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Sample records for modeling daily precipitationat

  1. Modelling erosion on a daily basis

    NASA Astrophysics Data System (ADS)

    Pikha Shrestha, Dhruba; Jetten, Victor

    2016-04-01

    Effect of soil erosion causing negative impact on ecosystem services and food security is well known. To assess annual erosion rates various empirical models have been extensively used in all the climatic regions. While these models are simple to operate and do not require lot of input data, the effect of extreme rain is not taken into account in the annual estimations. For analysing the effects of extreme rain the event- based models become handy. These models can simulate detail erosional processes including particle detachment, transportation and deposition of sediments during a storm. But they are not applicable for estimating annual erosion rates. Moreover storm event data may not be available everywhere which prohibits their extensive use. In this paper we describe a method by adapting the revised MMF model to assess erosion on daily basis so that the effects of extreme rains are taken into account. We couple it to a simple surface soil moisture balance on a daily basis and include estimation of daily vegetation cover changes. Annual soil loss is calculated by adding daily erosion rates. We compare the obtained results with that obtained from applying the revised MMF model in a case study in the Mamora plateau in northwest Morocco which is affected by severe gully formation. The results show clearly the effects of exceptional rain in erosional processes which cannot be captured in an annual model.

  2. Global daily reference evapotranspiration modeling and evaluation

    USGS Publications Warehouse

    Senay, G.B.; Verdin, J.P.; Lietzow, R.; Melesse, Assefa M.

    2008-01-01

    Accurate and reliable evapotranspiration (ET) datasets are crucial in regional water and energy balance studies. Due to the complex instrumentation requirements, actual ET values are generally estimated from reference ET values by adjustment factors using coefficients for water stress and vegetation conditions, commonly referred to as crop coefficients. Until recently, the modeling of reference ET has been solely based on important weather variables collected from weather stations that are generally located in selected agro-climatic locations. Since 2001, the National Oceanic and Atmospheric Administration's Global Data Assimilation System (GDAS) has been producing six-hourly climate parameter datasets that are used to calculate daily reference ET for the whole globe at 1-degree spatial resolution. The U.S. Geological Survey Center for Earth Resources Observation and Science has been producing daily reference ET (ETo) since 2001, and it has been used on a variety of operational hydrological models for drought and streamflow monitoring all over the world. With the increasing availability of local station-based reference ET estimates, we evaluated the GDAS-based reference ET estimates using data from the California Irrigation Management Information System (CIMIS). Daily CIMIS reference ET estimates from 85 stations were compared with GDAS-based reference ET at different spatial and temporal scales using five-year daily data from 2002 through 2006. Despite the large difference in spatial scale (point vs. ???100 km grid cell) between the two datasets, the correlations between station-based ET and GDAS-ET were very high, exceeding 0.97 on a daily basis to more than 0.99 on time scales of more than 10 days. Both the temporal and spatial correspondences in trend/pattern and magnitudes between the two datasets were satisfactory, suggesting the reliability of using GDAS parameter-based reference ET for regional water and energy balance studies in many parts of the world

  3. Modeling maximum daily temperature using a varying coefficient regression model

    NASA Astrophysics Data System (ADS)

    Li, Han; Deng, Xinwei; Kim, Dong-Yun; Smith, Eric P.

    2014-04-01

    Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature. A good predictive model for daily maximum temperature is required because daily maximum temperature is an important measure for predicting survival of temperature sensitive fish. To appropriately model the strong relationship between water and air temperatures at a daily time step, it is important to incorporate information related to the time of the year into the modeling. In this work, a time-varying coefficient model is used to study the relationship between air temperature and water temperature. The time-varying coefficient model enables dynamic modeling of the relationship, and can be used to understand how the air-water temperature relationship varies over time. The proposed model is applied to 10 streams in Maryland, West Virginia, Virginia, North Carolina, and Georgia using daily maximum temperatures. It provides a better fit and better predictions than those produced by a simple linear regression model or a nonlinear logistic model.

  4. Time series ARIMA models for daily price of palm oil

    NASA Astrophysics Data System (ADS)

    Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

    2015-02-01

    Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

  5. Models for estimating daily rainfall erosivity in China

    NASA Astrophysics Data System (ADS)

    Xie, Yun; Yin, Shui-qing; Liu, Bao-yuan; Nearing, Mark A.; Zhao, Ying

    2016-04-01

    The rainfall erosivity factor (R) represents the multiplication of rainfall energy and maximum 30 min intensity by event (EI30) and year. This rainfall erosivity index is widely used for empirical soil loss prediction. Its calculation, however, requires high temporal resolution rainfall data that are not readily available in many parts of the world. The purpose of this study was to parameterize models suitable for estimating erosivity from daily rainfall data, which are more widely available. One-minute resolution rainfall data recorded in sixteen stations over the eastern water erosion impacted regions of China were analyzed. The R-factor ranged from 781.9 to 8258.5 MJ mm ha-1 h-1 y-1. A total of 5942 erosive events from one-minute resolution rainfall data of ten stations were used to parameterize three models, and 4949 erosive events from the other six stations were used for validation. A threshold of daily rainfall between days classified as erosive and non-erosive was suggested to be 9.7 mm based on these data. Two of the models (I and II) used power law functions that required only daily rainfall totals. Model I used different model coefficients in the cool season (Oct.-Apr.) and warm season (May-Sept.), and Model II was fitted with a sinusoidal curve of seasonal variation. Both Model I and Model II estimated the erosivity index for average annual, yearly, and half-month temporal scales reasonably well, with the symmetric mean absolute percentage error MAPEsym ranging from 10.8% to 32.1%. Model II predicted slightly better than Model I. However, the prediction efficiency for the daily erosivity index was limited, with the symmetric mean absolute percentage error being 68.0% (Model I) and 65.7% (Model II) and Nash-Sutcliffe model efficiency being 0.55 (Model I) and 0.57 (Model II). Model III, which used the combination of daily rainfall amount and daily maximum 60-min rainfall, improved predictions significantly, and produced a Nash-Sutcliffe model efficiency

  6. A nested multisite daily rainfall stochastic generation model

    NASA Astrophysics Data System (ADS)

    Srikanthan, Ratnasingham; Pegram, Geoffrey G. S.

    2009-06-01

    SummaryThis paper describes a nested multisite daily rainfall generation model which preserves the statistics at daily, monthly and annual levels of aggregation. A multisite two-part daily model is nested in multisite monthly, then annual models. A multivariate set of fourth order Markov chains is used to model the daily occurrence of rainfall; the daily spatial correlation in the occurrence process is handled by using suitably correlated uniformly distributed variates via a Normal Scores Transform (NST) obtained from a set of matched multinormal pseudo-random variates, following Wilks [Wilks, D.S., 1998. Multisite generalisation of a daily stochastic precipitation generation model. Journal of Hydrology 210, 178-191]; we call it a hidden covariance model. A spatially correlated two parameter gamma distribution is used to obtain the rainfall depths; these values are also correlated via a specially matched hidden multinormal process. For nesting, the generated daily rainfall sequences at all the sites are aggregated to monthly rainfall values and these values are modified by a set of lag-1 autoregressive multisite monthly rainfall models. The modified monthly rainfall values are aggregated to annual rainfall and these are then modified by a lag-1 autoregressive multisite annual model. This nesting process ensures that the daily, monthly and annual means and covariances are preserved. The model was applied to a region with 30 rainfall sites, one of the five sets reported by Srikanthan [Srikanthan, R., 2005. Stochastic Generation of Daily Rainfall Data at a Number of Sites. Technical Report 05/7, CRC for Catchment Hydrology. Monash University, 66p]. A comparison of the historical and generated statistics shows that the model preserves all the important characteristics of rainfall at the daily, monthly and annual time scales, including the spatial structure. There are some outstanding features that need to be improved: depths of rainfall on isolated wet days and

  7. Stochastic daily precipitation model with a heavy-tailed component

    NASA Astrophysics Data System (ADS)

    Neykov, N. M.; Neytchev, P. N.; Zucchini, W.

    2014-02-01

    Stochastic daily precipitation models are commonly used to generate scenarios of climate variability or change on a daily time scale. The standard models consist of two components describing the occurrence and intensity series, respectively. Binary logistic regression is used to fit the occurrence data, and the intensity series is modeled by a continuous-valued right-skewed distribution, such as gamma, Weibull or lognormal. The precipitation series is then modeled using the joint density and standard software for generalized linear models can be used to perform the computations. A drawback of these precipitation models is that they do not produce a sufficiently heavy upper tail for the distribution of daily precipitation amounts; they tend to underestimate the frequency of large storms. In this study we adapted the approach of Furrer and Katz (2008) based on hybrid distributions in order to correct for this shortcoming. In particular we applied hybrid gamma - generalized Pareto (GP) and hybrid Weibull-GP distributions to develop a stochastic precipitation model for daily rainfall at Ihtiman in western Bulgaria. We report the results of simulations designed to compare the models based on the hybrid distributions and those based on the standard distributions. Some potential difficulties are outlined.

  8. Stochastic daily precipitation model with a heavy-tailed component

    NASA Astrophysics Data System (ADS)

    Neykov, N. M.; Neytchev, P. N.; Zucchini, W.

