa, where a large number of buildings were red-tagged by the City following the 24 August 2014 Mw 6.0 South Napa earthquake. From the recorded Rayleigh and Love waves, we developed 2-Dimensional S-wave velocity models to depths of about 0.5 km using the multichannel analysis of surface waves (MASW) method. Our MASW (Rayleigh) and MALW (Love) models show two prominent low-velocity (Vs = 350 to 1300 m/s) sub-basins that were also previously identified from gravity studies (Langenheim et al., 2010). These basins trend N-W and also coincide with the locations of more than 1500 red- and yellow-tagged buildings within the City of Napa that were tagged after the 2014 South Napa earthquake. The observed correlation between low-Vs, deep basins, and the red-and yellow-tagged buildings in Napa suggests similar large-scale seismic investigations can be performed. These correlations provide insights into the likely locations of significant structural damage resulting from future earthquakes that occur adjacent to or within sedimentary basins.

Trending Technologies for Indoor FM: Looking for "Geo" in Information

NASA Astrophysics Data System (ADS)

Gunduz, M.; Isikdag, U.; Basaraner, M.

2016-10-01

Today technological developments in the Architecture Engineering and Construction (AEC) industry provides opportunities to build huge and complex buildings and facilities. In order to operate these facilities and to meet the requirements of the occupants and also to manage energy, waste and to keep all facility services operational, several Facility Management (FM) solutions were developed. This paper starts by presenting a state of art review of research related to Indoor Facility Management Systems. Later, a textual analysis focused to identify the research trends in this field is presented in the paper. The result of the literature review and textual analysis indicates that current research in Indoor FM Systems is underestimating the role of Geoinformation, Geoinformation models and systems.

Trend analysis of the aerosol optical depth from fusion of MISR and MODIS retrievals over China

NASA Astrophysics Data System (ADS)

Guo, Jing; Gu, Xingfa; Yu, Tao; Cheng, Tianhai; Chen, Hao

2014-03-01

Atmospheric aerosol plays an important role in the climate change though direct and indirect processes. In order to evaluate the effects of aerosols on climate, it is necessary to have a research on their spatial and temporal distributions. Satellite aerosol remote sensing is a developing technology that may provide good temporal sampling and superior spatial coverage to study aerosols. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) have provided aerosol observations since 2000, with large coverage and high accuracy. However, due to the complex surface, cloud contamination, and aerosol models used in the retrieving process, the uncertainties still exist in current satellite aerosol products. There are several observed differences in comparing the MISR and MODIS AOD data with the AERONET AOD. Combing multiple sensors could reduce uncertainties and improve observational accuracy. The validation results reveal that a better agreement between fusion AOD and AERONET AOD. The results confirm that the fusion AOD values are more accurate than single sensor. We have researched the trend analysis of the aerosol properties over China based on nine-year (2002-2010) fusion data. Compared with trend analysis in Jingjintang and Yangtze River Delta, the accuracy has increased by 5% and 3%, respectively. It is obvious that the increasing trend of the AOD occurred in Yangtze River Delta, where human activities may be the main source of the increasing AOD.

Recurrent jellyfish blooms are a consequence of global oscillations.

PubMed

Condon, Robert H; Duarte, Carlos M; Pitt, Kylie A; Robinson, Kelly L; Lucas, Cathy H; Sutherland, Kelly R; Mianzan, Hermes W; Bogeberg, Molly; Purcell, Jennifer E; Decker, Mary Beth; Uye, Shin-ichi; Madin, Laurence P; Brodeur, Richard D; Haddock, Steven H D; Malej, Alenka; Parry, Gregory D; Eriksen, Elena; Quiñones, Javier; Acha, Marcelo; Harvey, Michel; Arthur, James M; Graham, William M

2013-01-15

A perceived recent increase in global jellyfish abundance has been portrayed as a symptom of degraded oceans. This perception is based primarily on a few case studies and anecdotal evidence, but a formal analysis of global temporal trends in jellyfish populations has been missing. Here, we analyze all available long-term datasets on changes in jellyfish abundance across multiple coastal stations, using linear and logistic mixed models and effect-size analysis to show that there is no robust evidence for a global increase in jellyfish. Although there has been a small linear increase in jellyfish since the 1970s, this trend was unsubstantiated by effect-size analysis that showed no difference in the proportion of increasing vs. decreasing jellyfish populations over all time periods examined. Rather, the strongest nonrandom trend indicated jellyfish populations undergo larger, worldwide oscillations with an approximate 20-y periodicity, including a rising phase during the 1990s that contributed to the perception of a global increase in jellyfish abundance. Sustained monitoring is required over the next decade to elucidate with statistical confidence whether the weak increasing linear trend in jellyfish after 1970 is an actual shift in the baseline or part of an oscillation. Irrespective of the nature of increase, given the potential damage posed by jellyfish blooms to fisheries, tourism, and other human industries, our findings foretell recurrent phases of rise and fall in jellyfish populations that society should be prepared to face.

Seasonal behavior of NO2 in the winter stratosphere - Inferred NO(x)

NASA Astrophysics Data System (ADS)

Zawodny, J. M.; Rusch, D. W.

1986-04-01

An analysis is performed of Solar Mesosphere Explorer (SME) data for the first 90 days of 1982, when a trend of increasing NO2 content in the stratosphere near the 10 mbar pressure level was detected. A photochemical-dynamical model is developed to account for the observed densities, which were also detected with ground-based instrumentation. The model calculations indicated that partitioning of the NO(x) family from N2O5 to NO2 was responsible for the trend. The new partitioning requires a lowering of the mixing ratio of NO(x), which was also observed. Finally, the SME data also confirmed that the enhanced NO2 concentrations were dependent on the solar zenith angle.

Tipping point analysis of ocean acoustic noise

NASA Astrophysics Data System (ADS)

Livina, Valerie N.; Brouwer, Albert; Harris, Peter; Wang, Lian; Sotirakopoulos, Kostas; Robinson, Stephen

2018-02-01

We apply tipping point analysis to a large record of ocean acoustic data to identify the main components of the acoustic dynamical system and study possible bifurcations and transitions of the system. The analysis is based on a statistical physics framework with stochastic modelling, where we represent the observed data as a composition of deterministic and stochastic components estimated from the data using time-series techniques. We analyse long-term and seasonal trends, system states and acoustic fluctuations to reconstruct a one-dimensional stochastic equation to approximate the acoustic dynamical system. We apply potential analysis to acoustic fluctuations and detect several changes in the system states in the past 14 years. These are most likely caused by climatic phenomena. We analyse trends in sound pressure level within different frequency bands and hypothesize a possible anthropogenic impact on the acoustic environment. The tipping point analysis framework provides insight into the structure of the acoustic data and helps identify its dynamic phenomena, correctly reproducing the probability distribution and scaling properties (power-law correlations) of the time series.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance.

PubMed

Poplová, Michaela; Sovka, Pavel; Cifra, Michal

2017-01-01

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance

PubMed Central

Poplová, Michaela; Sovka, Pavel

2017-01-01

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal. PMID:29216207

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

Exploiting Synoptic-Scale Climate Processes to Develop Nonstationary, Probabilistic Flood Hazard Projections

NASA Astrophysics Data System (ADS)

Spence, C. M.; Brown, C.; Doss-Gollin, J.

2016-12-01

Climate model projections are commonly used for water resources management and planning under nonstationarity, but they do not reliably reproduce intense short-term precipitation and are instead more skilled at broader spatial scales. To provide a credible estimate of flood trend that reflects climate uncertainty, we present a framework that exploits the connections between synoptic-scale oceanic and atmospheric patterns and local-scale flood-producing meteorological events to develop long-term flood hazard projections. We demonstrate the method for the Iowa River, where high flow episodes have been found to correlate with tropical moisture exports that are associated with a pressure dipole across the eastern continental United States We characterize the relationship between flooding on the Iowa River and this pressure dipole through a nonstationary Pareto-Poisson peaks-over-threshold probability distribution estimated based on the historic record. We then combine the results of a trend analysis of dipole index in the historic record with the results of a trend analysis of the dipole index as simulated by General Circulation Models (GCMs) under climate change conditions through a Bayesian framework. The resulting nonstationary posterior distribution of dipole index, combined with the dipole-conditioned peaks-over-threshold flood frequency model, connects local flood hazard to changes in large-scale atmospheric pressure and circulation patterns that are related to flooding in a process-driven framework. The Iowa River example demonstrates that the resulting nonstationary, probabilistic flood hazard projection may be used to inform risk-based flood adaptation decisions.

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.

Weight and the Future of Space Flight Hardware Cost Modeling

NASA Technical Reports Server (NTRS)

Prince, Frank A.

2003-01-01

Weight has been used as the primary input variable for cost estimating almost as long as there have been parametric cost models. While there are good reasons for using weight, serious limitations exist. These limitations have been addressed by multi-variable equations and trend analysis in models such as NAFCOM, PRICE, and SEER; however, these models have not be able to address the significant time lags that can occur between the development of similar space flight hardware systems. These time lags make the cost analyst's job difficult because insufficient data exists to perform trend analysis, and the current set of parametric models are not well suited to accommodating process improvements in space flight hardware design, development, build and test. As a result, people of good faith can have serious disagreement over the cost for new systems. To address these shortcomings, new cost modeling approaches are needed. The most promising approach is process based (sometimes called activity) costing. Developing process based models will require a detailed understanding of the functions required to produce space flight hardware combined with innovative approaches to estimating the necessary resources. Particularly challenging will be the lack of data at the process level. One method for developing a model is to combine notional algorithms with a discrete event simulation and model changes to the total cost as perturbations to the program are introduced. Despite these challenges, the potential benefits are such that efforts should be focused on developing process based cost models.

Modeling Alaska boreal forests with a controlled trend surface approach

Treesearch

Mo Zhou; Jingjing Liang

2012-01-01

An approach of Controlled Trend Surface was proposed to simultaneously take into consideration large-scale spatial trends and nonspatial effects. A geospatial model of the Alaska boreal forest was developed from 446 permanent sample plots, which addressed large-scale spatial trends in recruitment, diameter growth, and mortality. The model was tested on two sets of...

