Trend detection

Based on a newly developed daily precipitation dataset of 740 stations in China and more robust trend detection techniques, trends in annual and seasonal total precipitation and in extreme daily precipitation, defined as those larger than its 95th percentile for the year, summer, and winter half years, have been assessed for the period 1951-2000. Possible links between changes in total precipitation and frequency of extremes have also been explored. The results indicate that there is little trend in total precipitation for China as a whole, but there are distinctive regional and seasonal patterns of trends. Annual total precipitation has significantly decreased over southern northeast China, north China, and over the Sichuan Basin but significantly increased in western China, the Yangtze River valley, and the southeastern coast. In western China, precipitation increase has been observed for both cold and warm seasons. However, trends differ from one season to another in eastern China. Spring precipitation has increased in southern northeast China and north China but decreased significantly in the midreach of the Yangzte River. The summer precipitation trend is very similar to that of annual totals. Autumn precipitation has generally decreased throughout eastern China. In winter, precipitation has significantly decreased over the northern part of eastern China but increased in the south. The number of rain days has significantly decreased throughout most parts of China with northwest China being an exception. Meanwhile, precipitation intensity has significantly increased. This suggests that the precipitation increase in western China is due to the increase in both precipitation frequency and intensity. In eastern China, the impact of reduced number of rain days seems to be more dominant in the north while the influence of enhanced intensity prevails in the south. Over regions with increasing precipitation trends, there have been much higher than normal frequency of precipitation extreme events. For example, significant increases in extreme precipitation have been found in western China, in the mid-lower reaches of the Yangtze River, and in parts of the southwest and south China coastal area. A significant decrease in extremes is observed in north China and the Sichuan Basin. Trends in the number of extremes and total precipitation from nonextreme events are generally in phase. An exception is southwest China where an increase of extreme events is associated with a decrease in total nonextreme precipitation.

This paper describes the development and application of a procedure that identi®es trends in hydrologic variables. The procedure utilizes the Mann±Kendall non-parametric test to detect trends, a permutation approach to estimate the test distribution, and accounts for the correlation structure in the data in determining the signi®cance level of the test results. The research investigates 18 hydrologic variables that re¯ect different parts of the hydrologic cycle. The hydrologic variables are analyzed for a network of 248 Canadian catchments that are considered to re¯ect natural conditions. A selection of catchments identi®ed to have trends in hydrologic variables is studied further to investigate the presence of trends in meteorological variables and the relationship between the hydrologic and the meteorological response to climatic change. It is concluded that a greater number of trends are observed than are expected to occur by chance. There are differences in the geographic location of signi®cant trends in the hydrologic variables investigated implying that impacts are not spatially uniform. q

1] Prewhitening has been used to eliminate the influence of serial correlation on the Mann-Kendall (MK) test in trend-detection studies of hydrological time series. However, its ability to accomplish such a task has not been well documented. This study investigates this issue by Monte Carlo simulation. Simulated time series consist of a linear trend and a lag 1 autoregressive (AR(1)) process with a noise. Simulation results demonstrate that when trend exists in a time series, the effect of positive/negative serial correlation on the MK test is dependent upon sample size, magnitude of serial correlation, and magnitude of trend. When sample size and magnitude of trend are large enough, serial correlation no longer significantly affects the MK test statistics. Removal of positive AR(1) from time series by prewhitening will remove a portion of trend and hence reduces the possibility of rejecting the null hypothesis while it might be false. Contrarily, removal of negative AR(1) by prewhitening will inflate trend and leads to an increase in the possibility of rejecting the null hypothesis while it might be true. Therefore, prewhitening is not suitable for eliminating the effect of serial correlation on the MK test when trend exists within a time series.

Changes in the magnitude and frequency of droughts will have extensive impacts on water management, agriculture and aquatic ecosystems. With the projected global temperature increase, scientists generally agree that the global hydrological cycle will intensify and suggest that extremes will become or have already become more common. In this study, a pan-European dataset of more than 600 daily streamflow records from the European Water Archive (EWA) was analysed to detect spatial and temporal changes in streamflow droughts. Four different time periods were analysed. The focus was on hydrological droughts derived by applying the threshold level approach, which defines droughts as periods during which the streamflow is below a certain threshold. The Annual Maximum Series (AMS) of drought severity and the frequency of droughts in Partial Duration Series (PDS) were studied. Despite several reports on recent droughts in Europe, the non-parametric Mann-Kendall test and a resampling test for trend detection showed that it is not possible to conclude that drought conditions in general have become more severe or frequent. The period analysed and the selection of stations strongly influenced the regional pattern. For most stations, no significant changes were detected. However, distinct regional differences were found. Within the period 1962-1990 examples of increasing drought deficit volumes were found in Spain, the eastern part of Eastern Europe and in large parts of the UK, whereas decreasing drought deficit volumes occurred in large parts of Central Europe and in the western part of Eastern Europe. Trends in drought deficit volumes or durations could, to a large extent, be explained through changes in precipitation or artificial influences in the catchment. Changes in the number of drought events per year were determined by the combined effect of climate and catchment characteristics such as storage capacity. The importance of the time period chosen for trend analysis is illustrated using two very long time series.