    2014-09-01

    Stochastic daily precipitation models are commonly used to generate scenarios of climate variability or change on a daily timescale. The standard models consist of two components describing the occurrence and intensity series, respectively. Binary logistic regression is used to fit the occurrence data, and the intensity series is modeled using a continuous-valued right-skewed distribution, such as gamma, Weibull or lognormal. The precipitation series is then modeled using the joint density, and standard software for generalized linear models can be used to perform the computations. A drawback of these precipitation models is that they do not produce a sufficiently heavy upper tail for the distribution of daily precipitation amounts; they tend to underestimate the frequency of large storms. In this study, we adapted the approach of Furrer and Katz (2008) based on hybrid distributions in order to correct for this shortcoming. In particular, we applied hybrid gamma-generalized Pareto (GP) and hybrid Weibull-GP distributions to develop a stochastic precipitation model for daily rainfall at Ihtiman in western Bulgaria. We report the results of simulations designed to compare the models based on the hybrid distributions and those based on the standard distributions. Some potential difficulties are outlined.

  9. Statistical procedures for evaluating daily and monthly hydrologic model predictions

    USGS Publications Warehouse

    Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.

    2004-01-01

    The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.

  10. Coupling SWAT and ANN models for enhanced daily streamflow prediction

    NASA Astrophysics Data System (ADS)

    Noori, Navideh; Kalin, Latif

    2016-02-01

    To improve daily flow prediction in unmonitored watersheds a hybrid model was developed by combining a quasi-distributed watershed model and artificial neural network (ANN). Daily streamflow data from 29 nearby watersheds in and around the city of Atlanta, Southeastern United States, with leave-one-site-out jackknifing technique were used to build the flow predictive models during warm and cool seasons. Daily streamflow was first simulated with the Soil and Water Assessment Tool (SWAT) and then the SWAT simulated baseflow and stormflow were used as inputs to ANN. Out of the total 29 test watersheds, 62% and 83% of them had Nash-Sutcliffe values above 0.50 during the cool and warm seasons, respectively (considered good or better). As the percent forest cover or the size of test watershed increased, the performances of the models gradually decreased during both warm and cool seasons. This indicates that the developed models work better in urbanized watersheds. In addition, SWAT and SWAT Calibration Uncertainty Procedure (SWAT-CUP) program were run separately for each station to compare the flow prediction accuracy of the hybrid approach to SWAT. Only 31% of the sites during the calibration and 34% of validation runs had ENASH values ⩾0.50. This study showed that coupling ANN with semi-distributed models can lead to improved daily streamflow predictions in ungauged watersheds.

  11. Models of Deafness: Cochlear Implants in the Australian Daily Press

    ERIC Educational Resources Information Center

    Power, Des

    2005-01-01

    This article examined a database of Australian daily newspapers on the terms cochlear implant and deaf children to investigate how journalists and columnists report competing models of deafness: as either "medical" (deafness is a condition to be cured) or "sociocultural" (deafness provides a way of life to be lived). The results from the cochlear…

  12. A stochastic model for the analysis of maximum daily temperature

    NASA Astrophysics Data System (ADS)

    Sirangelo, B.; Caloiero, T.; Coscarelli, R.; Ferrari, E.

    2016-08-01

    In this paper, a stochastic model for the analysis of the daily maximum temperature is proposed. First, a deseasonalization procedure based on the truncated Fourier expansion is adopted. Then, the Johnson transformation functions were applied for the data normalization. Finally, the fractionally autoregressive integrated moving average model was used to reproduce both short- and long-memory behavior of the temperature series. The model was applied to the data of the Cosenza gauge (Calabria region) and verified on other four gauges of southern Italy. Through a Monte Carlo simulation procedure based on the proposed model, 105 years of daily maximum temperature have been generated. Among the possible applications of the model, the occurrence probabilities of the annual maximum values have been evaluated. Moreover, the procedure was applied for the estimation of the return periods of long sequences of days with maximum temperature above prefixed thresholds.

  13. A fuzzy-autoregressive model of daily river flows

    NASA Astrophysics Data System (ADS)

    Greco, R.

    2012-04-01

    A model for the identification of daily river flows has been developed, consisting of the combination of an autoregressive model with a fuzzy inference system. The AR model is devoted to the identification of base flow, supposed to be described by linear laws. The fuzzy model identifies the surface runoff, by applying a small set of linguistic statements, deriving from the knowledge of the physical features of the non linear rainfall-runoff transformation, to the inflow entering the river basin. The model has been applied to the identification of the daily flow series of river Volturno at Cancello-Arnone (Southern Italy), with a drainage basin of around 5560 km2, observed between 1970 and 1974. The inflow was estimated on the basis of daily precipitations registered during the same years at six rain gauges located throughout the basin. The first two years were used for model training, the remaining three for the validation. The obtained results show that the proposed model provides good predictions of either low river flows or high floods, although the analysis of residuals, which do not turn out to be a white noise, indicates that the cause and effect relationship between rainfall and runoff is not completely identified by the model.

  14. A fuzzy-autoregressive model of daily river flows

    NASA Astrophysics Data System (ADS)

    Greco, Roberto

    2012-06-01

    A model for the identification of daily river flows has been developed, consisting of the combination of an autoregressive model with a fuzzy inference system. The AR model is devoted to the identification of base flow, supposed to be described by linear laws. The fuzzy model identifies the surface runoff, by applying a small set of linguistic statements, deriving from the knowledge of the physical features of the nonlinear rainfall-runoff transformation, to the inflow entering the river basin. The model has been applied to the identification of the daily flow series of river Volturno at Cancello-Arnone (Southern Italy), with a drainage basin of around 5560 km2, observed between 1970 and 1974. The inflow was estimated on the basis of daily precipitations registered during the same years at six rain gauges located throughout the basin. The first two years were used for model training, the remaining three for the validation. The obtained results show that the proposed model provides good predictions of either low river flows or high floods, although the analysis of residuals, which do not turn out to be a white noise, indicates that the cause and effect relationship between rainfall and runoff is not completely identified by the model.

  15. Reconstruction of missing daily streamflow data using dynamic regression models

    NASA Astrophysics Data System (ADS)

    Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

    2015-12-01

    River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

  16. An empirical model of the quiet daily geomagnetic field variation

    USGS Publications Warehouse

    Yamazaki, Y.; Yumoto, K.; Cardinal, M.G.; Fraser, B.J.; Hattori, P.; Kakinami, Y.; Liu, J.Y.; Lynn, K.J.W.; Marshall, R.; McNamara, D.; Nagatsuma, T.; Nikiforov, V.M.; Otadoy, R.E.; Ruhimat, M.; Shevtsov, B.M.; Shiokawa, K.; Abe, S.; Uozumi, T.; Yoshikawa, A.

    2011-01-01

    An empirical model of the quiet daily geomagnetic field variation has been constructed based on geomagnetic data obtained from 21 stations along the 210 Magnetic Meridian of the Circum-pan Pacific Magnetometer Network (CPMN) from 1996 to 2007. Using the least squares fitting method for geomagnetically quiet days (Kp ??? 2+), the quiet daily geomagnetic field variation at each station was described as a function of solar activity SA, day of year DOY, lunar age LA, and local time LT. After interpolation in latitude, the model can describe solar-activity dependence and seasonal dependence of solar quiet daily variations (S) and lunar quiet daily variations (L). We performed a spherical harmonic analysis (SHA) on these S and L variations to examine average characteristics of the equivalent external current systems. We found three particularly noteworthy results. First, the total current intensity of the S current system is largely controlled by solar activity while its focus position is not significantly affected by solar activity. Second, we found that seasonal variations of the S current intensity exhibit north-south asymmetry; the current intensity of the northern vortex shows a prominent annual variation while the southern vortex shows a clear semi-annual variation as well as annual variation. Thirdly, we found that the total intensity of the L current system changes depending on solar activity and season; seasonal variations of the L current intensity show an enhancement during the December solstice, independent of the level of solar activity. Copyright 2011 by the American Geophysical Union.

  17. An empirical model of the quiet daily geomagnetic field variation

    NASA Astrophysics Data System (ADS)

    Yamazaki, Y.; Yumoto, K.; Cardinal, M. G.; Fraser, B. J.; Hattori, P.; Kakinami, Y.; Liu, J. Y.; Lynn, K. J. W.; Marshall, R.; McNamara, D.; Nagatsuma, T.; Nikiforov, V. M.; Otadoy, R. E.; Ruhimat, M.; Shevtsov, B. M.; Shiokawa, K.; Abe, S.; Uozumi, T.; Yoshikawa, A.

    2011-10-01

    An empirical model of the quiet daily geomagnetic field variation has been constructed based on geomagnetic data obtained from 21 stations along the 210 Magnetic Meridian of the Circum-pan Pacific Magnetometer Network (CPMN) from 1996 to 2007. Using the least squares fitting method for geomagnetically quiet days (Kp ≤ 2+), the quiet daily geomagnetic field variation at each station was described as a function of solar activity SA, day of year DOY, lunar age LA, and local time LT. After interpolation in latitude, the model can describe solar-activity dependence and seasonal dependence of solar quiet daily variations (S) and lunar quiet daily variations (L). We performed a spherical harmonic analysis (SHA) on these S and L variations to examine average characteristics of the equivalent external current systems. We found three particularly noteworthy results. First, the total current intensity of the S current system is largely controlled by solar activity while its focus position is not significantly affected by solar activity. Second, we found that seasonal variations of the S current intensity exhibit north-south asymmetry; the current intensity of the northern vortex shows a prominent annual variation while the southern vortex shows a clear semi-annual variation as well as annual variation. Thirdly, we found that the total intensity of the L current system changes depending on solar activity and season; seasonal variations of the L current intensity show an enhancement during the December solstice, independent of the level of solar activity.