Using Generalized Additive Models to Analyze Single-Case Designs

ERIC Educational Resources Information Center

Shadish, William; Sullivan, Kristynn

2013-01-01

Many analyses for single-case designs (SCDs)--including nearly all the effect size indicators-- currently assume no trend in the data. Regression and multilevel models allow for trend, but usually test only linear trend and have no principled way of knowing if higher order trends should be represented in the model. This paper shows how Generalized…

Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method.

PubMed

Sharafi, Mehdi; Ghaem, Haleh; Tabatabaee, Hamid Reza; Faramarzi, Hossein

2017-01-01

To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model. Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016. Moreover, various SARIMA models were assessed and the best one was selected. Then, the model's fitness was evaluated based on normality of the residuals' distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The study results indicated that SARIMA model (4,1,4)(0,1,0) (12) in general and SARIMA model (4,1,4)(0,1,1) (12) in below and above 15 years age groups could appropriately predict the disease trend in the study area. Moreover, temperature with a three-month delay (lag3) increased the disease trend, rainfall with a four-month delay (lag4) decreased the disease trend, and rainfall with a nine-month delay (lag9) increased the disease trend. Based on the results, leishmaniasis follows a descending trend in the study area in case drought condition continues, SARIMA models can suitably measure the disease trend, and the disease follows a seasonal trend. Copyright © 2017 Hainan Medical University. Production and hosting by Elsevier B.V. All rights reserved.

Modelling of volatility in monetary transmission mechanism

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

Dobešová, Anna; Klepáč, Václav; Kolman, Pavel

2015-03-10

The aim of this paper is to compare different approaches to modeling of volatility in monetary transmission mechanism. For this purpose we built time-varying parameter VAR (TVP-VAR) model with stochastic volatility and VAR-DCC-GARCH model with conditional variance. The data from three European countries are included in the analysis: the Czech Republic, Germany and Slovakia. Results show that VAR-DCC-GARCH system captures higher volatility of observed variables but main trends and detected breaks are generally identical in both approaches.

Exploratory studies of the cruise performance of upper surface blown configurations: Program analysis and conclusions

NASA Technical Reports Server (NTRS)

Braden, J. A.; Hancock, J. P.; Hackett, J. E.; Lyman, V.

1979-01-01

The experimental data encompassing surface pressure measurements, and wake surveys at static and wind-on conditions are analyzed. Cruise performance trends reflecting nacelle geometric variations, and nozzle operating conditions are presented. Details of the modeling process are included.

Empirical Study on Total Factor Productive Energy Efficiency in Beijing-Tianjin-Hebei Region-Analysis based on Malmquist Index and Window Model

NASA Astrophysics Data System (ADS)

Xu, Qiang; Ding, Shuai; An, Jingwen

2017-12-01

This paper studies the energy efficiency of Beijing-Tianjin-Hebei region and to finds out the trend of energy efficiency in order to improve the economic development quality of Beijing-Tianjin-Hebei region. Based on Malmquist index and window analysis model, this paper estimates the total factor energy efficiency in Beijing-Tianjin-Hebei region empirically by using panel data in this region from 1991 to 2014, and provides the corresponding political recommendations. The empirical result shows that, the total factor energy efficiency in Beijing-Tianjin-Hebei region increased from 1991 to 2014, mainly relies on advances in energy technology or innovation, and obvious regional differences in energy efficiency to exist. Throughout the window period of 24 years, the regional differences of energy efficiency in Beijing-Tianjin-Hebei region shrank. There has been significant convergent trend in energy efficiency after 2000, mainly depends on the diffusion and spillover of energy technologies.

Sources and preparation of data for assessing trends in concentrations of pesticides in streams of the United States, 1992–2010

USGS Publications Warehouse

Martin, Jeffrey D.; Eberle, Michael; Nakagaki, Naomi

2011-01-01

This report updates a previously published water-quality dataset of 44 commonly used pesticides and 8 pesticide degradates suitable for a national assessment of trends in pesticide concentrations in streams of the United States. Water-quality samples collected from January 1992 through September 2010 at stream-water sites of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program and the National Stream Quality Accounting Network (NASQAN) were compiled, reviewed, selected, and prepared for trend analysis. The principal steps in data review for trend analysis were to (1) identify analytical schedule, (2) verify sample-level coding, (3) exclude inappropriate samples or results, (4) review pesticide detections per sample, (5) review high pesticide concentrations, and (6) review the spatial and temporal extent of NAWQA pesticide data and selection of analytical methods for trend analysis. The principal steps in data preparation for trend analysis were to (1) select stream-water sites for trend analysis, (2) round concentrations to a consistent level of precision for the concentration range, (3) identify routine reporting levels used to report nondetections unaffected by matrix interference, (4) reassign the concentration value for routine nondetections to the maximum value of the long-term method detection level (maxLT-MDL), (5) adjust concentrations to compensate for temporal changes in bias of recovery of the gas chromatography/mass spectrometry (GCMS) analytical method, and (6) identify samples considered inappropriate for trend analysis. Samples analyzed at the USGS National Water Quality Laboratory (NWQL) by the GCMS analytical method were the most extensive in time and space and, consequently, were selected for trend analysis. Stream-water sites with 3 or more water years of data with six or more samples per year were selected for pesticide trend analysis. The selection criteria described in the report produced a dataset of 21,988 pesticide samples at 212 stream-water sites. Only 21,144 pesticide samples, however, are considered appropriate for trend analysis.

Vertical structure of stratospheric water vapour trends derived from merged satellite data

PubMed Central

Hegglin, M. I.; Plummer, D. A.; Shepherd, T. G.; Scinocca, J. F.; Anderson, J.; Froidevaux, L.; Funke, B.; Hurst, D.; Rozanov, A.; Urban, J.; von Clarmann, T.; Walker, K. A.; Wang, H. J.; Tegtmeier, S.; Weigel, K.

2017-01-01

Stratospheric water vapour is a powerful greenhouse gas. The longest available record from balloon observations over Boulder, Colorado, USA shows increases in stratospheric water vapour concentrations that cannot be fully explained by observed changes in the main drivers, tropical tropopause temperatures and methane. Satellite observations could help resolve the issue, but constructing a reliable long-term data record from individual short satellite records is challenging. Here we present an approach to merge satellite data sets with the help of a chemistry-climate model nudged to observed meteorology. We use the models' water vapour as a transfer function between data sets that overcomes issues arising from instrument drift and short overlap periods. In the lower stratosphere, our water vapour record extends back to 1988 and water vapour concentrations largely follow tropical tropopause temperatures. Lower and mid-stratospheric long-term trends are negative, and the trends from Boulder are shown not to be globally representative. In the upper stratosphere, our record extends back to 1986 and shows positive long-term trends. The altitudinal differences in the trends are explained by methane oxidation together with a strengthened lower-stratospheric and a weakened upper-stratospheric circulation inferred by this analysis. Our results call into question previous estimates of surface radiative forcing based on presumed global long-term increases in water vapour concentrations in the lower stratosphere. PMID:29263751

Vertical structure of stratospheric water vapour trends derived from merged satellite data.

PubMed

Hegglin, M I; Plummer, D A; Shepherd, T G; Scinocca, J F; Anderson, J; Froidevaux, L; Funke, B; Hurst, D; Rozanov, A; Urban, J; von Clarmann, T; Walker, K A; Wang, H J; Tegtmeier, S; Weigel, K

2014-01-01

Stratospheric water vapour is a powerful greenhouse gas. The longest available record from balloon observations over Boulder, Colorado, USA shows increases in stratospheric water vapour concentrations that cannot be fully explained by observed changes in the main drivers, tropical tropopause temperatures and methane. Satellite observations could help resolve the issue, but constructing a reliable long-term data record from individual short satellite records is challenging. Here we present an approach to merge satellite data sets with the help of a chemistry-climate model nudged to observed meteorology. We use the models' water vapour as a transfer function between data sets that overcomes issues arising from instrument drift and short overlap periods. In the lower stratosphere, our water vapour record extends back to 1988 and water vapour concentrations largely follow tropical tropopause temperatures. Lower and mid-stratospheric long-term trends are negative, and the trends from Boulder are shown not to be globally representative. In the upper stratosphere, our record extends back to 1986 and shows positive long-term trends. The altitudinal differences in the trends are explained by methane oxidation together with a strengthened lower-stratospheric and a weakened upper-stratospheric circulation inferred by this analysis. Our results call into question previous estimates of surface radiative forcing based on presumed global long-term increases in water vapour concentrations in the lower stratosphere.

Trend analysis of tropical intraseasonal oscillations in the summer and winter during 1982-2009

NASA Astrophysics Data System (ADS)

Tao, Li; Zhao, Jiuwei; Li, Tim

2015-04-01

Based on the daily outgoing long-wave radiation (OLR) data of the National Oceanic and Atmospheric Administration (NOAA) from 1979 to 2012, we investigated the intensity changes of the 20-70-d boreal summer (June-September; JJAS) intra-seasonal oscillation (BSISO) and winter (December-February; DJF) intra-seasonal oscillation, also known as the Madden-Julian Oscillation (MJO). The results showed that the intensity of the BSISO has a significant intensifying trend during 1982-2009. On the other hand, little trend was found for boreal winter MJO during this period. The wavenumber-frequency analysis (Hayashi, 1982) was applied to separate ISO into westward propagation and eastward propagation parts. The significant intensified trend was observed over tropical Indian Ocean for the eastward-propagation BSISO. The weakened but not significant trend was observed over southern tropical Indian Ocean for the eastward-propagation MJO. To gain insight into the different ISO characteristics, the tendencies of sea surface temperature (SST) and the vertical shear of zonal wind were analyzed. The results showed that in both seasons from 1982 to 2009, the global SST trends were similar, and thus they could not be used to explain the BSISO upward trend. However, lower-tropospheric easterly shear in boreal summer over tropical Indian Ocean has a decreasing trend, while the easterly vertical shear over maritime continent was enhanced in winter. It is proposed that the reduced easterly vertical shear over tropical Indian Ocean favored the amplification of the eastward-propagating Kelvin wave, which led to the intensified eastward-propagating BSISO. The enhanced easterly vertical shear over maritime continent might be unfavorable to the amplification of the eastward-propagating Kelvin wave, but its impact was offset by the enhanced upward motion over maritime continent. As a result, there was little trend of the MJO in boreal winter. The hypothesis above was further verified by intermediate model results.

Satellite-Based Evidence for Shrub and Graminoid Tundra Expansion in Northern Quebec from 1986-2010

NASA Technical Reports Server (NTRS)

McManus, K. M.; Morton, D. C.; Masek, J. G.; Wang, D.; Sexton, J. O.; Nagol, J.; Ropars, P.; Boudreau, S.