The phenomenon of software aging refers to the accumulation of errors during the execution of the software which eventually results in it's crash/hang failure. A gradual performance degradation may also accompany software aging. Pro-active fault management techniques such as “software rejuvenation” (Y. Huang et al., 1995) may be used to counteract aging if it exists. We propose a methodology for detection and estimation of aging in the UNIX operating system. First, we present the design and implementation of an SNMP based, distributed monitoring tool used to collect operating system resource usage and system activity data at regular intervals, from networked UNIX workstations. Statistical trend detection techniques are applied to this data to detect/validate the existence of aging. For quantifying the effect of aging in operating system resources, we propose a metric: “estimated time to exhaustion”, which is calculated using well known slope estimation techniques. Although the distributed data collection tool is specific to UNIX, the statistical techniques can be used for detection and estimation of aging in other software as well

The annual timing of river flows is a good indicator of climate-related changes, or lack of changes, for rivers with long-term data that drain unregulated basins with stable land use. Changes in the timing of annual winter/spring (January 1 to May 31) and fall (October 1 to December 31) center of volume dates were analyzed for 27 rural, unregulated river gaging stations in New England, USA with an average of 68 years of record. The center of volume date is the date by which half of the total volume of water for a given period of time flows past a river gaging station, and is a measure of the timing of the bulk of flow within the time period. Winter/spring center of volume (WSCV) dates have become significantly earlier ðp , 0:1Þ at all 11 river gaging stations in areas of New England where snowmelt runoff has the most effect on spring river flows. Most of this change has occurred in the last 30 years with dates advancing by 1 -2 weeks. WSCV dates were correlated with March through April air temperatures ðr ¼ 20:72Þ and with January precipitation ðr ¼ 20:37Þ: Three of 16 river gaging stations in the remainder of New England had significantly earlier WSCV dates. Four out of 27 river gaging stations had significantly earlier fall center of volume dates in New England. Changes in the timing of winter/spring and fall peak flow dates were consistent with the changes in the respective center of volume dates, given the greater variability in the peak flow dates. Changes in the WSCV dates over the last 30 years are consistent with previous studies of New England last-frost dates, lilac bloom dates, lake ice-out dates, and spring air temperatures. This suggests that these New England spring geophysical and biological changes all were caused by a common mechanism, temperature increases. Published by Elsevier Science B.V.

The feared global climate change could have important effects on various environmental variables including rainfall in many countries around the world. Changes in precipitation regime directly affect water resources management, agriculture, hydrology and ecosystems. For this reason it is important to investigate the changes in the spatial and temporal rainfall pattern in order to improve water management strategies. In this study a non-parametric statistical method (Mann-Kendall rank correlation method) is employed in order to verify the existence of trend in annual, seasonal and monthly rainfall and the distribution of the rainfall during the year. This test is applied to about 250 rain gauge stations in Sicily (Italy) after a series of procedures finalized to the estimation of missing records and to the verification of data consistency. In order to understand the regional pattern of precipitation in Sicily, the detected trends are spatially interpolated using spatial analysis techniques in a GIS environment. The results show the existence of a generalized negative trend for the entire region.

Medicine. Recently the PGD team has shown higher pregnancy rates in women of advanced age undergoing PGD. This team has performed more than 850 PGD cycles for translocations and over 8500 PGD cycles for chromosome abnormalities related to advanced maternal age. Dr Munné has more than 100 publications to his name, and is a frequent lecturer, both nationally and internationally, on his team's work and the fi eld of preimplantation genetics.

Most investigations on the nature and effects of solar ultraviolet (UV) radiation at the Earth's surface require measurements of high accuracy combined with well-defined procedures to assess their quality. Here we present a general evaluation of all relevant errors and uncertainties associated with measurements of spectral global irradiance in the UV. The uncertainties are quantified in terms of dependence of the characteristics of the spectroradiometer, the uncertainty of calibration standards, the solar zenith angle, and atmospheric conditions. The methodologies and equations presented can be applied to most spectroradiometers currently employed for UV research. The sources of error addressed include radiometric calibration, cosine error, spectral resolution, wavelength misalignment, stability, noise, stray light, and timing errors. The practical application of the method is demonstrated by setting up a complete uncertainty table for the mobile spectroradiometer of the Fraunhofer Institute for Atmospheric Environmental Research (IFU). This instrument has successfully participated in several international intercomparisons of UV spectroradiometers. The expanded uncertainty (coverage factor k-2) for measurements of global spectral irradiance conducted with this instrument varies between 6.3% in the UVA and 12.7% at 300 nm and 60 ø solar zenith angle. The expanded uncertainties in erythemally and DNA weighted irradiances are 6.1% and 6.6%, respectively. These expanded uncertainties are comparable to uncertainties at the 2or level in conventional statistics. A substantial reduction of these uncertainties would require smaller uncertainties in the irradiance standards used to calibrate the instrument. Though uncertainties caused by wavelength misalignment and noise become prominent in the shortwave UVB, which is the most important spectral range for UV trend detection, the results indicate that the accuracy of the IFU radiometer is sufficient to detect long-term trends in UV arising from a 3% change in atmospheric ozone. The detection of trends caused by a 1% change in ozone may be beyond the capabilities of current instrumentation. 14,$22 BERNHARD AND SECKMEYER: SOLAR UV MEASUREMENT UNCERTAINTY tion through the atmosphere and trend detection. For the last point, in particular, high accuracy is required because trends in UV are expected to be small. The overall uncertainty of a UV measurement depends on the specifications of the instrumentation employed, the uncertainty in the calibration sources, and atmospheric conditions. All points will be addressed thoroughly in this paper. Though very important, the influence of the technical skills of the personnel operating the instruments will not be discussed. All uncertainties reported are consequently based on the assumption that instruments are carefully maintained and characterized. This includes the application of a quality control (QC) plan. In addition, instruments should participate in intercomparison campaigns to assure their quality and to detect systematic errors that would otherwise go undetected. Several such campaigns have been organized in the past in the flamework of national, European, and international research programs [e.g., Gat