  18. Daily reservoir inflow forecasting combining QPF into ANNs model

    NASA Astrophysics Data System (ADS)

    Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian

    2009-01-01

    Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

  19. Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

    NASA Technical Reports Server (NTRS)

    Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

    2013-01-01

    The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

  20. Spatial and temporal modeling of daily pollen concentrations

    NASA Astrophysics Data System (ADS)

    Dellavalle, Curt T.; Triche, Elizabeth W.; Bell, Michelle L.

    2012-01-01

    Accurate assessments of pollen counts are valuable to allergy sufferers, the medical industry, and health researchers; however, monitoring stations do not exist in most areas. In addition, the degree of spatial reliability provided by the limited number of monitoring stations is poorly understood. We developed and compared spatial models to estimate pollen concentrations in locations without monitoring stations. Daily Acer, Quercus, and overall tree, grass, and weed pollen counts, in grains/m3, were obtained from 14 aeroallergen monitoring stations located in the northeastern and mid-Atlantic region of the United States from 2003 to 2006. Pollen counts were spatially interpolated using ordinary kriging. Mixed effects and generalized estimating equations incorporating daily and seasonal weather characteristics, pollen season characteristics and land-cover information were also developed to estimate daily pollen concentrations. We then compared observed values from a monitoring station to model estimates for that location. Observed counts and kriging estimates for tree pollen differed ( p = 0.04), but not when peak periods were removed ( p = 0.29). No differences between observed and kriging estimates of Acer ( p = 0.46), Quercus ( p = 0.24), grass ( p = 0.31) or weed pollen ( p = 0.29) were found. Estimates from longitudinal models also demonstrated good agreement with observed counts, except for the extremes of pollen distributions. Our results demonstrate that spatial interpolation techniques as well as regression methods incorporating both weather and land-cover characteristics can provide reliable estimates of daily pollen concentrations in areas where monitors do not exist for all but periods of extremely high pollen.

  1. Retrospective forecast of ETAS model with daily parameters estimate

    NASA Astrophysics Data System (ADS)

    Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang

    2016-04-01

    We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.

  2. A clonal selection algorithm model for daily rainfall data prediction.

    PubMed

    Noor Rodi, N S; Malek, M A; Ismail, Amelia Ritahani; Ting, Sie Chun; Tang, Chao-Wei

    2014-01-01

    This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction. PMID:25429452

  3. Statistical Modeling of Daily Stream Temperature for Mitigating Fish Mortality

    NASA Astrophysics Data System (ADS)

    Caldwell, R. J.; Rajagopalan, B.

    2011-12-01

    Water allocations in the Central Valley Project (CVP) of California require the consideration of short- and long-term needs of many socioeconomic factors including, but not limited to, agriculture, urban use, flood mitigation/control, and environmental concerns. The Endangered Species Act (ESA) ensures that the decision-making process provides sufficient water to limit the impact on protected species, such as salmon, in the Sacramento River Valley. Current decision support tools in the CVP were deemed inadequate by the National Marine Fisheries Service due to the limited temporal resolution of forecasts for monthly stream temperature and fish mortality. Finer scale temporal resolution is necessary to account for the stream temperature variations critical to salmon survival and reproduction. In addition, complementary, long-range tools are needed for monthly and seasonal management of water resources. We will present a Generalized Linear Model (GLM) framework of maximum daily stream temperatures and related attributes, such as: daily stream temperature range, exceedance/non-exceedance of critical threshold temperatures, and the number of hours of exceedance. A suite of predictors that impact stream temperatures are included in the models, including current and prior day values of streamflow, water temperatures of upstream releases from Shasta Dam, air temperature, and precipitation. Monthly models are developed for each stream temperature attribute at the Balls Ferry gauge, an EPA compliance point for meeting temperature criteria. The statistical framework is also coupled with seasonal climate forecasts using a stochastic weather generator to provide ensembles of stream temperature scenarios that can be used for seasonal scale water allocation planning and decisions. Short-term weather forecasts can also be used in the framework to provide near-term scenarios useful for making water release decisions on a daily basis. The framework can be easily translated to other

  4. Spatial interpolation schemes of daily precipitation for hydrologic modeling

    USGS Publications Warehouse

    Hwang, Y.; Clark, M.; Rajagopalan, B.; Leavesley, G.

    2012-01-01

    Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

  5. Modelling sub-daily evaporation from a small reservoir.

    NASA Astrophysics Data System (ADS)

    McGloin, Ryan; McGowan, Hamish; McJannet, David; Burn, Stewart

    2013-04-01

    Accurate quantification of evaporation from small water storages is essential for water management and is also required as input in some regional hydrological and meteorological models. Global estimates of the number of small storages or lakes (< 0.1 kilometers) are estimated to be in the order of 300 million (Downing et al., 2006). However, direct evaporation measurements at small reservoirs using the eddy covariance or scintillometry techniques have been limited due to their expensive and complex nature. To correctly represent the effect that small water bodies have on the regional hydrometeorology, reliable estimates of sub-daily evaporation are necessary. However, evaporation modelling studies at small reservoirs have so far been limited to quantifying daily estimates. In order to ascertain suitable methods for accurately modelling hourly evaporation from a small reservoir, this study compares evaporation results measured by the eddy covariance method at a small reservoir in southeast Queensland, Australia, to results from several modelling approaches using both over-water and land-based meteorological measurements. Accurate predictions of hourly evaporation were obtained by a simple theoretical mass transfer model requiring only over-water measurements of wind speed, humidity and water surface temperature. An evaporation model that was recently developed for use in small reservoir environments by Granger and Hedstrom (2011), appeared to overestimate the impact stability had on evaporation. While evaporation predictions made by the 1-dimensional hydrodynamics model, DYRESM (Dynamic Reservoir Simulation Model) (Imberger and Patterson, 1981), showed reasonable agreement with measured values. DYRESM did not show any substantial improvement in evaporation prediction when inflows and out flows were included and only a slighter better correlation was shown when over-water meteorological measurements were used in place of land-based measurements. Downing, J. A., Y. T

  6. Modeling daily streamflow at ungauged catchments: What information is necessary?

    NASA Astrophysics Data System (ADS)

    Patil, S.; Stieglitz, M.

    2011-12-01

    Streamflow modeling at ungauged catchments involves transfer of information (viz., model structure and parameters) from gauged to ungauged catchments that are judged to be hydrologically similar. In this study, we focus on identifying: (1) what constitutes the critical information that needs to be transferred among hydrologically similar catchments to achieve good predictability using models at ungauged sites, and (2) which is the best approach for transferring this information from gauged to ungauged catchments. We develop a simple hydrologic model with minimal calibration requirement and implement it over 756 catchments located across the continental United States. The model computes water balance at a daily time-step and conceptualizes subsurface runoff through a storage-dependent exponential decline in saturated hydraulic conductivity. Snow accumulation and melt are modeled using the thermal degree-day concept. The calibrated model performs better in humid runoff-dominated regions than in the drier evapotranspiration-dominated regions. Results show that within a region, transfer of hydrograph recession information alone is sufficient for reliable streamflow predictions at ungauged catchments. Information transfer from spatially proximate gauged catchments provides better streamflow predictability at ungauged catchments than transfer from catchments identified as physically similar. When considering spatially proximate catchments, information transfer from multiple donor catchments is preferable to transfer from a single donor catchment.

  7. Estimating daily SWE from daily snow depth observations: a time series perspective on modeling snow bulk density

    NASA Astrophysics Data System (ADS)

    McCreight, J. L.; Small, E. E.; Larson, K. M.

    2013-12-01

    Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We motivate and develop a new model of bulk density for the daily timestep and demonstrate its improved skill over the existing models. Snow depth and density are anticorrleated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed daily snow depth (long time scales) and the observed positive and negative anomalies from the smoothed timeseries (short timescales) as separate terms. A climatology of fit is also included as a predictor variable. Over a half-million, daily observations of depth and SWE at 345 SNOTEL sites are used to train and evaluate model performance in over 3300 water years. For each location, we train the three models to the neighboring stations within 70km, transfer the parameters to the location to be modeled, and evaluate modeled timeseries against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density RMSE by 9.6% and 4.2% compared to previous models while increasing R2 from .46 to .52 to .56 across models. Removing the challenge of parameter transfer increases these R2 scores considerably to .58, .66, and .75. Our model shows general improvement over existing models when data are more frequent than once every 5 days and at least 3 stations are available for training. Results are applied to estimate SWE at over 100 locations on NSF's Earthscope Plate Boundary Observatory where geodetic grade GPS is currently used to measure snow depth. SWE and density observations

  8. A HIERARCHIAL STOCHASTIC MODEL OF LARGE SCALE ATMOSPHERIC CIRCULATION PATTERNS AND MULTIPLE STATION DAILY PRECIPITATION

    EPA Science Inventory

    A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. our algorithms are invested for classification of daily weather states; k means, fuzzy clustering, principal components, and principal components coupled with ...

  9. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    NASA Technical Reports Server (NTRS)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  10. Modeling daily soil temperature using data-driven models and spatial distribution

    NASA Astrophysics Data System (ADS)

    Kim, Sungwon; Singh, Vijay P.

    2014-11-01

    The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for estimating daily soil temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily soil temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily soil temperature successfully in this study.