2012-01-01

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air-photo studies have documented recent changes in high-latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24-year (1986-2010) Landsat time series in a latitudinal transect across the boreal forest-tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last forty years. Using a per-pixel (30 m) trend analysis, 30% of the observable (cloud-free) land area experienced a significant (p < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak-summer conditions. The average NDVI trend (0.007/yr) corresponds to a leaf-area index (LAI) increase of 0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer (MODIS). Across the entire transect, the area-averaged LAI increase was 0.2 during 1986-2010. A higher area-averaged LAI change (0.3) within the shrub-tundra portion of the transect represents a 20-60% relative increase in LAI during the last two decades. Our Landsat-based analysis subdivides the overall high-latitude greening trend into changes in peak-summer greenness by cover type. Different responses within and among shrub, graminoid, and tree-dominated cover types in this study indicate important fine-scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low-biomass vegetation types over multi-decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.

Real Estate Site Selection: An Application of Artificial Intelligence for Military Retail Facilities

DTIC Science & Technology

2006-09-01

Information and Spatial Analysis (SCGISA), University of Sheffield. Kotler , P. (1984). Marketing Management: Analysis, Planning, and Control...Spatial Distribution of Retail Sales. Journal of Real Estate Finance and Economics, Vol. 31 Iss. 1, 53. Lilien, G., & Kotler , P. (1983). Marketing ...commissaries). The current business model for military retail facilities may not be optimized based upon current trends market data. Optimizing

Non-parametric characterization of long-term rainfall time series

NASA Astrophysics Data System (ADS)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

Teacher Professional Development and Student Achievement: Analysis of Trends from Grade 8 TIMSS 2003, 2007 and 2011 Math Data for the Kingdom of Bahrain

ERIC Educational Resources Information Center

Ajjawi, Samah

2015-01-01

It is a common knowledge that student achievement is a product of multiple individual and environmental factors. The literature developed various models to organize and explain the relationship between some of these variables and student learning which translates into student achievement. Yet, no comprehensive model is able to capture all possible…

Evaluating the spatiotemporal variations of water budget across China over 1951-2006 using IBIS model

USGS Publications Warehouse

Zhu, Q.; Jiang, H.; Liu, J.; Wei, X.; Peng, C.; Fang, X.; Liu, S.; Zhou, G.; Yu, S.; Ju, W.

2010-01-01

The Integrated Biosphere Simulator is used to evaluate the spatial and temporal patterns of the crucial hydrological variables [run-off and actual evapotranspiration (AET)] of the water balance across China for the period 1951–2006 including a precipitation analysis. Results suggest three major findings. First, simulated run-off captured 85% of the spatial variability and 80% of the temporal variability for 85 hydrological gauges across China. The mean relative errors were within 20% for 66% of the studied stations and within 30% for 86% of the stations. The Nash–Sutcliffe coefficients indicated that the quantity pattern of run-off was also captured acceptably except for some watersheds in southwestern and northwestern China. The possible reasons for underestimation of run-off in the Tibetan plateau include underestimation of precipitation and uncertainties in other meteorological data due to complex topography, and simplified representations of the soil depth attribute and snow processes in the model. Second, simulated AET matched reasonably with estimated values calculated as the residual of precipitation and run-off for watersheds controlled by the hydrological gauges. Finally, trend analysis based on the Mann–Kendall method indicated that significant increasing and decreasing patterns in precipitation appeared in the northwest part of China and the Yellow River region, respectively. Significant increasing and decreasing trends in AET were detected in the Southwest region and the Yangtze River region, respectively. In addition, the Southwest region, northern China (including the Heilongjiang, Liaohe, and Haihe Basins), and the Yellow River Basin showed significant decreasing trends in run-off, and the Zhemin hydrological region showed a significant increasing trend.

Trends in atmospheric heavy metals abundances over the Russian part of EMEP region in 1990-2012

NASA Astrophysics Data System (ADS)

Gromov, Sergey A.; Konkova, Elizaveta S.

2016-04-01

The European part of Russia is covered by two atmospheric environment monitoring networks established in the 1970s-1980s to monitor and evaluate anthropogenic pollution of regional/background natural environment. These are EMEP - European Monitoring and Evaluation Program of transboundary atmospheric pollutant transmission (under the UN ECE Convention on Long-Range Transboundary Air Pollution) and IBMoN - Integrated Background Monitoring Network of environmental toxic pollution (prior to 1990 under the UNEP/GEMS supervision, mostly for East European countries). IGCE laboratories operate as analytical centers for both networks. Historically, IBMoN was partly implemented at EMEP sites to support this international program with additional (optional) data. IBMoN datasets were selected for analysis of atmospheric heavy metal trends in the Russian territory of EMEP region for the last twenty three years due to more intensive operation up to now [1, 2]. Atmospheric heavy metals are collected at the remote sites with the air samples of atmospheric aerosols deposited on Petryanov's cellulose acetate filters through high-volume pumping during 24 hours. To measure lead and cadmium content, filters are transferred into the solution to determine total amounts by the Atomic Absorption Spectroscopy (AAS) with flameless atomization. Precipitation samples (collected monthly with acidic preserving) are directly injected into the AAS detection module after filtering. The sampling procedure, special processing and analytical techniques allow us to measure concentrations at substantially low levels [3, 2]. In this study we investigate the long term trends of lead and cadmium in air and precipitation at two stations, viz. Astrakhan Biosphere Reserve (46°N, 49°E) and Danki (Oka-Terrace Biosphere Reserve, 54.9°N, 37.8°E). Following the EMEP general recommendations, the evaluation was done for two continuous periods covering 1990-2001 and 2002-2012, respectively. We apply the common methodology recommended by WMO/EMEP Task Force for trend evaluation, implemented in software developed and distributed by EMEP [4]. This methodology allows approximation of apparent trends using the superposition of the exponential (main) and residual components obtained using the ad hoc trend regression model. We further use so-called reduction parameters to investigate quantitatively the nature of trends: The total over the period (Rtot) and annual average (Rave), with the latter corresponding to increasing trend at negative values. Overall, temporal tendencies of airborne cadmium and lead demonstrate similar behaviour, however on top of different average concentration levels. For both species our analysis confirms the increase in air and precipitation abundances at the regional and remote sites over the European part of Russia for the period of 2002-2012. References: 1. Gromov S.A., and S.G. Paramonov, 2015. Current status and prospects for the development of integrated background monitoring of environmental pollution. Problems of Ecological Monitoring and Ecosystem Modelling, v. XXVI, N 1, p. 205-221. 2. Rovinsky F.Ya. (Ed.), 1989. Analytical review of environmental pollution with heavy metals in background areas of the CMEA member countries (1982-1989). Moscow, Gidrometeoizdat, 88 p. 3. Izrael Yu.A., and F.Ya. Rovinsky, 1991. Integrated background monitoring of environmental pollution in mid-latitude Eurasia. WMO Global Atmospheric Watch No 72, WMO/TD No. 434, 104 p. 4. MSC-East, 2015. Methodology of trend analysis of air quality data (http://www.msceast.org/documents/ Methodology_of_trend_analysis.pdf).

Investigating the Influence of Anthropogenic Forcing on Observed Mean and Extreme Sea Level Pressure Trends over the Mediterranean Region

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

Barkhordarian, Armineh

We investigate whether the observed mean sea level pressure (SLP) trends over the Mediterranean region in the period from 1975 to 2004 are significantly consistent with what 17 models projected as response of SLP to anthropogenic forcing (greenhouse gases and sulphate aerosols, GS). Obtained results indicate that the observed trends in mean SLP cannot be explained by natural (internal) variability. Externally forced changes are detectable in all seasons, except spring. The large-scale component (spatial mean) of the GS signal is detectable in all the 17 models in winter and in 12 of the 17 models in summer. However, the small-scalemore » component (spatial anomalies about the spatial mean) of GS signal is only detectable in winter within 11 of the 17 models. We also show that GS signal has a detectable influence on observed decreasing (increasing) tendency in the frequencies of extremely low (high) SLP days in winter and that these changes cannot be explained by internal climate variability. While the detection of GS forcing is robust in winter and summer, there are striking inconsistencies in autumn, where analysis points to the presence of an external forcing, which is not GS forcing.« less

Investigating the Influence of Anthropogenic Forcing on Observed Mean and Extreme Sea Level Pressure Trends over the Mediterranean Region

DOE PAGES

Barkhordarian, Armineh

2012-01-01

We investigate whether the observed mean sea level pressure (SLP) trends over the Mediterranean region in the period from 1975 to 2004 are significantly consistent with what 17 models projected as response of SLP to anthropogenic forcing (greenhouse gases and sulphate aerosols, GS). Obtained results indicate that the observed trends in mean SLP cannot be explained by natural (internal) variability. Externally forced changes are detectable in all seasons, except spring. The large-scale component (spatial mean) of the GS signal is detectable in all the 17 models in winter and in 12 of the 17 models in summer. However, the small-scalemore » component (spatial anomalies about the spatial mean) of GS signal is only detectable in winter within 11 of the 17 models. We also show that GS signal has a detectable influence on observed decreasing (increasing) tendency in the frequencies of extremely low (high) SLP days in winter and that these changes cannot be explained by internal climate variability. While the detection of GS forcing is robust in winter and summer, there are striking inconsistencies in autumn, where analysis points to the presence of an external forcing, which is not GS forcing.« less

Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown

NASA Astrophysics Data System (ADS)

Smith, Doug M.; Booth, Ben B. B.; Dunstone, Nick J.; Eade, Rosie; Hermanson, Leon; Jones, Gareth S.; Scaife, Adam A.; Sheen, Katy L.; Thompson, Vikki

2016-10-01

The rate of global mean surface temperature (GMST) warming has slowed this century despite the increasing concentrations of greenhouse gases. Climate model experiments show that this slowdown was largely driven by a negative phase of the Pacific Decadal Oscillation (PDO), with a smaller external contribution from solar variability, and volcanic and anthropogenic aerosols. The prevailing view is that this negative PDO occurred through internal variability. However, here we show that coupled models from the Fifth Coupled Model Intercomparison Project robustly simulate a negative PDO in response to anthropogenic aerosols implying a potentially important role for external human influences. The recovery from the eruption of Mount Pinatubo in 1991 also contributed to the slowdown in GMST trends. Our results suggest that a slowdown in GMST trends could have been predicted in advance, and that future reduction of anthropogenic aerosol emissions, particularly from China, would promote a positive PDO and increased GMST trends over the coming years. Furthermore, the overestimation of the magnitude of recent warming by models is substantially reduced by using detection and attribution analysis to rescale their response to external factors, especially cooling following volcanic eruptions. Improved understanding of external influences on climate is therefore crucial to constrain near-term climate predictions.

