Seasonal

Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine © Code Editor and cloud computing infrastructure. The use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated.

Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1-24 month) prediction of Large marine ecosystem properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.

Background: This study attempted to investigate traffic accident fatalities during 2004-2009 and the effect of fuel rationing on traffic accident fatalities .

  Materials and Methods: This was a cross-sectional study on available data of all traffic accident fatalities in Fars Province, Iran, during 2004-2009. To identify and fit the best model, various instruments, including the autocorrelation function (ACF), partial autocorrelation function (PACF), and Akaike information criterion (AIC), were used. To determine the accuracy of the model, the residual chart was evaluated in terms of independency, normality, and stationary. Then, the best model in terms of more accurate estimation was selected and analyzed using ITMS, Minitab, and Microsoft Excel software.

  Results: During 2004 to 2009, 12954 people died due to road traffic fatalities in Fars Province. Among them, 10442 (80%) were male, and mean age at death was 36 ± 20. The number of traffic accident fatalities in Fars Province reduced from 45.5 per 100000 in 2004 to 44.8 per 100000 in 2007 . After petroleum rationing, the traffic accident fatality average further reduced (to 42.2 per 100000 in 2009) . T here was a difference between the observed value and predicted value with two different predication methods, as the number of observed fatalities was less than the estimated numbers. Seasonal Holt-Winters and autoregressive moving average (ARMA) methods’ predications were a close estimation of the accident fatality rate after petroleum rationing, and the difference between their estimations was not considerable.

  Conclusions: Although the overall rate of fatalities reduced after petroleum rationing, variation in fatality rate did not show any association with petroleum rationing. However, decrease in fatalities had a seasonal pattern. Further decrease in road traffic fatalities requires reinforcement of educational programs as well as application of effective laws.