  11. One-day offset in daily hydrologic modeling: An exploration of the issue in automatic model calibration

    NASA Astrophysics Data System (ADS)

    Asadzadeh, Masoud; Leon, Luis; Yang, Wanhong; Bosch, David

    2016-03-01

    Hydrologic modeling literature illustrates that daily simulation models are incapable of accurately representing hydrograph timing due to relationships between precipitation and watershed hydrologic response that happen with a sub-daily time step in the real world. For watersheds with a time of concentration less than 24 h and a late day precipitation event, the observed hydrographic response frequently occurs one day after the precipitation peak while the model simulates a same day event. The analysis of sub-daily precipitation and runoff in this study suggests that, this one-day offset is inevitable in daily analysis of the precipitation-runoff relationship when the same 24-h time interval, e.g. the calendar day, is used to prepare daily precipitation and runoff datasets. Under these conditions, daily simulation models will fail to emulate this one-day offset issue (1dOI) and result in significant daily residuals between simulated and measured hydrographs. Results of this study show that the automatic calibration of such daily models will be misled by model performance metrics that are based on the aggregation of daily residuals to a solution that systematically underestimate the peak flow rates while trying to emulate the one-day lags. In this study, a novel algorithm called Shifting Hydrograph In order to Fix Timing (SHIFT) is developed to reduce the impact of this one-day offset issue (1dOI) on the parameter estimation of daily simulation models. Results show that with SHIFT the aforementioned automatic calibration finds a solution that accurately estimates the magnitude of daily peak flow rates and the shape of the rising and falling limbs of the daily hydrograph. Moreover, it is shown that this daily calibrated model performs quite well with an alternative daily precipitation dataset that has a minimal number of 1dOIs, concluding that SHIFT can minimize the impact of 1dOI on parameter estimation of daily simulation models.

  12. Measuring Disability: Application of the Rasch Model to Activities of Daily Living (ADL/IADL).

    ERIC Educational Resources Information Center

    Sheehan, T. Joseph; DeChello, Laurie M.; Garcia, Ramon; Fifield, Judith; Rothfield, Naomi; Reisine, Susan

    2001-01-01

    Performed a comparative analysis of Activities of Daily Living (ADL) items administered to 4,430 older adults and Instrumental Activities of Daily Living administered to 605 people with rheumatoid arthritis scoring both with Likert and Rasch measurement models. Findings show the superiority of the Rasch approach over the Likert method. (SLD)

  13. Modeling daily flow patterns individuals to characterize disease spread

    SciTech Connect

    Smallwood, J.; Hyman, J. M.; Mirchandani, Pitu B.

    2002-11-17

    The effect of an individual's travels throughout a day on the spread of disease is examined using a deterministic SIR model. We determine which spatial and demographic characteristics most contribute to the disease spread and whether the progression of the disease can be slowed by appropriate vaccination of people belonging to a specific location-type.

  14. Models for estimating daily rainfall erosivity in China

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The multiplication of rainfall energy and maximum 30 minutes intensity (EI30) is the most widely used rainfall erosivity index for empirical soil loss prediction models, however its calculation requires high temporal resolution rainfall data which are often not readily available in China in most loc...

  15. Multilevel Factor Analysis and Structural Equation Modeling of Daily Diary Coping Data: Modeling Trait and State Variation

    ERIC Educational Resources Information Center

    Roesch, Scott C.; Aldridge, Arianna A.; Stocking, Stephanie N.; Villodas, Feion; Leung, Queenie; Bartley, Carrie E.; Black, Lisa J.

    2010-01-01

    This study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a comparison of the predictive ability of these trait/state factors. Daily diary data were collected on a large (n =…

  16. One-day offset in daily hydrologic modeling: An exploration of the issue in automatic model calibration

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The literature of daily hydrologic modelling illustrates that daily simulation models are incapable of accurately representing hydrograph timing due to relationships between precipitation and watershed hydrologic response. For watersheds with a time of concentration less than 24 hrs and a late day p...

  17. Modeling daily realized futures volatility with singular spectrum analysis

    NASA Astrophysics Data System (ADS)

    Thomakos, Dimitrios D.; Wang, Tao; Wille, Luc T.

    2002-09-01

    Using singular spectrum analysis (SSA), we model the realized volatility and logarithmic standard deviations of two important futures return series. The realized volatility and logarithmic standard deviations are constructed following the methodology of Andersen et al. [J. Am. Stat. Ass. 96 (2001) 42-55] using intra-day transaction data. We find that SSA decomposes the volatility series quite well and effectively captures both the market trend (accounting for about 34-38% of the total variance in the series) and, more importantly, a number of underlying market periodicities. Reliable identification of any periodicities is extremely important for options pricing and risk management and we believe that SSA can be a useful addition to the financial practitioners’ toolbox.

  18. The development of a regional geomagnetic daily variation model using neural networks

    NASA Astrophysics Data System (ADS)

    Sutcliffe, P. R.

    2000-01-01

    Global and regional geomagnetic field models give the components of the geomagnetic field as functions of position and epoch; most utilise a polynomial or Fourier series to map the input variables to the geomagnetic field values. The only temporal variation generally catered for in these models is the long term secular variation. However, there is an increasing need amongst certain users for models able to provide shorter term temporal variations, such as the geomagnetic daily variation. In this study, for the first time, artificial neural networks (ANNs) are utilised to develop a geomagnetic daily variation model. The model developed is for the southern African region; however, the method used could be applied to any other region or even globally. Besides local time and latitude, input variables considered in the daily variation model are season, sunspot number, and degree of geomagnetic activity. The ANN modelling of the geomagnetic daily variation is found to give results very similar to those obtained by the synthesis of harmonic coefficients which have been computed by the more traditional harmonic analysis of the daily variation.

  19. SELECTION OF CANDIDATE EUTROPHICATION MODELS FOR TOTAL MAXIMUM DAILY LOADS ANALYSES

    EPA Science Inventory

    A tiered approach was developed to evaluate candidate eutrophication models to select a common suite of models that could be used for Total Maximum Daily Loads (TMDL) analyses in estuaries, rivers, and lakes/reservoirs. Consideration for linkage to watershed models and ecologica...

  20. Daily radiation model for use in the simulation of passive solar buildings

    SciTech Connect

    Sillman, S.; Wortman, D.

    1981-04-01

    A model is presented to characterize solar radiation with just three input parameters for each day. This compressed daily radiation data may be used in place of hourly data in simulations of passive solar buildings. This method is tested with the SUNCAT passive simulation. Global horizontal and direct normal radiation data are input using the compressed daily form instead of by hour. Simulation results are found to be comparable to results based on hourly radiation data.

  1. Modeling bulk density and snow water equivalent using daily snow depth observations.

    NASA Astrophysics Data System (ADS)

    McCreight, J. L.; Small, E. E.

    2013-10-01

    Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily timestep and demonstrate its improved skill over the existing models. Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long time scales) and the observed positive and negative anomalies from the smoothed timeseries (short timescales) as separate terms. A climatology of fit is also included as a predictor variable. Over a half-million, daily observations of depth and SWE at 345 SNOTEL sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled timeseries against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density RMSE by 9.6% and 4.2% compared to previous models. Similarly, R2 increases from 0.46 to 0.52 to 0.56 across models. Removing the challenge of parameter transfer increases R2 scores for both the existing and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over the existing models when data are more frequent than once every 5 days and at least 3 stations are available for training.

  2. Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers.

    PubMed

    Rajaee, Taher

    2011-07-01

    In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values. PMID:21546062

  3. Evaluation of a watershed model for estimating daily flow using limited flow measurements

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Soil and Water Assessment Tool (SWAT) model was evaluated for estimation of continuous daily flow based on limited flow measurements in the Upper Oyster Creek (UOC) watershed. SWAT was calibrated against limited measured flow data and then validated. The Nash-Sutcliffe model Efficiency (NSE) and...

  4. Estimation of instantaneous peak flow from simulated maximum daily flow using the HBV model

    NASA Astrophysics Data System (ADS)

    Ding, Jie; Haberlandt, Uwe

    2014-05-01

    Instantaneous peak flow (IPF) data are the foundation of the design of hydraulic structures and flood frequency analysis. However, the long discharge records published by hydrological agencies contain usually only average daily flows which are of little value for design in small catchments. In former research, statistical analysis using observed peak and daily flow data was carried out to explore the link between instantaneous peak flow (IPF) and maximum daily flow (MDF) where the multiple regression model is proved to have the best performance. The objective of this study is to further investigate the acceptability of the multiple regression model for post-processing simulated daily flows from hydrological modeling. The model based flood frequency analysis allows to consider change in the condition of the catchments and in climate for design. Here, the HBV model is calibrated on peak flow distributions and flow duration curves using two approaches. In a two -step approach the simulated MDF are corrected with a priory established regressions. In a one-step procedure the regression coefficients are calibrated together with the parameters of the model. For the analysis data from 18 mesoscale catchments in the Aller-Leine river basin in Northern Germany are used. The results show that: (1) the multiple regression model is capable to predict the peak flows with the simulated MDF data; (2) the calibrated hydrological model reproduces well the magnitude and frequency distribution of peak flows; (3) the one-step procedure outperforms the two-step procedure regarding the estimation of peak flows.

  5. Spatial temporal disaggregation of daily rainfall from a generalized linear model

    NASA Astrophysics Data System (ADS)

    Segond, M.-L.; Onof, C.; Wheater, H. S.