Investigating the influence of rice crop irrigation on streamflow in the Ibicui river basin using trend analyses

NASA Astrophysics Data System (ADS)

Paiva, E. M. C. D.; Heatwole, C. H.; Paiva, J. B. D.; Paiva, R. C. D.

2012-04-01

Problems of water shortages and floods are often attributed to the damming of rivers, agriculture, mining, deforestation, forestry, urbanization, and other practices. In the south of Brazil, most river basins experience water deficit problems related to the indiscriminate use of water to irrigate rice. We present a statistical analysis of streamflow data of the Ibicuí Basin, to verify if there are significant trends in water availability related to the withdrawal of water for rice crop irrigation. The Ibicuí basin, located in the southwest of the state of Rio Grande do Sul, Brazil, has ~50,000 km2 drainage area. It is part of the Uruguai basin, and is characteristic of the Pampa biome. This analysis is based on twelve stream gauge stations with data covering the period of rice cultivation between 1970 and 2011. Records of daily flow data were standardized by subtracting the long-term monthly mean and then dividing by the long-term monthly standard deviation. Then for each month we calculated the flow for 50%, 60%, 70%, 80%, 90%, 95% and 99% duration. Trends in these series were assessed using Mann Kendall test. The results showed that there are trends of increasing discharge for nine of the twelve analyzed stations, and in six of those nine stations, the increasing trend was statistically significant. Just three stations presented negative trends. The result for six stations that streamflow is increasing is surprising, because historically it has been assumed that there are deficits of water due to major withdrawals for rice irrigation during the growing season of the crop. River discharges are typically low in this withdrawal period of November to February, although precipitation is similar for all months of the year. Also, some studies using physical models have confirmed the impact of irrigation withdrawals on flow. But the decrease in flow due to irrigation withdrawals was not supported with this statistical analysis. However, analyzing the trend values for several time flow durations, it was observed that there was a reduction of the trends with the duration. Only two stations presented increasing trends with duration. Also, it could be verified that in a river with sequential stations, the trends showed that the Mann Kendall Zs decreased with irrigated area. For verifying if it is possible to see the difference with water withdrawals for irrigation of rice, the station that showed the highest increasing trend was chosen for simulating an increasing water withdrawal on up to 5% of the area in 2011. In this analysis, despite the simulated water withdrawals in this basin, the trend of the water flow was still increasing. However, comparing the current situation to one without water withdrawal for irrigation of rice, the increasing trend was lower with the corresponding Mann-Kendall Z value reduced by half. We conclude that for the Ibicuí Basin comparison of trends in the flow data does not clearly reflect the effect of water withdrawals for irrigation of rice.

Active controls: A look at analytical methods and associated tools

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Adams, W. M., Jr.; Mukhopadhyay, V.; Tiffany, S. H.; Abel, I.

1984-01-01

A review of analytical methods and associated tools for active controls analysis and design problems is presented. Approaches employed to develop mathematical models suitable for control system analysis and/or design are discussed. Significant efforts have been expended to develop tools to generate the models from the standpoint of control system designers' needs and develop the tools necessary to analyze and design active control systems. Representative examples of these tools are discussed. Examples where results from the methods and tools have been compared with experimental data are also presented. Finally, a perspective on future trends in analysis and design methods is presented.

The drinking water contamination crisis in Flint: Modeling temporal trends of lead level since returning to Detroit water system.

PubMed

Goovaerts, Pierre

2017-03-01

Since Flint returned to its pre-crisis source of drinking water close to 25,000 water samples have been collected and tested for lead and copper in >10,000 residences. This paper presents the first analysis and time trend modeling of lead data, providing new insights about the impact of this intervention. The analysis started with geocoding all water lead levels (WLL) measured during an 11-month period following the return to the Detroit water supply. Each data was allocated to the corresponding tax parcel unit and linked to secondary datasets, such as the composition of service lines, year built, or census tract poverty level. Only data collected on residential parcels within the City limits were used in the analysis. One key feature of Flint data is their collection through two different sampling initiatives: (i) voluntary or homeowner-driven sampling whereby concerned citizens decided to acquire a testing kit and conduct sampling on their own (non-sentinel sites), and (ii) State-controlled sampling where data were collected bi-weekly at selected sites after training of residents by technical teams (sentinel sites). Temporal trends modeled from these two datasets were found to be statistically different with fewer sentinel data exceeding WLL thresholds ranging from 10 to 50μg/L. Even after adjusting for housing characteristics the odds ratio (OR) of measuring WLL above 15μg/L at non-sentinel sites is significantly >1 (OR=1.480) and it increases with the threshold (OR=2.055 for 50μg/L). Joinpoint regression showed that the city-wide percentage of WLL data above 15μg/L displayed four successive trends since the return to Detroit Water System. Despite the recent improvement in water quality, the culprit for differences between sampling programs needs to be identified as it impacts exposure assessment and might influence whether there is compliance or not with the Lead and Copper Rule. Copyright © 2016 Elsevier B.V. All rights reserved.

A Study on Standard Competition with Network Effect Based on Evolutionary Game Model

NASA Astrophysics Data System (ADS)

Wang, Ye; Wang, Bingdong; Li, Kangning

Owing to networks widespread in modern society, standard competition with network effect is now endowed with new connotation. This paper aims to study the impact of network effect on standard competition; it is organized in the mode of "introduction-model setup-equilibrium analysis-conclusion". Starting from a well-structured model of evolutionary game, it is then extended to a dynamic analysis. This article proves both theoretically and empirically that whether or not a standard can lead the market trends depends on the utility it would bring, and the author also discusses some advisable strategies revolving around the two factors of initial position and border break.

Seasonal Trends in Stratospheric Water Vapor as Derived from SAGE II Data

NASA Technical Reports Server (NTRS)

Roell, Marilee M.; Fu, Rong

2008-01-01

Published analysis of HALOE and Boulder balloon measurements of water vapor have shown conflicting trends in stratospheric water vapor for the periods of 1981 through 2005. Analysis of the SAGE II monthly mean water vapor data filtered for large aerosol events for time periods from 1985-1991, 1995-1999, and 2000-2005 have shown a globally decreasing water vapor trend at 17.5km. Seasonal analysis for these three time periods show a decreasing trend in water vapor at 17.5km for the winter and spring seasons. The summer and autumn seasonal analysis show a decreasing trend from 1985-2005, however, there is a increasing trend in water vapor at 17.5km for these seasons during 1995-2005. Latitude vs height seasonal analysis show a decreasing trend in the lower stratosphere between 20S - 20N for the autumn season, while at the latitudes of 30-50S and 30-50N there is an increasing trend in water vapor at heights up to 15km for that season. Comparison with regions of monsoon activity (Asian and North American) show that the Asian monsoon region had some effect on the lower stratospheric moistening in 1995-1999, however, for the time period of 2000-2005, there was no change in the global trend analysis due to either monsoon region. This may be due to the limitations of the SAGE II data from 2000-2005.

Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: A multiscale joinpoint regression analysis

PubMed Central

2011-01-01

Background Although prostate cancer-related incidence and mortality have declined recently, striking racial/ethnic differences persist in the United States. Visualizing and modelling temporal trends of prostate cancer late-stage incidence, and how they vary according to geographic locations and race, should help explaining such disparities. Joinpoint regression is increasingly used to identify the timing and extent of changes in time series of health outcomes. Yet, most analyses of temporal trends are aspatial and conducted at the national level or for a single cancer registry. Methods Time series (1981-2007) of annual proportions of prostate cancer late-stage cases were analyzed for non-Hispanic Whites and non-Hispanic Blacks in each county of Florida. Noise in the data was first filtered by binomial kriging and results were modelled using joinpoint regression. A similar analysis was also conducted at the state level and for groups of metropolitan and non-metropolitan counties. Significant racial differences were detected using tests of parallelism and coincidence of time trends. A new disparity statistic was introduced to measure spatial and temporal changes in the frequency of racial disparities. Results State-level percentage of late-stage diagnosis decreased 50% since 1981; a decline that accelerated in the 90's when Prostate Specific Antigen (PSA) screening was introduced. Analysis at the metropolitan and non-metropolitan levels revealed that the frequency of late-stage diagnosis increased recently in urban areas, and this trend was significant for white males. The annual rate of decrease in late-stage diagnosis and the onset years for significant declines varied greatly among counties and racial groups. Most counties with non-significant average annual percent change (AAPC) were located in the Florida Panhandle for white males, whereas they clustered in South-eastern Florida for black males. The new disparity statistic indicated that the spatial extent of racial disparities reached a peak in 1990 because of an early decline in frequency of late-stage diagnosis observed for black males. Conclusions Analyzing temporal trends in cancer incidence and mortality rates outside a spatial framework is unsatisfactory, since it leads one to overlook significant geographical variation which can potentially generate new insights about the impact of various interventions. Differences observed among nested geographies in Florida show how the modifiable areal unit problem (MAUP) also impacts the analysis of temporal changes. PMID:22142274

Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: a multiscale joinpoint regression analysis.