Statistical trend analyses have been performed for monthly zonal average total ozone data from both TOMS and SBUV satellite sources and ground-based instruments over the period 1978-2002 for detection of a 'turnaround' in the previous downward trend behavior and hence evidence for the beginning of an ozone recovery. Since other climatic and geophysical changes can impact ozone behavior and can influence the detection of turnaround and recovery, we also focus on accounting for ozone variations that may be ascribed to various physical and chemical influences. Thus we include in the statistical trend modeling and analysis the effects of various dynamical and circulation variations in the atmosphere, including those associated with the quasibiennial oscillation (QBO), Arctic oscillation (AO) and Antarctic oscillation (AAO), and Eliassen-Palm (EP) flux influences, as well as influences of solar cycle. A notable result of the analysis is that for latitude zones of 40˚ and above in both hemispheres, large positive and significant estimates of a change in trend (since 1996) are obtained (on the order of 1.5 to 3 DU per year). The dynamic index series, AO/AAO and EP flux, are found to have a substantial influence on total ozone for these higher latitudes, and significant influences of lesser magnitude are also found for lower latitudes. The feature of positive significant change in trend in total ozone over recent years, however, is obtained both without and with the dynamical index terms included in the statistical models.

In order to investigate the behaviour of climatic and hydrological variables, several statistical and stochastic techniques are currently applied to time series. In the present study a statistical analysis of annual and seasonal precipitation has been performed over 109 cumulated rainfall series with more than 50 years of data observed in a region of Southern Italy (Calabria). Trend analyses have been made by using both nonparametric (Mann–Kendall test) and parametric (linear regression analysis) procedures. The long historical series of monthly rainfall data employed in this work have been previously processed through a pre-whitening (PW) technique in order to reduce the autocorrelation of rainfall series and its effects on outcomes of trend detection. The application of the above mentioned procedures has shown a decreasing trend for annual and winter–autumn rainfall and an increasing trend for summer precipitation. Moreover the Mann–Whitney test has been used to evidence the possible change points in the data. The higher percentages of rainfall series show possible year changes during decade 1960–1970 for almost all of the temporal aggregation rainfall. Copyright © 2009 Royal Meteorological Society

Abstract}We compared patterns of historical watershed nutrient inputs with in-river nutrient loads for the Neuse River, NC. Basin-wide sources of both nitrogen and phosphorus have increased substantially during the past century, marked by a sharp increase in the last 10 years resulting from an intensification of animal production. However, this recent increase is not reflected in changes in river loading over the last 20 years. Temporal patterns in river loads more closely parallel short-term changes in point sources and cropland nutrient application despite their overall lower magnitude. Total phosphorus loads have declined at all stations considered, corresponding to a 1988 phosphate detergent ban. Nitrogen load temporal patterns vary by location and the nitrogen fraction considered. The furthest upstream station exhibited nitrogen decreases after the completion of a dam in 1983. At a station just downstream of a rapidly growing urban area, the total nitrogen load has increased since the mid-1980s, primarily as a nitrate concentration increase. This is consistent with concurrent increases in chemical fertilizer use and point source discharges, as well as increased nitrification at treatment plants. This increase in nitrate loading is not reflected at the most downstream station, where no clear nitrogen trends are discernable. The lack of clear downstream nutrient increases suggests that current water quality impairment in the lower river and estuary may result from chronic nutrient overload rather than recent changes in the watershed. If this is true, then the impact of a planned 30% nitrogen loading reduction may not be immediately apparent. We calculate that, given annual variability, detecting a load reduction of this magnitude will take at least four years, and, should nutrients accumulated in the watershed become a significant source, detecting the resulting ecological improvements is likely to take substantially longer. #

compare several tests for linear trends in air quality time series. They found that low-pass (i.e., KZ filter) filtered data produced actual type I error rates much smaller than nominal when based on the use of the following equation adapted from : where ME is the 95% margin of error, t 0:975;f is the 0.975th percentile of Student's t distribution with f degrees-of-freedom, Y is the filtered time series, # Y is the least squares linear approximation to Y ; n is sample size and # r is the sample autocorrelation. We believe that the type I error rates for the filtered data in are much too small because the effective sample size used for the slope standard deviation calculation is not n À 2; as in Eq. , but # f: The expression for ME should therefore be Use of Eq. (2) will result in approximately nominal type I error rates for filtered data. The use of autocorrelations to compute degrees-offreedom for filtered time series is awkward in any case, more so for irregularly sampled time series, because sample autocorrelations for filtered data may be significant at very long lag times, and as such are difficult to estimate . Another way to estimate f is to notice that the reduction in degrees-of-freedom due to filtering is related to the amount of variance reduction. For the KZ filter, f ((n À 2)/variance reduction) is given approximately by where 2q þ 1 is the window size for the moving average and k is the number of iterations. Perhaps a better method for estimating slopes and confidence intervals is to use a filter designed to remove only seasonality. This can be accomplished using both a low-pass and a high-pass filter (i.e., a notch filter). For example, define an air quality time series as the sum of 3 components: where L t represents low-frequency variation, S seasonality, and W short-term (synoptic) variation. Estimate the slope and its standard deviation by applying classical least squares formulas to Y t : where # L t is a time series smoothed by a low-pass filter with a cut-off period of about 2 years, and # W t is the residual of a low-pass filter with a cut-off period of about 50 days. Use of a notch filter reduces the slope variance (by reducing seasonality) while retaining the high-frequency component simplifies computations, as for many air quality time series, Y t will more closely match the trend+white noise model assumed by classical least squares than will # L t : In other words, estimating the slope from Y t (the notch filtered time series) follows the same paradigm as the other methods examined by in that it only removes seasonality while retaining the effects of both long-term and short-term fluctuations on the slope estimates and, thus, the confidence intervals. This procedure was applied (although sometimes this was not explicitly stated) to estimate the trend confidence intervals in previous studies using the KZ filter (Milanchus et al.