    2006-12-01

    SummaryThis paper describes a methodology for continuous simulation of spatially-distributed hourly rainfall, based on observed data from a daily raingauge network. Generalized linear models (GLMs), which can represent the spatial and temporal non-stationarities of multi-site daily rainfall (Chandler, R.E., Wheater, H.S., 2002. Analysis of rainfall variability using generalised linear models: a case study from the west of Ireland. Water Resources Research, 38 (10), 1192. doi:10.1029/2001WR000906), are combined with a single-site disaggregation model based on Poisson cluster processes (Koutsoyiannis, D., Onof, C., 2001. Rainfall disaggregation using adjusting procedures on a Poisson cluster model. Journal of Hydrology 246, 109-122). The resulting sub-daily temporal profile is then applied linearly to all sites over the catchment to reproduce the spatially-varying daily totals. The method is tested for the River Lee catchment, UK, a tributary of the Thames covering an area of 1400 km 2. Twenty simulations of 12 years of hourly rainfall are generated at 20 sites and compared with the historical series. The proposed model preserves most standard statistics but has some limitations in the representation of extreme rainfall and the correlation structure. The method can be extended to sites within the modelled region not used in the model calibration.

  6. Modelling the atmospheric boundary layer for remotely sensed estimates of daily evaporation

    NASA Technical Reports Server (NTRS)

    Gurney, R. J.; Blyth, K.; Camillo, P. J.

    1984-01-01

    An energy and moisture balance model of the soil surface was used to estimate daily evaporation from wheat and barley fields in West Germany. The model was calibrated using remotely sensed surface temperature estimates. Complete atmospheric boundary layer models are difficult to use because of the number of parameters involved and a simplified model was used here. The resultant evaporation estimates were compared to eddy correlation evaporation estimates and good agreement was found.

  7. Modeling turbidity and flow at daily steps in karst using ARIMA/ARFIMA-GARCH error models

    NASA Astrophysics Data System (ADS)

    Massei, N.

    2013-12-01

    Hydrological and physico-chemical variations recorded at karst springs usually reflect highly non-linear processes and the corresponding time series are then very often also highly non-linear. Among others, turbidity, as an important parameter regarding water quality and management, is a very complex response of karst systems to rain events, involving direct transfer of particles from point-source recharge as well as resuspension of particles previously deposited and stored within the system. For those reasons, turbidity modeling has not been well taken in karst hydrological models so far. Most of the time, the modeling approaches would involve stochastic linear models such ARIMA-type models and their derivatives (ARMA, ARMAX, ARIMAX, ARFIMA...). Yet, linear models usually fail to represent well the whole (stochastic) process variability, and their residuals still contain useful information that can be used to either understand the whole variability or to enhance short-term predictability and forecasting. Model residuals are actually not i.i.d., which can be identified by the fact that squared residuals still present clear and significant serial correlation. Indeed, high (low) amplitudes are followed in time by high (low) amplitudes, which can be seen on residuals time series as periods of time during which amplitudes are higher (lower) then the mean amplitude. This is known as the ARCH effet (AutoRegressive Conditional Heteroskedasticity), and the corresponding non-linear process affecting residuals of a linear model can be modeled using ARCH or generalized ARCH (GARCH) non-linear modeling, which approaches are very well known in econometrics. Here we investigated the capability of ARIMA-GARCH error models to represent a ~20-yr daily turbidity time series recorded at a karst spring used for water supply of the city of Le Havre (Upper Normandy, France). ARIMA and ARFIMA models were used to represent the mean behavior of the time series and the residuals clearly

  8. Estimation of instantaneous peak flow from daily data using the HBV model

    NASA Astrophysics Data System (ADS)

    Ding, Jie; Haberlandt, Uwe

    2015-04-01

    The length of the observed instantaneous peak flow (IPF) period has a great influence on the flood design whereas these high resolution flow data are not always available. Our previous research has shown that IPFs can be derived from the easier available observed long time series of mean daily flows (MDFs) using a multiple regression model. The primary aim here is to explore the possibility of deriving frequency distributions of IPFs using hydrological modelling with daily and hourly time steps in comparison. In the post-correction approach the rainfall-runoff model is operated on daily time steps , a flood frequency distribution is fitted to the simulated annual MDFs and the resulting daily quantiles are transferred into IPF quantiles using the multiple regression model. In the pre-processing approach, hourly rainfall is produced by disaggregation of daily data. Then the rainfall-runoff model is operated on hourly time steps resulting in a frequency distribution of IPFs. In addition, two calibrations strategies for the hydrological model using the hydrograph and using flow statistics, respectively, are applied for both approaches. Finally, the performances of estimating the IPFs from daily data using these two approaches are compared considering also the two different calibration strategies. The hydrological simulations are carried out with the HBV-IWW model and the case study is carried out for 18 catchments of the Aller-Leine-River basin in northern Germany. The results show that: (1) the multiple regression model is capable to predict IPFs with the simulated MDFs as well; (2) the estimation of extreme flow quantiles in summer is not as good as in winter; (3) both of the two approaches enable a reasonable estimation of IPFs; (4) if on hand the hydrological model is calibrated on the hydrograph the post-correction approach with daily simulations is superior and if on the other hand the model is calibrated on flow statistics the pre-processing with hourly

  9. Comparing flow duration curve and rainfall-runoff modelling for predicting daily runoff in ungauged catchments

    NASA Astrophysics Data System (ADS)

    Zhang, Yongqiang; Vaze, Jai; Chiew, Francis H. S.; Li, Ming

    2015-06-01

    Predicting daily runoff time series in ungauged catchments is both important and challenging. For the last few decades, the rainfall-runoff (RR) modelling approach has been the method of choice. There have been very few studies reported in literature which attempt to use flow duration curve (FDC) to predict daily runoff time series. This study comprehensively compares the two approaches using an extensive dataset (228 catchments) for a large region of south-eastern Australia and provides guidelines for choosing the suitable method. For each approach we used the nearest neighbour method and two weightings - a 5-donor simple mathematical average (SA) and a 5-donor inverse-distance weighting (5-IDW) - to predict daily runoff time series. The results show that 5-IDW was noticeably better than a single donor to predict daily runoff time series, especially for the FDC approach. The RR modelling approach calibrated against daily runoff outperformed the FDC approach for predicting high flows. The FDC approach was better at predicting medium to low flows in traditional calibration against the Nash-Sutcliffe-Efficiency or Root Mean Square Error, but when calibrated against a low flow objective function, both the FDC and rainfall-runoff models performed equally well in simulating the low flows. These results indicate that both methods can be further improved to simulate daily hydrographs describing the range of flow metrics in ungauged catchments. Further studies should be carried out for improving the accuracy of predicted FDC in ungauged catchments, including improving the FDC model structure and parameter fitting.

  10. Modeling bulk density and snow water equivalent using daily snow depth observations

    NASA Astrophysics Data System (ADS)

    McCreight, J. L.; Small, E. E.

    2014-03-01

    Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily time step and demonstrate its improved skill over the existing models. Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long timescales) and the observed positive and negative anomalies from the smoothed time series (short timescales) as separate terms. A climatology of fit is also included as a predictor variable. Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled time series against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density root mean square error (RMSE) by 9.9 and 4.5% compared to previous models while increasing R2 from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window around peak SWE in each water year, our model reduces density RMSE by 24 and 17.4% relative to the previous models, with R2 increasing from 0.55 to 0.58 to 0.71 across models. Removing the challenge of parameter transfer over the full observational record increases R2 scores for both the existing and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over

  11. A comparison of modeling schemes for mapping daily evapotranspiration at high resolution using remote sensing

    NASA Astrophysics Data System (ADS)

    Ring, T.; Cuenca, R. H.; Anderson, M. C.; Hain, C.; Semmens, K. A.; Kustas, W. P.; Alfieri, J. G.

    2014-12-01

    Evapotranspiration (ET) is an important component of the hydrologic cycle that transfers large quantities of water vapor away from Earth's surface into the atmosphere. In addition to having water management applications in agriculture, including monitoring water rights compliance and irrigation scheduling, it is also important to be able to accurately measure water used by other landscapes for soil-vegetation-atmosphere-transfer (SVAT) models. This can only be done with large scale estimations which are most efficiently achieved with remote sensing. This research compares daily ET retrieved from two remote sensing modeling schemes: a) Reconstructed METRIC: Mapping EvapoTranspiration at high Resolution with Internalized Calibration; and b) ALEXI/DisALEXI: Atmosphere-Land EXchange Inverse /Disaggregated ALEXI, over two predominately forested landscapes. ET flux estimates are retrieved from ALEXI/DisALEXI using GOES (daily, 4km), MODIS (daily, 1km) and Landsat 8 (16 days, 30m) and from Reconstructed METRIC using Landsat 8. We develop daily Landsat scale ET maps for the summer months of 2013. The flux-tower footprint is calculated at each site to match the remotely sensed retrieval with that of the flux tower such that modeled output can be evaluated against ground based observations, taken from the AmeriFlux network. In addition, surface and evaporative fluxes retrieved from the two models are inter-compared over the different land cover types in the scenes. Differences in input and data processing requirements for each of the two methods will be also described

  12. Modeling Comparative Daily Enrollment Indicators To Aid Intelligent College Decisions. AIR 2001 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Lajubutu, Oyebanjo A.

    This paper shows how three critical enrollment indicators drawn from a relationship database were used to guide planning and management decisions. The paper discusses the guidelines for the development of the model, attributes needed, variables to be calculated, and other issues that may improve the effectiveness and efficiency of daily enrollment…

  13. MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

    EPA Science Inventory

    Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

  14. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

    SciTech Connect

    Behrang, M.A.; Assareh, E.; Ghanbarzadeh, A.; Noghrehabadi, A.R.