PubMed

Goovaerts, Pierre; Xiao, Hong

2011-12-05

Although prostate cancer-related incidence and mortality have declined recently, striking racial/ethnic differences persist in the United States. Visualizing and modelling temporal trends of prostate cancer late-stage incidence, and how they vary according to geographic locations and race, should help explaining such disparities. Joinpoint regression is increasingly used to identify the timing and extent of changes in time series of health outcomes. Yet, most analyses of temporal trends are aspatial and conducted at the national level or for a single cancer registry. Time series (1981-2007) of annual proportions of prostate cancer late-stage cases were analyzed for non-Hispanic Whites and non-Hispanic Blacks in each county of Florida. Noise in the data was first filtered by binomial kriging and results were modelled using joinpoint regression. A similar analysis was also conducted at the state level and for groups of metropolitan and non-metropolitan counties. Significant racial differences were detected using tests of parallelism and coincidence of time trends. A new disparity statistic was introduced to measure spatial and temporal changes in the frequency of racial disparities. State-level percentage of late-stage diagnosis decreased 50% since 1981; a decline that accelerated in the 90's when Prostate Specific Antigen (PSA) screening was introduced. Analysis at the metropolitan and non-metropolitan levels revealed that the frequency of late-stage diagnosis increased recently in urban areas, and this trend was significant for white males. The annual rate of decrease in late-stage diagnosis and the onset years for significant declines varied greatly among counties and racial groups. Most counties with non-significant average annual percent change (AAPC) were located in the Florida Panhandle for white males, whereas they clustered in South-eastern Florida for black males. The new disparity statistic indicated that the spatial extent of racial disparities reached a peak in 1990 because of an early decline in frequency of late-stage diagnosis observed for black males. Analyzing temporal trends in cancer incidence and mortality rates outside a spatial framework is unsatisfactory, since it leads one to overlook significant geographical variation which can potentially generate new insights about the impact of various interventions. Differences observed among nested geographies in Florida show how the modifiable areal unit problem (MAUP) also impacts the analysis of temporal changes.

Sustainable groundwater management in California

USGS Publications Warehouse

Phillips, Steven P.; Rogers, Laurel Lynn; Faunt, Claudia

2015-12-01

The U.S. Geological Survey (USGS) uses data collection, modeling tools, and scientific analysis to help water managers plan for, and assess, hydrologic issues that can cause “undesirable results” associated with groundwater use. This information helps managers understand trends and investigate and predict effects of different groundwater-management strategies.

MODEL ANALYSIS OF RIPARIAN BUFFER EFFECTIVENESS FOR REDUCING NUTRIENT INPUTS TO STREAMS IN AGRICULTURAL LANDSCAPES

EPA Science Inventory

Federal and state agencies responsible for protecting water quality rely mainly on statistically-based methods to assess and manage risks to the nation's streams, lakes and estuaries. Although statistical approaches provide valuable information on current trends in water quality...

Resource Planning Model | Energy Analysis | NREL

Science.gov Websites

balancing authority. An image of a overlapping circles labelled Resource, Technical, Economic, and Market competing electricity technologies. An image of a overlapping circles labelled Resource, Technical, Economic ; Federal Resource Planning. Volume 1: Sectoral, Technical, and Economic Trends, NREL Technical Report (2016

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.

Series length used during trend analysis affects sensitivity to changes in progression rate in the ocular hypertension treatment study.

PubMed

Gardiner, Stuart K; Demirel, Shaban; De Moraes, Carlos Gustavo; Liebmann, Jeffrey M; Cioffi, George A; Ritch, Robert; Gordon, Mae O; Kass, Michael A

2013-02-15

Trend analysis techniques to detect glaucomatous progression typically assume a constant rate of change. This study uses data from the Ocular Hypertension Treatment Study to assess whether this assumption decreases sensitivity to changes in progression rate, by including earlier periods of stability. Series of visual fields (mean 24 per eye) completed at 6-month intervals from participants randomized initially to observation were split into subseries before and after the initiation of treatment (the "split-point"). The mean deviation rate of change (MDR) was derived using these entire subseries, and using only the window length (W) tests nearest the split-point, for different window lengths of W tests. A generalized estimating equation model was used to detect changes in MDR occurring at the split-point. Using shortened subseries with W = 7 tests, the MDR slowed by 0.142 dB/y upon initiation of treatment (P < 0.001), and the proportion of eyes showing "rapid deterioration" (MDR <-0.5 dB/y with P < 5%) decreased from 11.8% to 6.5% (P < 0.001). Using the entire sequence, no significant change in MDR was detected (P = 0.796), and there was no change in the proportion of eyes progressing (P = 0.084). Window lengths 6 ≤ W ≤ 9 produced similar benefits. Event analysis revealed a beneficial treatment effect in this dataset. This effect was not detected by linear trend analysis applied to entire series, but was detected when using shorter subseries of length between six and nine fields. Using linear trend analysis on the entire field sequence may not be optimal for detecting and monitoring progression. Nonlinear analyses may be needed for long series of fields. (ClinicalTrials.gov number, NCT00000125.).

Spatial-temporal trend for mother-to-child transmission of HIV up to infancy and during pre-Option B+ in western Kenya, 2007-13.

PubMed

Waruru, Anthony; Achia, Thomas N O; Muttai, Hellen; Ng'ang'a, Lucy; Zielinski-Gutierrez, Emily; Ochanda, Boniface; Katana, Abraham; Young, Peter W; Tobias, James L; Juma, Peter; De Cock, Kevin M; Tylleskär, Thorkild

2018-01-01

Using spatial-temporal analyses to understand coverage and trends in elimination of mother-to-child transmission of HIV (e-MTCT) efforts may be helpful in ensuring timely services are delivered to the right place. We present spatial-temporal analysis of seven years of HIV early infant diagnosis (EID) data collected from 12 districts in western Kenya from January 2007 to November 2013, during pre-Option B+ use. We included in the analysis infants up to one year old. We performed trend analysis using extended Cochran-Mantel-Haenszel stratified test and logistic regression models to examine trends and associations of infant HIV status at first diagnosis with: early diagnosis (<8 weeks after birth), age at specimen collection, infant ever having breastfed, use of single dose nevirapine, and maternal antiretroviral therapy status. We examined these covariates and fitted spatial and spatial-temporal semiparametric Poisson regression models to explain HIV-infection rates using R-integrated nested Laplace approximation package. We calculated new infections per 100,000 live births and used Quantum GIS to map fitted MTCT estimates for each district in Nyanza region. Median age was two months, interquartile range 1.5-5.8 months. Unadjusted pooled positive rate was 11.8% in the seven-years period and declined from 19.7% in 2007 to 7.0% in 2013, p < 0.01. Uptake of testing ≤8 weeks after birth was under 50% in 2007 and increased to 64.1% by 2013, p < 0.01. By 2013, the overall standardized MTCT rate was 447 infections per 100,000 live births. Based on Bayesian deviance information criterion comparisons, the spatial-temporal model with maternal and infant covariates was best in explaining geographical variation in MTCT. Improved EID uptake and reduced MTCT rates are indicators of progress towards e-MTCT. Cojoined analysis of time and covariates in a spatial context provides a robust approach for explaining differences in programmatic impact over time. During this pre-Option B+ period, the prevention of mother to child transmission program in this region has not achieved e-MTCT target of ≤50 infections per 100,000 live births. Geographical disparities in program achievements may signify gaps in spatial distribution of e-MTCT efforts and could indicate areas needing further resources and interventions.

Spatial–temporal trend for mother-to-child transmission of HIV up to infancy and during pre-Option B+ in western Kenya, 2007–13

PubMed Central

Achia, Thomas N.O.; Muttai, Hellen; Ng’ang’a, Lucy; Zielinski-Gutierrez, Emily; Ochanda, Boniface; Katana, Abraham; Tobias, James L.; Juma, Peter; De Cock, Kevin M.

2018-01-01

Introduction Using spatial–temporal analyses to understand coverage and trends in elimination of mother-to-child transmission of HIV (e-MTCT) efforts may be helpful in ensuring timely services are delivered to the right place. We present spatial–temporal analysis of seven years of HIV early infant diagnosis (EID) data collected from 12 districts in western Kenya from January 2007 to November 2013, during pre-Option B+ use. Methods We included in the analysis infants up to one year old. We performed trend analysis using extended Cochran–Mantel–Haenszel stratified test and logistic regression models to examine trends and associations of infant HIV status at first diagnosis with: early diagnosis (<8 weeks after birth), age at specimen collection, infant ever having breastfed, use of single dose nevirapine, and maternal antiretroviral therapy status. We examined these covariates and fitted spatial and spatial–temporal semiparametric Poisson regression models to explain HIV-infection rates using R-integrated nested Laplace approximation package. We calculated new infections per 100,000 live births and used Quantum GIS to map fitted MTCT estimates for each district in Nyanza region. Results Median age was two months, interquartile range 1.5–5.8 months. Unadjusted pooled positive rate was 11.8% in the seven-years period and declined from 19.7% in 2007 to 7.0% in 2013, p < 0.01. Uptake of testing ≤8 weeks after birth was under 50% in 2007 and increased to 64.1% by 2013, p < 0.01. By 2013, the overall standardized MTCT rate was 447 infections per 100,000 live births. Based on Bayesian deviance information criterion comparisons, the spatial–temporal model with maternal and infant covariates was best in explaining geographical variation in MTCT. Discussion Improved EID uptake and reduced MTCT rates are indicators of progress towards e-MTCT. Cojoined analysis of time and covariates in a spatial context provides a robust approach for explaining differences in programmatic impact over time. Conclusion During this pre-Option B+ period, the prevention of mother to child transmission program in this region has not achieved e-MTCT target of ≤50 infections per 100,000 live births. Geographical disparities in program achievements may signify gaps in spatial distribution of e-MTCT efforts and could indicate areas needing further resources and interventions. PMID:29576942

Predictive data modeling of human type II diabetes related statistics

NASA Astrophysics Data System (ADS)

Jaenisch, Kristina L.; Jaenisch, Holger M.; Handley, James W.; Albritton, Nathaniel G.

2009-04-01

During the course of routine Type II treatment of one of the authors, it was decided to derive predictive analytical Data Models of the daily sampled vital statistics: namely weight, blood pressure, and blood sugar, to determine if the covariance among the observed variables could yield a descriptive equation based model, or better still, a predictive analytical model that could forecast the expected future trend of the variables and possibly eliminate the number of finger stickings required to montior blood sugar levels. The personal history and analysis with resulting models are presented.

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.

Warming Trends and Bleaching Stress of the World’s Coral Reefs 1985-2012

NASA Astrophysics Data System (ADS)

Heron, Scott F.; Maynard, Jeffrey A.; van Hooidonk, Ruben; Eakin, C. Mark

2016-12-01

Coral reefs across the world’s oceans are in the midst of the longest bleaching event on record (from 2014 to at least 2016). As many of the world’s reefs are remote, there is limited information on how past thermal conditions have influenced reef composition and current stress responses. Using satellite temperature data for 1985-2012, the analysis we present is the first to quantify, for global reef locations, spatial variations in warming trends, thermal stress events and temperature variability at reef-scale (~4 km). Among over 60,000 reef pixels globally, 97% show positive SST trends during the study period with 60% warming significantly. Annual trends exceeded summertime trends at most locations. This indicates that the period of summer-like temperatures has become longer through the record, with a corresponding shortening of the ‘winter’ reprieve from warm temperatures. The frequency of bleaching-level thermal stress increased three-fold between 1985-91 and 2006-12 - a trend climate model projections suggest will continue. The thermal history data products developed enable needed studies relating thermal history to bleaching resistance and community composition. Such analyses can help identify reefs more resilient to thermal stress.