1] This paper describes regional methods for assessing field significance and regional consistency for trend detection in hydrological extremes. Four procedures for assessing field significance are compared on the basis of Monte Carlo simulations. Then three regional tests, based on a regional variable, on the regional average Mann-Kendall test, and a new semiparametric approach, are tested. The latter was found to be the most adequate to detect consistent changes within homogeneous hydro-climatic regions. Finally, these procedures are applied to France, using daily discharge data arising from 195 gauging stations. No generalized change was found at the national scale on the basis of the field significance assessment of at-site results. Hydro-climatic regions were then defined, and the semiparametric procedure applied. Most of the regions showed no consistent change, but three exceptions were found: in the northeast flood peaks were found to increase, in the Pyrenees high and low flows showed decreasing trends, and in the Alps, earlier snowmelt-related floods were detected, along with less severe drought and increasing runoff due to glacier melting. The trend affecting floods in the northeast was compared to changes in rainfall, using rainfall-runoff simulation. The results showed flood trends consistent with the observed rainfall. Citation: Renard, B., et al. (2008), Regional methods for trend detection: Assessing field significance and regional consistency, Water Resour. Res., 44, W08419,

Urban change analysis has traditionally been supported through land use/cover classification and map-to-map comparison. In this research, we investigate the usefulness of satellitederived imperviousness index as an alternative for urban spatial growth characterization. The study area, Pensacola, FL, has witnessed considerable growth in population and regional economies during the past decade. The research consists of a number of procedures. First, we identify a method for landscape imperviousness estimation by synergistic use of medium-resolution satellite imagery and high-resolution color orthophoto through multivariate statistical analysis. We apply this method to map landscape imperviousness index for the years of 1989 and 2002, respectively. We assess the mapsÕ accuracy with the imperviousness estimation from high-resolution DOQQ imagery as the reference. The overall error is estimated to be less than 10%. Then, we analyze the spatio-temporal changing trend of landscape imperviousness index with the emphasis upon some ÔhotÕ spots of development areas. We find that this trend is compatible with the urban land use/cover changing trend detected through image interpretation. We conclude that satellite-derived landscape imperviousness index is able to serve as an invaluable alternative for quick and objective assessment of urban spatial growth, particularly over large areas.

Sediments of Last Glacial and Holocene age from two different locations in the basin of Lago di Pergusa have been palynologically and isotopically investigated. New data have been integrated with those available from previous researches carried out by the authors. This study indicates once more that Lago di Pergusa was and still is very sensitive to climate changes. In particular, during the Last Glacial, it played an important role in preserving the biodiversity as thermophilous and mesophilous taxa never completely disappeared. In particular, Zelkova pollen indicates that the distribution area of this tree (still living as a relic plant in south-eastern Sicily) was wider during the Last Glacial, where its last spread is found around 20,000 years BP. The d 18 O record, mirroring the arboreal concentration trend, confirms the climate inferences based on vegetation features. Both the Late Glacial and the Postglacial reforestation periods show slower vegetation dynamics and forest development than expected, with trends mirroring the slow and sustained decline of d 18 O record. The Younger Dryas oscillation is not pronounced in any of the lacustrine palaeoenvironmental proxies, its termination occurring around 11,100 cal. years BP. The opening of the woodland recorded by a slow trend ranging from the Neolithic (around 8000 cal. years BP) to the Eneolithic (few centuries before 4000 cal. years BP) seems to be a natural forest clearance and not due to human impact. Anomalies in pollen, microcharcoals and positive correlations between d 18 O and AP curves are found after a climate change occurring at 4500 years BP, and interpreted as a signal of human impact. The new d 18 O data presented in this paper throws new light to propose this hypothesis, confirming an early aridification trend detected from pollen, and a relatively late human impact inferred from fire dynamics in this central and montane region of Sicily. r

1] The CO 2 fertilization hypothesis stipulates that rising atmospheric CO 2 has a direct positive effect on net primary productivity (NPP), with experimental evidence suggesting a 23% growth enhancement with a doubling of CO 2 . Here, we test this hypothesis by comparing a bioclimatic model simulation of NPP over the twentieth century against tree growth increment (TGI) data of 192 Pinus banksiana trees from the Duck Mountain Provincial Forest in Manitoba, Canada. We postulate that, if a CO 2 fertilization effect has occurred, climatically driven simulations of NPP and TGI will diverge with increasing CO 2 . We use a two-level scaling approach to simulate NPP. A leaf-level model is first used to simulate high-frequency responses to climate variability. A canopy-level model of NPP is then adjusted to the aggregated leaf-level results and used to simulate yearly plot-level NPP. Neither model accounts for CO 2 fertilization. The climatically driven simulations of NPP for 1912-2000 are effective for tracking the measured year-to-year variations in TGI, with 47.2% of the variance in TGI reproduced by the simulation. In addition, the simulation reproduces without divergence the positive linear trend detected in TGI over the same period. Our results therefore do not support the attribution of a portion of the historical linear trend in TGI to CO 2 fertilization at the level suggested by current experimental evidence. A sensitivity analysis done by adding an expected CO 2 fertilization effect to simulations suggests that the detection limit of the study is for a 14% growth increment with a doubling of atmospheric CO 2 concentration.