    2010-08-15

    The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output. (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)

  15. Application of Markov chain model to daily maximum temperature for thermal comfort in Malaysia

    SciTech Connect

    Nordin, Muhamad Asyraf bin Che; Hassan, Husna

    2015-10-22

    The Markov chain’s first order principle has been widely used to model various meteorological fields, for prediction purposes. In this study, a 14-year (2000-2013) data of daily maximum temperatures in Bayan Lepas were used. Earlier studies showed that the outdoor thermal comfort range based on physiologically equivalent temperature (PET) index in Malaysia is less than 34°C, thus the data obtained were classified into two state: normal state (within thermal comfort range) and hot state (above thermal comfort range). The long-run results show the probability of daily temperature exceed TCR will be only 2.2%. On the other hand, the probability daily temperature within TCR will be 97.8%.

  16. Influence of daily versus monthly fire emissions on atmospheric model applications in the tropics

    NASA Astrophysics Data System (ADS)

    Marlier, M. E.; Voulgarakis, A.; Faluvegi, G.; Shindell, D. T.; DeFries, R. S.

    2012-12-01

    Fires are widely used throughout the tropics to create and maintain areas for agriculture, but are also significant contributors to atmospheric trace gas and aerosol concentrations. However, the timing and magnitude of fire activity can vary strongly by year and ecosystem type. For example, frequent, low intensity fires dominate in African savannas whereas Southeast Asian peatland forests are susceptible to huge pulses of emissions during regional El Niño droughts. Despite the potential implications for modeling interactions with atmospheric chemistry and transport, fire emissions have commonly been input into global models at a monthly resolution. Recognizing the uncertainty that this can introduce, several datasets have parsed fire emissions to daily and sub-daily scales with satellite active fire detections. In this study, we explore differences between utilizing the monthly and daily Global Fire Emissions Database version 3 (GFED3) products as inputs into the NASA GISS-E2 composition climate model. We aim to understand how the choice of the temporal resolution of fire emissions affects uncertainty with respect to several common applications of global models: atmospheric chemistry, air quality, and climate. Focusing our analysis on tropical ozone, carbon monoxide, and aerosols, we compare modeled concentrations with available ground and satellite observations. We find that increasing the temporal frequency of fire emissions from monthly to daily can improve correlations with observations, predominately in areas or during seasons more heavily affected by fires. Differences between the two datasets are more evident with public health applications: daily resolution fire emissions increases the number of days exceeding World Health Organization air quality targets.

  17. Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece

    NASA Astrophysics Data System (ADS)

    Nastos, P. T.; Paliatsos, A. G.; Koukouletsos, K. V.; Larissi, I. K.; Moustris, K. P.

    2014-07-01

    Extreme daily precipitation events are involved in significant environmental damages, even in life loss, because of causing adverse impacts, such as flash floods, in urban and sometimes in rural areas. Thus, long-term forecast of such events is of great importance for the preparation of local authorities in order to confront and mitigate the adverse consequences. The objective of this study is to estimate the possibility of forecasting the maximum daily precipitation for the next coming year. For this reason, appropriate prognostic models, such as Artificial Neural Networks (ANNs) were developed and applied. The data used for the analysis concern annual maximum daily precipitation totals, which have been recorded at the National Observatory of Athens (NOA), during the long term period 1891-2009. To evaluate the potential of daily extreme precipitation forecast by the applied ANNs, a different period for validation was considered than the one used for the ANNs training. Thus, the datasets of the period 1891-1980 were used as training datasets, while the datasets of the period 1981-2009 as validation datasets. Appropriate statistical indices, such as the coefficient of determination (R2), the index of agreement (IA), the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE), were applied to test the reliability of the models. The findings of the analysis showed that, a quite satisfactory relationship (R2 = 0.482, IA = 0.817, RMSE = 16.4 mm and MBE = + 5.2 mm) appears between the forecasted and the respective observed maximum daily precipitation totals one year ahead. The developed ANN seems to overestimate the maximum daily precipitation totals appeared in 1988 while underestimate the maximum in 1999, which could be attributed to the relatively low frequency of occurrence of these extreme events within GAA having impact on the optimum training of ANN.

  18. A Predictive Model of Daily Seismic Activity Induced by Mining, Developed with Data Mining Methods

    NASA Astrophysics Data System (ADS)

    Jakubowski, Jacek

    2014-12-01

    The article presents the development and evaluation of a predictive classification model of daily seismic energy emissions induced by longwall mining in sector XVI of the Piast coal mine in Poland. The model uses data on tremor energy, basic characteristics of the longwall face and mined output in this sector over the period from July 1987 to March 2011. The predicted binary variable is the occurrence of a daily sum of tremor seismic energies in a longwall that is greater than or equal to the threshold value of 105 J. Three data mining analytical methods were applied: logistic regression,neural networks, and stochastic gradient boosted trees. The boosted trees model was chosen as the best for the purposes of the prediction. The validation sample results showed its good predictive capability, taking the complex nature of the phenomenon into account. This may indicate the applied model's suitability for a sequential, short-term prediction of mining induced seismic activity.

  19. Modeling of daily body weights and body weight changes of Nordic Red cows.

    PubMed

    Mäntysaari, P; Mäntysaari, E A

    2015-10-01

    Increased availability of automated weighing systems have made it possible to record massive amounts of body weight (BW) data in a short time. If the BW measurement is unbiased, the changes in BW reflect the energy status of the cow and can be used for management or breeding purposes. The usefulness of the BW data depends on the reliability of the measures. The noise in BW measurements can be smoothed by fitting a parametric or time series model into the BW measurements. This study examined the accuracy of different models to predict BW of the cows based on daily BW measurements and investigated the usefulness of modeling in increasing the value of BW measurements as management and breeding tools. Data included daily BW measurements, production, and intake from 230 Nordic Red dairy cows. The BW of the cows was recorded twice a day on their return from milking. In total, the data included 50,594 daily observations with 98,418 BW measurements. A clear diurnal change was present in the BW of the cows even if they had feed available 24 h. The daily average BW were used in the modeling. Five different models were tested: (1) a cow-wise fixed second-order polynomial regression model (FiX) including the exponential Wilmink term, (2) a random regression model with fixed and random animal lactation stage functions (MiX), (3) MiX with 13 periods of weighing added (PER), (4) natural cubic smoothing splines with 8 equally spaced knots (SPk8), and (5) spline model with no restriction on knots but a smoothing parameter corresponding to a fit of 5 degrees of freedom (SPdf5). In the original measured BW data, the within-animal variation was 6.4% of the total variance. Modeling decreased the within animal variation to levels of 2.9 to 5.1%. The smallest day-to-day variation and thereafter highest day-to-day repeatabilities were with PER and MiX models. The usability of modeled BW as energy balance (EB) indicator were evaluated by estimating relationships between EB, or EB

  20. A Temperature-Based Model for Estimating Monthly Average Daily Global Solar Radiation in China

    PubMed Central

    Li, Huashan; Cao, Fei; Wang, Xianlong; Ma, Weibin

    2014-01-01

    Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China. PMID:24605046

  1. Semiparametric Modeling of Daily Ammonia Levels in Naturally Ventilated Caged-Egg Facilities

    PubMed Central

    Gutiérrez-Zapata, Diana María; Galeano-Vasco, Luis Fernando; Cerón-Muñoz, Mario Fernando

    2016-01-01

    Ammonia concentration (AMC) in poultry facilities varies depending on different environmental conditions and management; however, this is a relatively unexplored subject in Colombia (South America). The objective of this study was to model daily AMC variations in a naturally ventilated caged-egg facility using generalized additive models. Four sensor nodes were used to record AMC, temperature, relative humidity and wind speed on a daily basis, with 10 minute intervals for 12 weeks. The following variables were included in the model: Heat index, Wind, Hour, Location, Height of the sensor to the ground level, and Period of manure accumulation. All effects included in the model were highly significant (p<0.001). The AMC was higher during the night and early morning when the wind was not blowing (0.0 m/s) and the heat index was extreme. The average and maximum AMC were 5.94±3.83 and 31.70 ppm, respectively. Temperatures above 25°C and humidity greater than 80% increased AMC levels. In naturally ventilated caged-egg facilities the daily variations observed in AMC primarily depend on cyclic variations of the environmental conditions and are also affected by litter handling (i.e., removal of the bedding material). PMID:26812150

  2. Semiparametric Modeling of Daily Ammonia Levels in Naturally Ventilated Caged-Egg Facilities.

    PubMed

    Gutiérrez-Zapata, Diana María; Galeano-Vasco, Luis Fernando; Cerón-Muñoz, Mario Fernando

    2016-01-01

    Ammonia concentration (AMC) in poultry facilities varies depending on different environmental conditions and management; however, this is a relatively unexplored subject in Colombia (South America). The objective of this study was to model daily AMC variations in a naturally ventilated caged-egg facility using generalized additive models. Four sensor nodes were used to record AMC, temperature, relative humidity and wind speed on a daily basis, with 10 minute intervals for 12 weeks. The following variables were included in the model: Heat index, Wind, Hour, Location, Height of the sensor to the ground level, and Period of manure accumulation. All effects included in the model were highly significant (p<0.001). The AMC was higher during the night and early morning when the wind was not blowing (0.0 m/s) and the heat index was extreme. The average and maximum AMC were 5.94±3.83 and 31.70 ppm, respectively. Temperatures above 25°C and humidity greater than 80% increased AMC levels. In naturally ventilated caged-egg facilities the daily variations observed in AMC primarily depend on cyclic variations of the environmental conditions and are also affected by litter handling (i.e., removal of the bedding material). PMID:26812150

  3. Quantum spatial-periodic harmonic model for daily price-limited stock markets

    NASA Astrophysics Data System (ADS)

    Meng, Xiangyi; Zhang, Jian-Wei; Xu, Jingjing; Guo, Hong

    2015-11-01

    We investigate the behaviors of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is considered to be oscillating and damping in a quantum spatial-periodic harmonic oscillator potential well. A complicated non-linear relation including inter-band positive correlation and intra-band negative correlation between the volatility and trading volume of a stock is numerically derived with the energy band structure of the model concerned. The effectiveness of price limit is re-examined, with some observed characteristics of price-limited stock markets in China studied by applying our quantum model.