Warming Trends and Bleaching Stress of the World’s Coral Reefs 1985–2012

PubMed Central

Heron, Scott F.; Maynard, Jeffrey A.; van Hooidonk, Ruben; Eakin, C. Mark

2016-01-01

Coral reefs across the world’s oceans are in the midst of the longest bleaching event on record (from 2014 to at least 2016). As many of the world’s reefs are remote, there is limited information on how past thermal conditions have influenced reef composition and current stress responses. Using satellite temperature data for 1985–2012, the analysis we present is the first to quantify, for global reef locations, spatial variations in warming trends, thermal stress events and temperature variability at reef-scale (~4 km). Among over 60,000 reef pixels globally, 97% show positive SST trends during the study period with 60% warming significantly. Annual trends exceeded summertime trends at most locations. This indicates that the period of summer-like temperatures has become longer through the record, with a corresponding shortening of the ‘winter’ reprieve from warm temperatures. The frequency of bleaching-level thermal stress increased three-fold between 1985–91 and 2006–12 – a trend climate model projections suggest will continue. The thermal history data products developed enable needed studies relating thermal history to bleaching resistance and community composition. Such analyses can help identify reefs more resilient to thermal stress. PMID:27922080

Warming Trends and Bleaching Stress of the World's Coral Reefs 1985-2012.

PubMed

Heron, Scott F; Maynard, Jeffrey A; van Hooidonk, Ruben; Eakin, C Mark

2016-12-06

Coral reefs across the world's oceans are in the midst of the longest bleaching event on record (from 2014 to at least 2016). As many of the world's reefs are remote, there is limited information on how past thermal conditions have influenced reef composition and current stress responses. Using satellite temperature data for 1985-2012, the analysis we present is the first to quantify, for global reef locations, spatial variations in warming trends, thermal stress events and temperature variability at reef-scale (~4 km). Among over 60,000 reef pixels globally, 97% show positive SST trends during the study period with 60% warming significantly. Annual trends exceeded summertime trends at most locations. This indicates that the period of summer-like temperatures has become longer through the record, with a corresponding shortening of the 'winter' reprieve from warm temperatures. The frequency of bleaching-level thermal stress increased three-fold between 1985-91 and 2006-12 - a trend climate model projections suggest will continue. The thermal history data products developed enable needed studies relating thermal history to bleaching resistance and community composition. Such analyses can help identify reefs more resilient to thermal stress.

No Trend in the Intergenerational Transmission of Divorce

PubMed Central

LI, JUI-CHUNG ALLEN; WU, LAWRENCE L.

2008-01-01

Previous studies on trends in the intergenerational transmission of divorce have produced mixed findings, with two studies (McLanahan and Bumpass 1988; Teachman 2002) reporting no trend in divorce transmission and one study (Wolfinger 1999) finding that divorce transmission has weakened substantially. Using a stratified Cox proportional hazard model, we analyze data from the National Survey of Families and Households and find no evidence for any trend in divorce transmission. To reconcile apparent differences in results, we note that the General Social Survey data used by Wolfinger lack information on marital duration, permitting analysis only for whether respondents have divorced by interview. As a result, an apparent decline in divorce transmission could be due to inadequate adjustments for the longer exposures to risk by earlier marriage cohorts, yielding a higher probability of divorce by interview for earlier cohorts relative to more recent cohorts even if divorce risks are identical across all marriage cohorts. We confirm this possibility by using a series of discrete-time hazard logistic regressions to investigate the sensitivity of estimates of trends in divorce transmission to different adjustments for exposure to risk. We conclude that there has been no trend in the intergenerational transmission of divorce. PMID:19110902

ACRIM-gap and total solar irradiance revisited: Is there a secular trend between 1986 and 1996?

NASA Astrophysics Data System (ADS)

Krivova, N. A.; Solanki, S. K.; Wenzler, T.

2009-10-01

A gap in the total solar irradiance (TSI) measurements between ACRIM-1 and ACRIM-2 led to the ongoing debate on the presence or not of a secular trend between the minima preceding cycles 22 (in 1986) and 23 (1996). It was recently proposed to use the SATIRE model of solar irradiance variations to bridge this gap. When doing this, it is important to use the appropriate SATIRE-based reconstruction, which we do here, employing a reconstruction based on magnetograms. The accuracy of this model on months to years timescales is significantly higher than that of a model developed for long-term reconstructions used by the ACRIM team for such an analysis. The constructed ‘mixed’ ACRIM — SATIRE composite shows no increase in the TSI from 1986 to 1996, in contrast to the ACRIM TSI composite.

On the Photometric Calibration of FORS2 and the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Bramich, D.; Moehler, S.; Coccato, L.; Freudling, W.; Garcia-Dabó, C. E.; Müller, P.; Saviane, I.

2012-09-01

An accurate absolute calibration of photometric data to place them on a standard magnitude scale is very important for many science goals. Absolute calibration requires the observation of photometric standard stars and analysis of the observations with an appropriate photometric model including all relevant effects. In the FORS Absolute Photometry (FAP) project, we have developed a standard star observing strategy and modelling procedure that enables calibration of science target photometry to better than 3% accuracy on photometrically stable nights given sufficient signal-to-noise. In the application of this photometric modelling to large photometric databases, we have investigated the Sloan Digital Sky Survey (SDSS) and found systematic trends in the published photometric data. The amplitudes of these trends are similar to the reported typical precision (˜1% and ˜2%) of the SDSS photometry in the griz- and u-bands, respectively.

Trends of Obesity in Iranian Adults from 1990s to late 2000s; a Systematic Review and Meta-analysis

PubMed Central

Mirzazadeh, Ali; Salimzadeh, Hamideh; Arabi, Minoo; Navadeh, Soodabeh; Hajarizadeh, Behzad; Haghdoost, Ali Akbar

2013-01-01

BACKGROUND Obesity is currently emerging as a global epidemic, affecting 10% of adult population worldwide. The primary objective of the current systematic review is to describe the trend of overall prevalence of obesity in Iranian women and menthrough a meta-analysis. METHODS We searched the medical literature published from 1990 to 2007 in Medline (PubMed), EMBASE database, and the Iranian digital library. All published reports of research projects, papers in relevant congresses, unpublished crude data analysis, proceedings, books and dissertations were reviewed. Data from eligible papers that fulfilled the qualification criteria entered meta-analysis (Random Model). RESULTS Data from 209,166 individuals were analyzed. The overall prevalence of obesity in adults was 18.5% (95%CI: 15.1-21.8), respectively. The prevalence of obesity in men and women was 12.9% (95%CI: 10.9-14.9) and 26.2% (95%CI: 21.3-30.5), respectively. The trend of obesity was similar in both genders; women had almost a constantly higher risk of obesity than men during the recent two decades. CONCLUSION Data from 209,166 individuals were analyzed. The overall prevalence of obesity in adults was 18.5% (95%CI: 15.1-21.8), respectively. The prevalence of obesity in men and women was 12.9% (95%CI: 10.9-14.9) and 26.2% (95%CI: 21.3-30.5), respectively. The trend of obesity was similar in both genders; women had almost a constantly higher risk of obesity than men during the recent two decades. PMID:24829686

Aerosol optical depth trend over the Middle East

NASA Astrophysics Data System (ADS)

Klingmueller, Klaus; Pozzer, Andrea; Metzger, Swen; Abdelkader, Mohamed; Stenchikov, Georgiy; Lelieveld, Jos

2016-04-01

We use the combined Dark Target/Deep Blue aerosol optical depth (AOD) satellite product of the Moderate-resolution Imaging Spectroradiometer (MODIS) collection 6 to study trends over the Middle East between 2000 and 2015. Our analysis corroborates a previously identified positive AOD trend over large parts of the Middle East during the period 2001 to 2012. By relating the annual AOD to precipitation, soil moisture and surface wind, being the main factors controlling the dust cycle, we identify regions where these attributes are significantly correlated to the AOD over Saudi Arabia, Iraq and Iran. The Fertile Crescent turns out to be of prime importance for the AOD trend over these countries. Using multiple linear regression we show that AOD trend and interannual variability can be attributed to the above mentioned dust cycle parameters, confirming that the AOD increase is predominantly driven by dust. In particular, the positive AOD trend relates to a negative soil moisture trend. This suggests that increasing temperature and decreasing relative humidity in the last decade have promoted soil drying, leading to increased dust emissions and AOD; consequently an AOD increase is expected due to climate change. Based on simulations using the ECHAM/MESSy atmospheric chemistry-climate model (EMAC), we interpret the correlations identified in the observational data in terms of causal relationships.

Response of ice cover on shallow lakes of the North Slope of Alaska to contemporary climate conditions (1950-2011): radar remote sensing and numerical modeling data analysis

NASA Astrophysics Data System (ADS)

Surdu, C. M.; Duguay, C. R.; Brown, L. C.; Fernández Prieto, D.

2013-07-01

Air temperature and winter precipitation changes over the last five decades have impacted the timing, duration, and thickness of the ice cover on Arctic lakes as shown by recent studies. In the case of shallow tundra lakes, many of which are less than 3 m deep, warmer climate conditions could result in thinner ice covers and consequently, to a smaller fraction of lakes freezing to their bed in winter. However, these changes have not yet been comprehensively documented. The analysis of a 20 yr time series of ERS-1/2 synthetic aperture radar (SAR) data and a numerical lake ice model were employed to determine the response of ice cover (thickness, freezing to the bed, and phenology) on shallow lakes of the North Slope of Alaska (NSA) to climate conditions over the last six decades. Analysis of available SAR data from 1991-2011, from a sub-region of the NSA near Barrow, shows a reduction in the fraction of lakes that freeze to the bed in late winter. This finding is in good agreement with the decrease in ice thickness simulated with the Canadian Lake Ice Model (CLIMo), a lower fraction of lakes frozen to the bed corresponding to a thinner ice cover. Observed changes of the ice cover show a trend toward increasing floating ice fractions from 1991 to 2011, with the greatest change occurring in April, when the grounded ice fraction declined by 22% (α = 0.01). Model results indicate a trend toward thinner ice covers by 18-22 cm (no-snow and 53% snow depth scenarios, α = 0.01) during the 1991-2011 period and by 21-38 cm (α = 0.001) from 1950-2011. The longer trend analysis (1950-2011) also shows a decrease in the ice cover duration by ∼24 days consequent to later freeze-up dates by 5.9 days (α = 0.1) and earlier break-up dates by 17.7-18.6 days (α = 0.001).