This study investigates temporal evolution of 1-, 7-, 15-and 30-day annual and seasonal low· flow regimes of pristine river basins, included in the Canadian reference hydrometric basin network (RHBN), for three time frames : 1974-2003, 1964-2003 and 1954-2003. For the analysis, the RHBN stations are classified into three categories, which correspond to stations where annual low flows occur in winter only, summer only and both summer and winter seasons, respectively. Unlike in previous studies for the RHBN, such classification is essential to better understand and interpret the identified trends in low flow regimes in the RHBN. Nonparametric trend detection and bootstrap resampling approaches are used for the assessment of at-site temporal trends under the assumption of short-term persistence (STP). The results of the study demonstrate that previously suggested prewhitening and trend free pre-whitening approaches are inadequate in capturing the STP structure of low flow regimes compared to a bootstrap based approach. The analyses of 10 relatively longer records reveal that trends in low flow regimes exhibit fluctuating behavior and hence their temporal and spatial interpretations appear to be sensitive to the time frame chosen for the analysis. Furthermore, under the assumption of long-term persistence (LTP), which is a possible explanation for the fluctuating behavior of trends, many of the significant trends in low flow regimes, noted under the assumption of STP, become non-significant and their field significance also disappears. Therefore, correct identification of STP or LTP in time series of low flow regimes is very important as it has serious implications for the detection and interpretation of trends. hydrological trends in the presence of seriaI and cross correlations: review of the methodology and application to Canadian rivers, J. Hydrol. (in review) Khaliq, M.N., T.B.M.J. Ouarda, J.-C. Ondo, P. Gachon, and B. Bobée (2006), Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review, J Hydral., 329,534-552. Koutsoyiannis, D., and A. Montanari (2007), Statistical analysis ofhydroclimatic time series: Uncertainty and insights, Water Resour.

Nam Co Lake is the highest lake in the central Tibetan Plateau, and existing research on water storage and water level variations are lacking. This paper provides a method for estimating the lake water storage based on historical meteorological records from 1976 to 2009, remote sensing images scattered in this period, in situ bathymetric survey, and GIS techniques, and presents a comprehensive 34-year analysis of intra-annual and inter-annual variations of Nam Co Lake water storage. The multi-year mean water storage of Nam Co Lake is 842.36 Â 10 8 m 3 , with the maximum water depth of about 98 m. During 1976-2009, the lake water storage increased from 786.06 Â 10 8 m 3 to 870.30 Â 10 8 m 3 , with a tendency value of 2.67 Â 10 8 m 3 /a; the lake area enlarged from 1927.48 km 2 to 2015.12 km 2 , with a tendency value of 2.71 km 2 /a. The lake area fluctuations annually, increasing from April of each year until late September and early October, then decreasing until March of the next year. Climate change has a significant impact on the water storage variation of the lake. A general pattern of warming temperature is evident with the regional annual mean air temperature increasing significantly by 0.404 °C/10 a. Preliminary analysis indicates that the enlarging status of Nam Co Lake water storage is closely related to increasing of precipitation and stream runoff especially coming from the input of glacial meltwater. By combining this data with other research, it can be presented that under the trend of global warming, on Tibetan Plateau, the inland lakes which depend on the rainfall and river supply in the basin are shrinking, while the lakes which depend on glacial meltwater supply are enlarging. Climate change is an important factor promoting the lake variation.

The response of a selected number of chemical species is inspected with respect to climate change. The coupled Atmosphere-Ocean General Circulation Model ECHAM4-OPYC3 is providing meteorological fields for the Chemical long-range Transport Model DEHM. Three selected decades (1990s, 2040s and 2090s) are inspected. The 1990s 5 are used as a reference and validation period. In this decade an evaluation of the output from the DEHM model with ECHAM4-OPYC3 meteorology input data is carried out. The model results are tested against similar model simulations with MM5 meteorology and against observations from the EMEP monitoring sites in Europe.

The deep waters in the Western Mediterranean (N600 m) are the result of mixing between the two water masses above it (Atlantic Water, 0-200 m and Levantine Intermediate Water, 200-600 m) and heat and buoyancy losses in late winter. Deep waters in the Western Mediterranean have undergone a continuous warming during the second half of the twentieth century and initially it was hypothesized that this had been caused by the warming of the contributing water masses, very likely linked to global warming. Nevertheless, no clear signals of warming have been detected in the intermediate layers and no warming trends were detected in the upper layer before the 1980s. This fact suggested that the cause of deep water warming could be linked to river damming and the consequent salinity increase, instead of to an increase of the heat absorbed by the upper ocean, as in other parts of the world ocean. In this work we use the data base MEDATLAS and data from more recent monitoring programs to construct the longest temperature and salinity time series ever analysed in the Western Mediterranean (1900 to 2008). These time series show that both the upper and intermediate layers have warmed throughout the twentieth century. Long term and decadal variability in the upper layer correlate with surface air temperature in the northern hemisphere and heat absorbed by the upper North Atlantic Ocean, suggesting that the time series analysed in this work reflect the present heat absorption of the oceans in the context of global warming. The present data set highlights the importance of monitoring programs and provides a proxy for the study of climate change.

Sixteen water quality parameters have been monitored at four stations located along the Maroon River during 1989–2008. The trend analysis was performed on seasonal and annual timescales using the Mann–Kendall test, the Sen’s slope estimator and the linear regression. The relationships of the water quality parameters to river discharge were also investigated. The statistical methods showed both positive and negative trends in annual water quality data. However, significant trends were detected by the statistical methods only in calcium, magnesium, sodium absorption ratio, pH, and turbidity series. The results indicated that the concentrations of the water quality parameters increased in spring and winter seasons, while the concentrations were diluted in summer and autumn seasons in the last two decades. Moreover, the highest numbers of significant trends were found in the spring and summer series, respectively. According to the regression analysis, most of the water quality parameters were negatively correlated with river discharge.were negatively correlated with river discharge.