  4. Daily Care

    MedlinePlus

    ... to Know Online Tools Enhancing Daily Life Daily Plan Activities Communication Food & Eating Music & Art Personal Care Incontinence Bathing ... Tweet Email | Print Create a Daily Routine Daily Plan Activities Communication Food/Eating Get Tips on Personal Care Bathing ...

  5. Global forecast model to predict the daily dose of the solar erythemally effective UV radiation.

    PubMed

    Schmalwieser, Alois W; Schauberger, Günther; Janouch, Michal; Nunez, Manuel; Koskela, Tapani; Berger, Daniel; Karamanian, Gabriel

    2005-01-01

    A worldwide forecast of the erythemally effective ultraviolet (UV) radiation is presented. The forecast was established to inform the public about the expected amount of erythemally effective UV radiation for the next day. Besides the irradiance, the daily dose is forecasted to enable people to choose the appropriate sun protection tools. Following the UV Index as the measure of global erythemally effective irradiance, the daily dose is expressed in units of UV Index hours. In this study, we have validated the model and the forecast against measurements from broadband UV radiometers of the Robertson-Berger type. The measurements were made at four continents ranging from the northern polar circle (67.4 degrees N) to the Antarctic coast (61.1 degrees S). As additional quality criteria the frequency of underestimation was taken into account because the forecast is a tool of radiation protection and made to avoid overexposure. A value closer than one minimal erythemal dose for the most sensitive skin type 1 to the observed value was counted as hit and greater deviations as underestimation or overestimation. The Austrian forecast model underestimates the daily dose in 3.7% of all cases, whereas 1.7% results from the model and 2.0% from the assumed total ozone content. The hit rate could be found in the order of 40%. PMID:15453822

  6. An Extended Version of the Richardson Model for Simulating Daily Weather Variables.

    NASA Astrophysics Data System (ADS)

    Parlange, Marc B.; Katz, Richard W.

    2000-05-01

    The Richardson model is a popular technique for stochastic simulation of daily weather variables, including precipitation amount, maximum and minimum temperature, and solar radiation. This model is extended to include two additional variables, daily mean wind speed and dewpoint, because these variables (or related quantities such as relative humidity) are required as inputs for certain ecological/vegetation response and agricultural management models. To allow for the positively skewed distribution of wind speed, a power transformation is applied. Solar radiation also is transformed to make the shape of its modeled distribution more realistic. A model identification criterion is used as an aid in determining whether the distributions of these two variables depend on precipitation occurrence. The approach can be viewed as an integration of what is known about the statistical properties of individual weather variables into a single multivariate model.As an application, this extended model is fitted to weather data in the Pacific Northwest. To aid in understanding how such a stochastic weather generator works, considerable attention is devoted to its statistical properties. In particular, marginal and conditional distributions of wind speed and solar radiation are examined, with the model being capable of representing relationships between variables in which the variance is not constant, as well as certain forms of nonlinearity.

  7. Development of Daily Maximum Flare-Flux Forecast Models for Strong Solar Flares

    NASA Astrophysics Data System (ADS)

    Shin, Seulki; Lee, Jin-Yi; Moon, Yong-Jae; Chu, Hyoungseok; Park, Jongyeob

    2016-03-01

    We have developed a set of daily maximum flare-flux forecast models for strong flares (M- and X-class) using multiple linear regression (MLR) and artificial neural network (ANN) methods. Our input parameters are solar-activity data from January 1996 to December 2013 such as sunspot area, X-ray maximum, and weighted total flare flux of the previous day, as well as mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classifications. For a training dataset, we used 61 events each of C-, M-, and X-class from January 1996 to December 2004. For a testing dataset, we used all events from January 2005 to November 2013. A comparison between our maximum flare-flux models and NOAA model based on true skill statistics (TSS) shows that the MLR model for X-class and the average of all flares (M{+}X-class) are much better than the NOAA model. According to the hitting fraction (HF), which is defined as a fraction of events satisfying the condition that the absolute differences of predicted and observed flare flux on a logarithm scale are smaller than or equal to 0.5, our models successfully forecast the maximum flare flux of about two-thirds of the events for strong flares. Since all input parameters for our models are easily available, the models can be operated steadily and automatically on a daily basis for space-weather services.

  8. Extreme Rainfall Events Over Southern Africa: Assessment of a Climate Model to Reproduce Daily Extremes

    NASA Astrophysics Data System (ADS)

    Williams, C.; Kniveton, D.; Layberry, R.

    2007-12-01

    It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable extreme events, due to a number of factors including extensive poverty, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of a state-of-the-art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of SST anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the UK Meteorological Office Hadley Centre's climate model's domain size are firstly presented. Then simulations of current climate from the model, operating in both regional and global mode, are compared to the MIRA dataset at daily timescales. Thirdly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. Finally, the results from the idealised SST experiments are briefly presented, suggesting associations between rainfall extremes and both local and remote SST anomalies.

  9. A regional neural network model for predicting mean daily river water temperature

    USGS Publications Warehouse

    Wagner, Tyler; DeWeber, Jefferson Tyrell

    2014-01-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

  10. Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes

    NASA Astrophysics Data System (ADS)

    Williams, C.; Kniveton, D.; Layberry, R.

    2009-04-01

    It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will

  11. Fitting optimum order of Markov chain models for daily rainfall occurrences in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Deni, Sayang Mohd; Jemain, Abdul Aziz; Ibrahim, Kamarulzaman

    2009-06-01

    The analysis of the daily rainfall occurrence behavior is becoming more important, particularly in water-related sectors. Many studies have identified a more comprehensive pattern of the daily rainfall behavior based on the Markov chain models. One of the aims in fitting the Markov chain models of various orders to the daily rainfall occurrence is to determine the optimum order. In this study, the optimum order of the Markov chain models for a 5-day sequence will be examined in each of the 18 rainfall stations in Peninsular Malaysia, which have been selected based on the availability of the data, using the Akaike’s (AIC) and Bayesian information criteria (BIC). The identification of the most appropriate order in describing the distribution of the wet (dry) spells for each of the rainfall stations is obtained using the Kolmogorov-Smirnov goodness-of-fit test. It is found that the optimum order varies according to the levels of threshold used (e.g., either 0.1 or 10.0 mm), the locations of the region and the types of monsoon seasons. At most stations, the Markov chain models of a higher order are found to be optimum for rainfall occurrence during the northeast monsoon season for both levels of threshold. However, it is generally found that regardless of the monsoon seasons, the first-order model is optimum for the northwestern and eastern regions of the peninsula when the level of thresholds of 10.0 mm is considered. The analysis indicates that the first order of the Markov chain model is found to be most appropriate for describing the distribution of wet spells, whereas the higher-order models are found to be adequate for the dry spells in most of the rainfall stations for both threshold levels and monsoon seasons.

  12. Infilling Missing Daily Precipitation Data at Multiple Sites Using a Multivariate Truncated Normal Distribution Model

    NASA Astrophysics Data System (ADS)

    Ng, W.; Rasmussen, P. F.; Panu, U. S.

    2009-12-01

    Stochastic weather modeling is subject to a number of challenges including varied spatial-dependency and the existence of missing observations. Daily precipitation possesses unique statistical characteristics in distribution, such as the existence of high frequency of zero records and the high skewness of the distribution of precipitation amount. To address for these difficulties, a methodology based on the multivariate truncated Normal distribution model is proposed. The methodology transforms the skewed distribution of precipitation amounts at multiple sites into a multivariate Normal distribution model. The missing observations are then be estimated through the conditional mean and variance obtained from the multivariate Normal distribution model. The adequacy of the proposed model structure was first verified using a synthetic data set. Subsequently, 30 years of historical daily precipitation records from 10 Canadian meteorological stations were used to evaluate the performance of the model. The result of the evaluation shows that the proposed model reasonably can preserve the statistical characteristics of the historical records in estimated the missing records at multiple sites.

  13. Daily diaries and minority adolescents: random coefficient regression modeling of attributional style, coping, and affect.