Seasonality of coastal upwelling trends under future warming scenarios along the southern limit of the canary upwelling system

NASA Astrophysics Data System (ADS)

Sousa, Magda Catarina; Alvarez, Ines; deCastro, Maite; Gomez-Gesteira, Moncho; Dias, João Miguel

2017-04-01

The Canary Upwelling Ecosystem (CUE) is one of the four most important upwelling sites around the world in terms of primary production, with coastal upwelling mostly a year-round phenomenon south of 30°N. Based on annual future projections, several previous studies indicated that global warming will intensify coastal upwelling in the northern region and will induce its weakening at the southernmost latitudes. However, analysis of historical data, showed that coastal upwelling depends on the length of the time series, the season, and even the database used. Thus, despite previous efforts, an overall detailed description of seasonal upwelling trends and their effects on sea surface temperature (SST) along the Canary coast over the 21st century remains unclear. To address this issue, several regional and global wind and SST climate models from CORDEX and CMIP5 projects for the period 1976-2099 were analyzed. This research provides new insights about coastal upwelling trends under future warming scenarios for the CUE, with results showing opposite patterns for upwelling index (UI) trends depending on the season. A weakening of the UI occurs from May to August all along the coast, whereas it increases from October to April. Analysis of SST trends reveals a general warming throughout the area, although the warming rate is considerably lower near the shore than at open ocean locations due to coastal upwelling effects. In addition, SST projections show higher warming rates from May to August than from October to April in response to the future decreasing trend in the UI during the summer months.

Trend analysis of vegetation in Louisiana's Atchafalaya river basin

USGS Publications Warehouse

O'Neil, Calvin P.; deSteiguer, J. Edward; North, Gary W.

1978-01-01

The purpose of the study was to determine vegetation succession trends; produce a current vegetation map of the basin; and to develop a mathematical model capable of predicting vegetation changes based on hydrologic factors. A statistical relationship of forests and hydrological variables with forest succession constraints predicted forest acreage totals for 16 forest categories within 70% or better of actual values in two-thirds of the cases. Using time-lapsed photography covering 42 years, 23 categories were described. The succession trend of vegetation since 1930, by sedimentation, had been toward mixed hardwoods, except for isolated areas. Satellite MSS Band 7 imagery was used to map the current vegetation into three main categories and for assessment of acreage. Additionally, a geological anomaly was recognized on satellite imagery indication an effect on drainage and sedimentation.

The impact of roads on the demography of grizzly bears in Alberta.

PubMed

Boulanger, John; Stenhouse, Gordon B

2014-01-01

One of the principal factors that have reduced grizzly bear populations has been the creation of human access into grizzly bear habitat by roads built for resource extraction. Past studies have documented mortality and distributional changes of bears relative to roads but none have attempted to estimate the direct demographic impact of roads in terms of both survival rates, reproductive rates, and the interaction of reproductive state of female bears with survival rate. We applied a combination of survival and reproductive models to estimate demographic parameters for threatened grizzly bear populations in Alberta. Instead of attempting to estimate mean trend we explored factors which caused biological and spatial variation in population trend. We found that sex and age class survival was related to road density with subadult bears being most vulnerable to road-based mortality. A multi-state reproduction model found that females accompanied by cubs of the year and/or yearling cubs had lower survival rates compared to females with two year olds or no cubs. A demographic model found strong spatial gradients in population trend based upon road density. Threshold road densities needed to ensure population stability were estimated to further refine targets for population recovery of grizzly bears in Alberta. Models that considered lowered survival of females with dependant offspring resulted in lower road density thresholds to ensure stable bear populations. Our results demonstrate likely spatial variation in population trend and provide an example how demographic analysis can be used to refine and direct conservation measures for threatened species.

The Impact of Roads on the Demography of Grizzly Bears in Alberta

PubMed Central

2014-01-01

One of the principal factors that have reduced grizzly bear populations has been the creation of human access into grizzly bear habitat by roads built for resource extraction. Past studies have documented mortality and distributional changes of bears relative to roads but none have attempted to estimate the direct demographic impact of roads in terms of both survival rates, reproductive rates, and the interaction of reproductive state of female bears with survival rate. We applied a combination of survival and reproductive models to estimate demographic parameters for threatened grizzly bear populations in Alberta. Instead of attempting to estimate mean trend we explored factors which caused biological and spatial variation in population trend. We found that sex and age class survival was related to road density with subadult bears being most vulnerable to road-based mortality. A multi-state reproduction model found that females accompanied by cubs of the year and/or yearling cubs had lower survival rates compared to females with two year olds or no cubs. A demographic model found strong spatial gradients in population trend based upon road density. Threshold road densities needed to ensure population stability were estimated to further refine targets for population recovery of grizzly bears in Alberta. Models that considered lowered survival of females with dependant offspring resulted in lower road density thresholds to ensure stable bear populations. Our results demonstrate likely spatial variation in population trend and provide an example how demographic analysis can be used to refine and direct conservation measures for threatened species. PMID:25532035

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.

Distinguishing the drivers of trends in land carbon fluxes and plant volatile emissions over the past three decades

NASA Astrophysics Data System (ADS)

Yue, X.; Unger, N.; Zheng, Y.

2015-08-01

The terrestrial biosphere has experienced dramatic changes in recent decades. Estimates of historical trends in land carbon fluxes remain uncertain because long-term observations are limited on the global scale. Here, we use the Yale Interactive terrestrial Biosphere (YIBs) model to estimate decadal trends in land carbon fluxes and emissions of biogenic volatile organic compounds (BVOCs) and to identify the key drivers for these changes during 1982-2011. Driven with hourly meteorology from WFDEI (WATCH Forcing Data methodology applied to ERA-Interim data), the model simulates an increasing trend of 297 Tg C a-2 in gross primary productivity (GPP) and 185 Tg C a-2 in the net primary productivity (NPP). CO2 fertilization is the main driver for the flux changes in forest ecosystems, while meteorology dominates the changes in grasslands and shrublands. Warming boosts summer GPP and NPP at high latitudes, while drought dampens carbon uptake in tropical regions. North of 30° N, increasing temperatures induce a substantial extension of 0.22 day a-1 for the growing season; however, this phenological change alone does not promote regional carbon uptake and BVOC emissions. Nevertheless, increases of LAI at peak season accounts for ~ 25 % of the trends in GPP and isoprene emissions at the northern lands. The net land sink shows statistically insignificant increases of only 3 Tg C a-2 globally because of simultaneous increases in soil respiration. In contrast, driven with alternative meteorology from MERRA (Modern Era-Retrospective Analysis), the model predicts significant increases of 59 Tg C a-2 in the land sink due to strengthened uptake in the Amazon. Global BVOC emissions are calculated using two schemes. With the photosynthesis-dependent scheme, the model predicts increases of 0.4 Tg C a-2 in isoprene emissions, which are mainly attributed to warming trends because CO2 fertilization and inhibition effects offset each other. Using the MEGAN (Model of Emissions of Gases and Aerosols from Nature) scheme, the YIBs model simulates global reductions of 1.1 Tg C a-2 in isoprene and 0.04 Tg C a-2 in monoterpene emissions in response to the CO2 inhibition effects. Land use change shows limited impacts on global carbon fluxes and BVOC emissions, but there are regional contrasting impacts over Europe (afforestation) and China (deforestation).

The Use of Surrogate Data in Demographic Population Viability Analysis: A Case Study of California Sea Lions

PubMed Central

2015-01-01

Reliable data necessary to parameterize population models are seldom available for imperiled species. As an alternative, data from populations of the same species or from ecologically similar species have been used to construct models. In this study, we evaluated the use of demographic data collected at one California sea lion colony (Los Islotes) to predict the population dynamics of the same species from two other colonies (San Jorge and Granito) in the Gulf of California, Mexico, for which demographic data are lacking. To do so, we developed a stochastic demographic age-structured matrix model and conducted a population viability analysis for each colony. For the Los Islotes colony we used site-specific pup, juvenile, and adult survival probabilities, as well as birth rates for older females. For the other colonies, we used site-specific pup and juvenile survival probabilities, but used surrogate data from Los Islotes for adult survival probabilities and birth rates. We assessed these models by comparing simulated retrospective population trajectories to observed population trends based on count data. The projected population trajectories approximated the observed trends when surrogate data were used for one colony but failed to match for a second colony. Our results indicate that species-specific and even region-specific surrogate data may lead to erroneous conservation decisions. These results highlight the importance of using population-specific demographic data in assessing extinction risk. When vital rates are not available and immediate management actions must be taken, in particular for imperiled species, we recommend the use of surrogate data only when the populations appear to have similar population trends. PMID:26413746

Challenges Facing Design and Analysis Tools

NASA Technical Reports Server (NTRS)

Knight, Norman F., Jr.; Broduer, Steve (Technical Monitor)

2001-01-01

The design and analysis of future aerospace systems will strongly rely on advanced engineering analysis tools used in combination with risk mitigation procedures. The implications of such a trend place increased demands on these tools to assess off-nominal conditions, residual strength, damage propagation, and extreme loading conditions in order to understand and quantify these effects as they affect mission success. Advances in computer hardware such as CPU processing speed, memory, secondary storage, and visualization provide significant resources for the engineer to exploit in engineering design. The challenges facing design and analysis tools fall into three primary areas. The first area involves mechanics needs such as constitutive modeling, contact and penetration simulation, crack growth prediction, damage initiation and progression prediction, transient dynamics and deployment simulations, and solution algorithms. The second area involves computational needs such as fast, robust solvers, adaptivity for model and solution strategies, control processes for concurrent, distributed computing for uncertainty assessments, and immersive technology. Traditional finite element codes still require fast direct solvers which when coupled to current CPU power enables new insight as a result of high-fidelity modeling. The third area involves decision making by the analyst. This area involves the integration and interrogation of vast amounts of information - some global in character while local details are critical and often drive the design. The proposed presentation will describe and illustrate these areas using composite structures, energy-absorbing structures, and inflatable space structures. While certain engineering approximations within the finite element model may be adequate for global response prediction, they generally are inadequate in a design setting or when local response prediction is critical. Pitfalls to be avoided and trends for emerging analysis tools will be described.