Multiple sclerosis (MS) is a common inflammatory disease of the central nervous system characterized by progressive neurological dysfunction. No curative therapy is currently available, and approximately 80-90% of afflicted individuals are ultimately disabled. Interferon beta (IFNb) has been shown to decrease clinical relapses, reduce brain disease activity, and possibly slow progression of disability. However, the overall effect of treatment is partial and a substantial number of patients are considered poor or nonresponders. For this report, we tested the pharmacogenomic effects of eight polymorphisms in the interferon receptor genes (IFNAR1 and IFNAR2) in a group of 147 patients undergoing open-label IFNb therapy. Overall, no significant differences in the distribution of responders and nonresponders, classified based on prospectively acquired primary and secondary clinical end points, were observed when stratified by any of the studied IFNAR gene polymorphisms. A trend detected with a single nucleotide polymorphism SNP 16469 (A/T) located at the third intron of the IFNAR1 gene, suggesting modest association with relapse-free status, will require confirmation in an independent data set. In addition, no significant association was observed of any of the IFNAR gene polymorphisms with susceptibility to MS, as studied by a family-based association analysis.

This study deals with temporal trends in the Penman-Monteith reference evapotranspiration estimated from standard meteorological observations, observed pan evaporation, and four related meteorological variables during 1970-2000 in the Yangtze River catchment. Relative contributions of the four meteorological variables to changes in the reference evapotranspiration are quantified. The results show that both the reference evapotranspiration and the pan evaporation have significant decreasing trends in the upper, the middle as well as in the whole Changjiang (Yangtze) River catchment at the 5% significance level, while the air temperature shows a significant increasing trend. The decreasing trend detected in the reference evapotranspiration can be attributed to the significant decreasing trends in the net radiation and the wind speed.

1] Total ozone trends are typically studied using linear regression models that assume a first-order autoregression of the residuals [so-called AR(1) models]. We consider total ozone time series over 60°S-60°N from 1979 to 2005 and show that most latitude bands exhibit long-range correlated (LRC) behavior, meaning that ozone autocorrelation functions decay by a power law rather than exponentially as in AR(1). At such latitudes the uncertainties of total ozone trends are greater than those obtained from AR(1) models and the expected time required to detect ozone recovery correspondingly longer. We find no evidence of LRC behavior in southern middle-and high-subpolar latitudes (45°-60°S), where the long-term ozone decline attributable to anthropogenic chlorine is the greatest. We thus confirm an earlier prediction based on an AR(1) analysis that this region (especially the highest latitudes, and especially the South Atlantic) is the optimal location for the detection of ozone recovery, with a statistically significant ozone increase attributable to chlorine likely to be detectable by the end of the next decade. In northern middle and high latitudes, on the other hand, there is clear evidence of LRC behavior. This increases the uncertainties on the long-term trend attributable to anthropogenic chlorine by about a factor of 1.5 and lengthens the expected time to detect ozone recovery by a similar amount (from $2030 to $2045). If the long-term changes in ozone are instead fit by a piecewise-linear trend rather than by stratospheric chlorine loading, then the strong decrease of northern middle-and high-latitude ozone during the first half of the 1990s and its subsequent increase in the second half of the 1990s projects more strongly on the trend and makes a smaller contribution to the noise. This both increases the trend and weakens the LRC behavior at these latitudes, to the extent that ozone recovery (according to this model, and in the sense of a statistically significant ozone increase) is already on the verge of being detected. The implications of this rather controversial interpretation are discussed.

Today most of our bedside decisions are based on subjective judgment and experience, rather than on hard data analysis. Most of the changes of a variable over time are more important than one pathological value at the time of observation. Over the past three decades mathematical methods have been developed that allow the assessment of single or multiple variables over time.

About two decades after the discovery of the amplitude decline of the light curve of the classical Cepheid Y Oph, its study is resumed using an increased amount of homogenized data and an extended time base. In our approach, the investigation of different time series concerning the light curve amplitude of Y Oph is not only the reason for the present study, but also a stimulus for developing a coherent methodology for studying long-and short-term variability phenomena in variable stars, taking into account the details of concrete observing conditions: amount of data, data sampling, time base, and individual errors of observational data. The statistical significance of this decreasing trend was estimated by assuming its linearity. We approached the decision-making process by formulating adequate null and alternative hypotheses, and testing the value of the regression line slope for different data sets via Monte Carlo simulations. A variability analysis, through various methods, of the original data and of the residuals obtained after removing the linear trend was performed. We also proposed a new statistical test, based on amplitude spectrum analysis and Monte Carlo simulations, intended to evaluate how detectible is a given (linear) trend in well-defined observing conditions: the trend detection probability. The main conclusion of our study on Y Oph is that, even if the false alarm probability is low enough to consider the decreasing trend to be statistically significant, the available data do not allow us to obtain a reasonably powerful test. We are able to confirm the light curve amplitude decline, and the order of magnitude of its slope with a better statistical substantiation. According to the obtained values of the trend detection probability, it seems that the trend we are dealing with is marked by a low detectibility. Our attempt to find signs of possible variability phenomena at shorter timescales ended by emphasizing the relative constancy of our data, within their precision limits.