Trends and predicted trends in presentations of older people to Australian emergency departments: effects of demand growth, population aging and climate change.

PubMed

Burkett, Ellen; Martin-Khan, Melinda G; Scott, Justin; Samanta, Mayukh; Gray, Leonard C

2017-07-01

Objectives The aim of the present study was to describe trends in and age and gender distributions of presentations of older people to Australian emergency departments (EDs) from July 2006 to June 2011, and to develop ED utilisation projections to 2050. Methods A retrospective analysis of data collected in the National Non-admitted Patient Emergency Department Care Database was undertaken to assess trends in ED presentations. Three standard Australian Bureau of Statistics population growth models, with and without adjustment for current trends in ED presentation growth and effects of climate change, were examined with projections of ED presentations across three age groups (0-64, 65-84 and ≥85 years) to 2050. Results From 2006-07 to 2010-11, ED presentations increased by 12.63%, whereas the Australian population over this time increased by only 7.26%. Rates of presentation per head of population were greatest among those aged ≥85 years. Projections of ED presentations to 2050 revealed that overall ED presentations are forecast to increase markedly, with the rate of increase being most marked for older people. Conclusion Growth in Australian ED presentations from 2006-07 to 2010-11 was greater than that expected from population growth alone. The predicted changes in demand for ED care will only be able to be optimally managed if Australian health policy, ED funding instruments and ED models of care are adjusted to take into account the specific care and resource needs of older people. What is known about the topic? Rapid population aging is anticipated over coming decades. International studies and specific local-level Australian studies have demonstrated significant growth in ED presentations. There have been no prior national-level Australian studies of ED presentation trends by age group. What does this paper add? The present study examined national ED presentation trends from July 2006 to June 2011, with specific emphasis on trends in presentation by age group. ED presentation growth was found to exceed population growth in all age groups. The rate of ED presentations per head of population was highest among those aged ≥85 years. ED utilisation projections to 2050, using standard Australian Bureau of Statistics population modelling, with and without adjustment for current ED growth, were developed. The projections demonstrated linear growth in ED presentation for those aged 0-84 years, with growth in ED presentations of the ≥85 year age group demonstrating marked acceleration after 2030. What are the implications for practitioners? Growth in ED presentations exceeding population growth suggests that current models of acute health care delivery require review to ensure that optimal care is delivered in the most fiscally efficient manner. Trends in presentation of older people emphasise the imperative for ED workforce planning and education in care of this complex patient cohort, and the requirement to review funding models to incentivise investment in ED avoidance and substitutive care models targeting older people.

Development of time-trend model for analysing and predicting case pattern of dog bite injury induced rabies-like-illness in Liberia, 2014-2017.

PubMed

Jomah, N D; Ojo, J F; Odigie, E A; Olugasa, B O

2014-12-01

The post-civil war records of dog bite injuries (DBI) and rabies-like-illness (RLI) among humans in Liberia is a vital epidemiological resource for developing a predictive model to guide the allocation of resources towards human rabies control. Whereas DBI and RLI are high, they are largely under-reported. The objective of this study was to develop a time model of the case-pattern and apply it to derive predictors of time-trend point distribution of DBI-RLI cases. A retrospective 6 years data of DBI distribution among humans countrywide were converted to quarterly series using a transformation technique of Minimizing Squared First Difference statistic. The generated dataset was used to train a time-trend model of the DBI-RLI syndrome in Liberia. An additive detenninistic time-trend model was selected due to its performance compared to multiplication model of trend and seasonal movement. Parameter predictors were run on least square method to predict DBI cases for a prospective 4 years period, covering 2014-2017. The two-stage predictive model of DBI case-pattern between 2014 and 2017 was characterised by a uniform upward trend within Liberia's coastal and hinterland Counties over the forecast period. This paper describes a translational application of the time-trend distribution pattern of DBI epidemics, 2008-2013 reported in Liberia, on which a predictive model was developed. A computationally feasible two-stage time-trend permutation approach is proposed to estimate the time-trend parameters and conduct predictive inference on DBI-RLI in Liberia.

Trend time-series modeling and forecasting with neural networks.

PubMed

Qi, Min; Zhang, G Peter

2008-05-01

Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

NASA Astrophysics Data System (ADS)

Akperov, Mirseid; Rinke, Annette; Mokhov, Igor I.; Matthes, Heidrun; Semenov, Vladimir A.; Adakudlu, Muralidhar; Cassano, John; Christensen, Jens H.; Dembitskaya, Mariya A.; Dethloff, Klaus; Fettweis, Xavier; Glisan, Justin; Gutjahr, Oliver; Heinemann, Günther; Koenigk, Torben; Koldunov, Nikolay V.; Laprise, René; Mottram, Ruth; Nikiéma, Oumarou; Scinocca, John F.; Sein, Dmitry; Sobolowski, Stefan; Winger, Katja; Zhang, Wenxin

2018-03-01

The ability of state-of-the-art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA-Interim, National Centers for Environmental Prediction-Climate Forecast System Reanalysis, National Aeronautics and Space Administration-Modern-Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency-Japanese 55-year reanalysis) in winter and summer for 1981-2010 period. In addition, we compare cyclone statistics between ERA-Interim and the Arctic System Reanalysis reanalyses for 2000-2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large-scale spectral nudging show a better agreement with ERA-Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables.

Self-reconfigurable ship fluid-network modeling for simulation-based design

NASA Astrophysics Data System (ADS)

Moon, Kyungjin

Our world is filled with large-scale engineering systems, which provide various services and conveniences in our daily life. A distinctive trend in the development of today's large-scale engineering systems is the extensive and aggressive adoption of automation and autonomy that enable the significant improvement of systems' robustness, efficiency, and performance, with considerably reduced manning and maintenance costs, and the U.S. Navy's DD(X), the next-generation destroyer program, is considered as an extreme example of such a trend. This thesis pursues a modeling solution for performing simulation-based analysis in the conceptual or preliminary design stage of an intelligent, self-reconfigurable ship fluid system, which is one of the concepts of DD(X) engineering plant development. Through the investigations on the Navy's approach for designing a more survivable ship system, it is found that the current naval simulation-based analysis environment is limited by the capability gaps in damage modeling, dynamic model reconfiguration, and simulation speed of the domain specific models, especially fluid network models. As enablers of filling these gaps, two essential elements were identified in the formulation of the modeling method. The first one is the graph-based topological modeling method, which will be employed for rapid model reconstruction and damage modeling, and the second one is the recurrent neural network-based, component-level surrogate modeling method, which will be used to improve the affordability and efficiency of the modeling and simulation (M&S) computations. The integration of the two methods can deliver computationally efficient, flexible, and automation-friendly M&S which will create an environment for more rigorous damage analysis and exploration of design alternatives. As a demonstration for evaluating the developed method, a simulation model of a notional ship fluid system was created, and a damage analysis was performed. Next, the models representing different design configurations of the fluid system were created, and damage analyses were performed with them in order to find an optimal design configuration for system survivability. Finally, the benefits and drawbacks of the developed method were discussed based on the result of the demonstration.

Improving FIA trend analysis through model-based estimation using landsat disturbance maps and the forest vegetation simulator

Treesearch

Sean P. Healey; Gretchen G. Moisen; Paul L. Patterson

2012-01-01

The Forest Inventory and Analysis (FIA) Program's panel system, in which 10-20 percent of the sample is measured in any given year, is designed to increase the currency of FIA reporting and its sensitivity to factors operating at relatively fine temporal scales. Now that much of the country has completed at least one measurement cycle over all panels, there is an...

Application and Analysis of Measurement Model for Calibrating Spatial Shear Surface in Triaxial Test

NASA Astrophysics Data System (ADS)

Zhang, Zhihua; Qiu, Hongsheng; Zhang, Xiedong; Zhang, Hang

2017-12-01

Discrete element method has great advantages in simulating the contacts, fractures, large displacement and deformation between particles. In order to analyze the spatial distribution of the shear surface in the three-dimensional triaxial test, a measurement model is inserted in the numerical triaxial model which is generated by weighted average assembling method. Due to the non-visibility of internal shear surface in laboratory, it is largely insufficient to judge the trend of internal shear surface only based on the superficial cracks of sheared sample, therefore, the measurement model is introduced. The trend of the internal shear zone is analyzed according to the variations of porosity, coordination number and volumetric strain in each layer. It shows that as a case study on confining stress of 0.8 MPa, the spatial shear surface is calibrated with the results of the rotated particle distribution and the theoretical value with the specific characteristics of the increase of porosity, the decrease of coordination number, and the increase of volumetric strain, which represents the measurement model used in three-dimensional model is applicable.

A comparison of three approaches to non-stationary flood frequency analysis

NASA Astrophysics Data System (ADS)

Debele, S. E.; Strupczewski, W. G.; Bogdanowicz, E.

2017-08-01

Non-stationary flood frequency analysis (FFA) is applied to statistical analysis of seasonal flow maxima from Polish and Norwegian catchments. Three non-stationary estimation methods, namely, maximum likelihood (ML), two stage (WLS/TS) and GAMLSS (generalized additive model for location, scale and shape parameters), are compared in the context of capturing the effect of non-stationarity on the estimation of time-dependent moments and design quantiles. The use of a multimodel approach is recommended, to reduce the errors due to the model misspecification in the magnitude of quantiles. The results of calculations based on observed seasonal daily flow maxima and computer simulation experiments showed that GAMLSS gave the best results with respect to the relative bias and root mean square error in the estimates of trend in the standard deviation and the constant shape parameter, while WLS/TS provided better accuracy in the estimates of trend in the mean value. Within three compared methods the WLS/TS method is recommended to deal with non-stationarity in short time series. Some practical aspects of the GAMLSS package application are also presented. The detailed discussion of general issues related to consequences of climate change in the FFA is presented in the second part of the article entitled "Around and about an application of the GAMLSS package in non-stationary flood frequency analysis".