To assess benthic macrofaunal community condition in Southern California, 838 sites were sampled using spatially random designs in 1994, 1998, or 2003. Benthic community condition was assessed on a fourcategory scale and the area in each category estimated. Overall, benthic macrofauna in Southern California were in good condition during 2003, with 98% of the area in reference condition or deviating only marginally. There was no evidence of disturbance near Channel Islands or small wastewater discharges, and virtually none on the mainland shelf. In contrast, bay and estuary macrofaunal communities were more frequently disturbed with nearly 13% of the area supporting disturbed benthos. The condition of the mainland shelf did not change substantially over the 9-year period, with 1.6-2.8% of the area in poor benthic condition. Southern California benthic condition evaluations may be improved by extending the depth and salinity ranges of assessment tools, and improving trend detection methods.

Many wildlife-monitoring programmes have long time series of species abundance that cannot be summarized adequately by linear trend lines. To describe long time series better, generalized additive models may be used to obtain a smooth trend line through abundance data. We describe another approach to estimate a smoothed trend line through time series consisting of one observation per time point, such as year or month. This method is based on structural time-series models in combination with the Kalman filter and is computerized in the TrendSpotter software. One of its strengths is the possibility to test changes in smoothed abundances between years, taking into account serial correlation. The trend method is applied in the Dutch Waterbird Monitoring Scheme (DWMS), a monitoring scheme for migrating and overwintering waterbirds. Taking the numbers of overwintering Greater Scaup (Aythia marila) in the Netherlands as an example, we demonstrate three applications of the method: (1) trend calculation and classification for each year in the time series, (2) assessing alerts for alarming population declines and (3) testing yearly abundance against a population threshold. We discuss the situations where TrendSpotter is to be preferred over other methods.

This paper presents a comparative performance analysis of various trend detection methods developed using fuzzy logic, statistical, regression, and wavelet techniques. The main contribution of this paper is the introduction of a new method that uses noise rejection fuzzy clustering to enhance the performance of trend detection methodologies. Furthermore, another contribution of this work is a comparative investigation that produced systematic guidelines for the selection of a proper trend detection method for different application requirements. Examples of representative physiological variables considered in this paper to examine the trend detection algorithms are: 1) blood pressure signals (diastolic and systolic); and 2) heartbeat rate based on RR intervals of electrocardiography signal. Furthermore, synthetic physiological data intentionally contaminated with various types of real-life noise has been generated and used to test the performance of trend detection methods and develop noise-insensitive trend-detection algorithms.

Demographic monitoring is a well established tool for conservation managers. But, monitoring programs typically do not offer methods to explore the relationship between projected population trends and the probability of trend detection. Moreover, conservation biologists need to evaluate and incorporate the effect of variability and habitat perturbation on the efficiency of monitoring programs.

Literature on the estimation and detection of linear trends in (environmental) time series is extensive, as overviewed by Hess and others in work published in 2001. However, not all data patterns behave in a linear fashion or, at least, a monotonous increasing or decreasing fashion. Therefore, it may be advantageous to analyse data with more flexible trends. Once flexibility is introduced the problem of detecting statistically significant increase/decrease becomes much more complicated: the trend may be alternating between being constant, decreasing or increasing. As a consequence, the problem of detecting significant increases or decreases cannot be summarised in one single figure or statistical test, as in the case of linear trend detection.

We determine the power of population monitoring in source or sink habitat to detect declining reproductive success in source habitat using a stochastic population model. The relative power to detect a trend in the source by monitoring either the source or the sink varies with life history parameters, environmental stochasticity, and observation uncertainty. The power to detect a decline monitoring either source or sink habitat is maximized when the reproductive surplus in the source is low. The power to detect a decline by monitoring the sink increases with increasing reproductive deficit in the sink. If environmental stochasticity in the source increases, the power in the sink goes down due to a lower signal-to-noise ratio. However, the power in the sink increases if environmental stochasticity is increased further, because increasing stochasticity reduces the geometric mean growth rate in the source. Intriguingly, it is often most efficient to monitor the sink even though the actual reproductive decline occurs in the source. If reproductive success is declining in both habitats, censusing the sink will always have higher power. However, the probability of Type 1 error is always higher in the sink. Our results clearly have implications for optimal population monitoring in source-sink landscapes.

The reunification of Germany led to dramatically reduced emissions of nitrogen (N) and phosphorus (P) to the environment. The aim of the present study was to examine how these exceptional decreases influenced the amounts of nutrients carried by the Elbe River to the North Sea. In particular, we attempted to extract anthropogenic signals from time series of riverine loads of nitrogen and phosphorus by developing a normalization technique that enabled removal of natural fluctuations caused by several weatherdependent variables. This analysis revealed several notable downward trends. The normalized loads of total-N and NO 3 -N exhibited an almost linear trend, even though the nitrogen surplus in agriculture dropped dramatically in 1990 and then slowly increased.

Social networking media generate huge content streams, which leverage, both academia and developers efforts in providing unbiased, powerful indications of users' opinion and interests. Here, we present Cloud4Trends, a framework for collecting and analyzing user generated content through microblogging and blogging applications, both separately and jointly, focused on certain geographical areas, towards the identification of the most significant topics using trend analysis techniques. The cloud computing paradigm appears to offer a significant benefit in order to make such applications viable considering that the massive data sizes produced daily impose the need of a scalable and powerful infrastructure. Cloud4Trends constitutes an efficient Cloud-based approach in order to solve the online trend tracking problem based on Web 2.0 sources. A detailed system architecture model is also proposed, which is largely based on a set of service modules developed within the VENUS-C research project to facilitate the deployment of research applications on Cloud infrastructures.

We examined statewide time series (1940s–2002) of mean length at ages 2, 3, and 4 for seven fish species sampled from Michigan and Wisconsin inland lakes for temporal trends. We used a components of variance approach to examine how total variation in mean length at age was partitioned into lake-to-lake, coherent temporal, ephemeral temporal, trend, and residual variation. Using these estimated variance components, we simulated the effects of different variance structures on the power to detect trends in mean length at age. Of the 42 data sets examined, only four demonstrated significant regional (statewide) trends: age 4 largemouth bass (Micropterus salmoides) from Wisconsin lakes increased about 0.7 mm·year–1 in mean length at age, and ages 2, 3, and 4 walleye (Sander vitreus) from Wisconsin lakes decreased between 0.5 and 0.9 mm·year–1 in mean length at age. The structure of variation differed substantially among data sets, and these differences strongly affected the power to dete...

1] There have been relatively few studies that have attempted to separate the effects of the different factors affecting the short-and long-term changes of UV radiation. The effect of ozone is fairly easily represented, but that of other factors (surface albedo, aerosols, clouds) is more complex. In this paper, we present a methodology that can account for the other effects as well. We have used this methodology to study what part of the short-and long-term variability of measured spectral UV data from Sodankylä, Finland (67°N), and Thessaloniki, Greece (40°N), is explained by each factor. It was found that the effect of ozone on the short-term variability of monthly mean irradiance can be almost as high as 100%, whereas on average it is about 35%. The corresponding impacts by clouds are typically smaller, 40% and 12%, respectively. During May the albedo-related effect is strongest at Sodankylä, being 21% at its maximum and 7% on average at monthly levels. The amplitude of the variability caused by ozone is much stronger at Sodankylä than at Thessaloniki, so a longer time series at the former place is needed to detect any possible long-term trend. In the Thessaloniki time series there is no significant ozone-related increase. In the summer data from Thessaloniki, however, there is a long-term increase, which is mainly caused by cloudiness. It was found that it is crucial to check the spectral data for any possible wavelength shift, if the long-term variability at a single wavelength is studied. Moreover, it was demonstrated that if the irradiance measurements of a given solar zenith angle (SZA) range are selected, the use of even the rather narrow band of 2°c an introduce an effect that hampers trend detection. In other words, it is important to remove the effect of SZA and normalize all the measurements to some constant SZA value.

High resolution δ18O and methane records over the last 2ka have been reconstructed from Dasuopu ice core recovered from the Himalayas. Analysis shows that the δ18O record correlates well with the Northern Hemispheric temperature, Dunde ice core record, and with temperature record in eastern China. The warming trend detected in δ18O record from the last century is similar to that during the Medieval warm period. There is a dramatic increasing in methane concentration in the Dasuopu ice core, which reached 1031 nmol ⋅ mol-1 in 1997. Moreover, methane concentration in the Dasuopu ice core is about 15%-20% higher than that in Antarctica and Greenland. There is a positive correlation between methane concentration and δ18O in Dasuopu ice core.

In order to create an effective article, having great content is essential. However, to achieve this, the writer needs to target a specific audience. A target audience refers to a group of readers that a writer intends to reach with his content. Defining a target audience is substantial because it has a direct effect on adjusting writing style and content of the article. Nowadays, writers rely solely on annotated attributes of articles, such as location and language to understand his/her audience. The aim of this work is to identify the audience attributes of articles , especially not-annotated attributes. Among others, this work focuses on the detection of three key audience attributes of related articles: age, gender, and personality. We compare between multiple machine learning classifiers to detect these attributes. Finally, we demonstrate a prototypical application that enables writers to run existing algorithms such as trend detection and showing related articles that are specific to a defined target audience based on the newly detected attributes.

A goal of the acidic deposition control program in the United States has been to link emissions control policies, such as those mandated under Title IV of the US Clean Air Act Amendments (CAAA) of 1990, to improvements in air and water quality. Recently, several researchers have reported trends in the time series of pollutant data in an effort to evaluate the effectiveness of the CAAA in reducing the acidic deposition problem. It is well known that pollutant concentrations are highly influenced by meteorological and climatic variations. Also, spatial and temporal inhomogeneities in time series of pollutant concentrations, induced by differences in the data collection, reduction, and reporting practices, can significantly affect the trend estimates. We present a method to discern breaks or discontinuities in the time series of pollutants stemming from emission reductions in the presence of meteorological and climatological variability. Using data from a few sites, this paper illustrates that linear trend estimates of concentrations of SO 2 , aerosol SO 2@ 4 , and precipitation-weighted SO 2@ 4 and NO @ 3 can be biased because of such complex features embedded in pollutant time series. r

We consider the problem of on-the-fly detection of temporal changes in the cognitive state of human subjects due to varying levels of difficulty of performed tasks using real-time EEG and EOG data. We construct the Cognitive State Indicator (CSI) as a function that projects the multidimensional EEG/EOG signals onto the interval OE0; 1 by maximizing the Kullback-Leibler distance between distributions of the signals, and whose values change continuously with variations in cognitive load. During offline testing (i.e. when evolution in time is disregarded) it was demonstrated that the CSI can serve as a statistically significant discriminator between states of different cognitive loads. In the online setting, a trend detection heuristic (TDH) has been proposed to detect real-time changes in the cognitive state by monitoring trends in the CSI. Our results support the application of the CSI and the TDH in future closed-loop control systems with human supervision.

The objective of this paper is to verify the applicability of the trend-free pre-whitening (TFPW) approach, developed by for positively correlated series, to negatively correlated series using similar Monte Carlo simulations. This study was initiated when a project on trend detection for streamflow and baseflow series across Canada revealed that a significant number of series had negative correlation coefficients. The TFPW procedure confirmed to be also well suited for negatively correlated series. This study also showed that the estimated values for slopes (trends) and correlation coefficients of the prewhitened series are affected by the sample size, especially for negatively correlated series.