    PubMed

    Roesch, Scott C; Vaughn, Allison A; Aldridge, Arianna A; Villodas, Feion

    2009-10-01

    Many researchers underscore the importance of coping in the daily lives of adolescents, yet very few studies measure this and related constructs at this level. Using a daily diary approach to stress and coping, the current study evaluated a series of mediational coping models in a sample of low-income minority adolescents (N = 89). Specifically, coping was hypothesized to mediate the relationship between attributional style (and dimensions) and daily affect. Using random coefficient regression modeling, the relationship between (a) the locus of causality dimension and positive affect was completely mediated by the use of acceptance and humor as coping strategies; (b) the stability dimension and positive affect was completely mediated by the use of both problem-solving and positive thinking; and (c) the stability dimension and negative affect was partially mediated by the use of religious coping. In addition, the locus of causality and stability (but not globality) dimensions were also directly related to affect. However, the relationship between pessimistic explanatory style and affect was not mediated by coping. Consistent with previous research, these findings suggest that attributions are both directly and indirectly related to indices of affect or adjustment. Thus, attributions may not only influence the type of coping strategy employed, but may also serve as coping strategies themselves. PMID:22029618

  14. Statistical Modeling of Spatio-Temporal Variability in Monthly Average Daily Solar Radiation over Turkey

    PubMed Central

    Evrendilek, Fatih; Ertekin, Can

    2007-01-01

    Though one of the most significant driving forces behind ecological processes such as biogeochemical cycles and energy flows, solar radiation data are limited or non-existent by conventional ground-based measurements, and thus, often estimated from other meteorological data through (geo)statistical models. In this study, spatial and temporal patterns of monthly average daily solar radiation on a horizontal surface at the ground level were quantified using 130 climate stations for the entire Turkey and its conventionally-accepted seven geographical regions through multiple linear regression (MLR) models as a function of latitude, longitude, altitude, aspect, distance to sea; minimum, maximum and mean air temperature and relative humidity, soil temperature, cloudiness, precipitation, pan evapotranspiration, day length, maximum possible sunshine duration, monthly average daily extraterrestrial solar radiation, and time (month), and universal kriging method. The resulting 20 regional best-fit MLR models (three MLR models for each region) based on parameterization datasets had R2adj values of 91.5% for the Central Anatolia region to 98.0% for the Southeast Anatolia region. Validation of the best-fit MLR models for each region led to R2 values of 87.7% for the Mediterranean region to 98.5% for the Southeast Anatolia region. The best-fit anisotropic semi-variogram models for universal kriging as a result of one-leave-out cross-validation gave rise to R2 values of 10.9% in July to 52.4% in November. Surface maps of monthly average daily solar radiation were generated over Turkey, with a grid resolution of 500 m × 500 m.

  15. Spatial modeling of the highest daily maximum temperature in Korea via max-stable processes

    NASA Astrophysics Data System (ADS)

    Lee, Youngsaeng; Yoon, Sanghoo; Murshed, Md. Sharwar; Kim, Maeng-Ki; Cho, ChunHo; Baek, Hee-Jeong; Park, Jeong-Soo

    2013-11-01

    This paper examines the annual highest daily maximum temperature (DMT) in Korea by using data from 56 weather stations and employing spatial extreme modeling. Our approach is based on max-stable processes (MSP) with Schlather’s characterization. We divide the country into four regions for a better model fit and identify the best model for each region. We show that regional MSP modeling is more suitable than MSP modeling for the entire region and the pointwise generalized extreme value distribution approach. The advantage of spatial extreme modeling is that more precise and robust return levels and some indices of the highest temperatures can be obtained for observation stations and for locations with no observed data, and so help to determine the effects and assessment of vulnerability as well as to downscale extreme events.

  16. Neyman-Scott cluster model for daily rainfall processes in lower extremadura (Spain): Rainfall Generating Mechanisms

    NASA Astrophysics Data System (ADS)

    Marroquin, A.; Garcia, J. A.; Garrido, J.; Mateos, V. L.

    1995-09-01

    A Neyman-Scott cluster model was fitted to the daily rainfall data recorded at the observatory of Badajoz (southwestern Spain) for the period 1901 1990. The data were previously homogenized. The goodness of the fit that indicated the daily rainfall process follows some Rainfall Generating Mechanism (RGM). Having decided on the criteria that a block of rainfall must fulfill to be considered as a RGM, a method was proposed to classify the days that belong to RGMs according to the 500 hPa and the surface topography. In this method each day is characterized by a string of 22 alphanumeric characters. From the subsequent analysis, the structure of the synoptic patterns associated with each RGM was deduced.

  17. Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast

    NASA Astrophysics Data System (ADS)

    Wong, Jefferson See; Zhang, Qiang; Chen, Yongqin David

    2010-03-01

    This study attempted to address statistical properties and forecast of daily urban water consumption in Hong Kong from 1990 to 2007. A statistical model was formulated to differentiate the effects of five factors on water use, i.e., trend, seasonality, climatic regression, calendar effect, and autoregression. The postulate of the statistical model is that total water use is made up of base, seasonal, and calendrical water use. Daily urban water consumption in Hong Kong from 1990 to 2001 was modeled and the developed statistical model explains 83% of the variance with six factors: trend (8%), seasonality (27%), climatic regression (2%), day-of-the-week effect (17%), holiday effect (17%), and autoregression (12%). The model was further validated using an independent data set from 2002 to 2007, yielding a R2 of 76%. The results indicated good performance of the developed statistical model in depicting the temporal variations of the urban water use in Hong Kong, offering an improved insight into urban utilization of water resources and acting as the theoretical support for effective urban water resource management in Hong Kong under the changing environment.

  18. The optimum order of a Markov chain model for daily rainfall in Nigeria

    NASA Astrophysics Data System (ADS)

    Jimoh, O. D.; Webster, P.

    1996-11-01

    Markov type models are often used to describe the occurrence of daily rainfall. Although models of Order 1 have been successfully employed, there remains uncertainty concerning the optimum order for such models. This paper is concerned with estimation of the optimum order of Markov chains and, in particular, the use of objective criteria of the Akaike and Bayesian Information Criteria (AIC and BIC, respectively). Using daily rainfall series for five stations in Nigeria, it has been found that the AIC and BIC estimates vary with month as well as the value of the rainfall threshold used to define a wet day. There is no apparent system to this variation, although AIC estimates are consistently greater than or equal to BIC estimates, with values of the latter limited to zero or unity. The optimum order is also investigated through generation of synthetic sequences of wet and dry days using the transition matrices of zero-, first- and second-order Markov chains. It was found that the first-order model is superior to the zero-order model in representing the characteristics of the historical sequence as judged using frequency duration curves. There was no discernible difference between the model performance for first- and second-order models. There was no seasonal varation in the model performance, which contrasts with the optimum models identified using AIC and BIC estimates. It is concluded that caution is needed with the use of objective criteria for determining the optimum order of the Markov model and that the use of frequency duration curves can provide a robust alternative method of model identification. Comments are also made on the importance of record length and non-stationarity for model identification

  19. Generating daily weather data for ecosystem modelling in the Congo River Basin

    NASA Astrophysics Data System (ADS)

    Petritsch, Richard; Pietsch, Stephan A.

    2010-05-01

    Daily weather data are an important constraint for diverse applications in ecosystem research. In particular, temperature and precipitation are the main drivers for forest ecosystem productivity. Mechanistic modelling theory heavily relies on daily values for minimum and maximum temperatures, precipitation, incident solar radiation and vapour pressure deficit. Although the number of climate measurement stations increased during the last centuries, there are still regions with limited climate data. For example, in the WMO database there are only 16 stations located in Gabon with daily weather measurements. Additionally, the available time series are heavily affected by measurement errors or missing values. In the WMO record for Gabon, on average every second day is missing. Monthly means are more robust and may be estimated over larger areas. Therefore, a good alternative is to interpolate monthly mean values using a sparse network of measurement stations, and based on these monthly data generate daily weather data with defined characteristics. The weather generator MarkSim was developed to produce climatological time series for crop modelling in the tropics. It provides daily values for maximum and minimum temperature, precipitation and solar radiation. The monthly means can either be derived from the internal climate surfaces or prescribed as additional inputs. We compared the generated outputs observations from three climate stations in Gabon (Lastourville, Moanda and Mouilla) and found that maximum temperature and solar radiation were heavily overestimated during the long dry season. This is due to the internal dependency of the solar radiation estimates to precipitation. With no precipitation a cloudless sky is assumed and thus high incident solar radiation and a large diurnal temperature range. However, in reality it is cloudy in the Congo River Basin during the long dry season. Therefore, we applied a correction factor to solar radiation and temperature range

  20. Prospective Measurement of Daily Health Behaviors: Modeling Temporal Patterns in Missing Data, Sexual Behavior, and Substance Use in an Online Daily Diary Study of Gay and Bisexual Men.

    PubMed

    Rendina, H Jonathon; Ventuneac, Ana; Mustanski, Brian; Grov, Christian; Parsons, Jeffrey T

    2016-08-01

    Daily diary and other intensive longitudinal methods are increasingly being used to investigate fluctuations in psychological and behavioral processes. To inform the development of this methodology, we sought to explore predictors of and patterns in diary compliance and behavioral reports. We used multilevel modeling to analyze data from an online daily diary study of 371 gay and bisexual men focused on sexual behavior and substance use. We found that greater education and older age as well as lower frequency of substance use were associated with higher compliance. Using polynomial and trigonometric functions, we found evidence for circaseptan patterns in compliance, sexual behavior, and substance use, as well as linear declines in compliance and behavior over time. The results suggest potential sources of non-random patterns of missing data and suggest that trigonometric terms provide a similar but more parsimonious investigation of circaseptan rhythms than do third-order polynomial terms. PMID:26992392

  1. Development of Daily Solar Maximum Flare Flux Forecast Models for Strong Flares

    NASA Astrophysics Data System (ADS)

    Shin, Seulki; Chu, Hyoungseok

    2015-08-01

    We have developed a set of daily solar maximum flare flux forecast models for strong flares using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray maximum flare flux and weighted total flux of the previous day, and mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classification. For a training data set, we use the same number of 61 events for each C-, M-, and X-class from Jan. 1996 to Dec. 2004, while other previous models use all flares. For a testing data set, we use all flares from Jan. 2005 to Nov. 2013. The statistical parameters from contingency tables show that the ANN models are better for maximum flare flux forecasting than the MLR models. A comparison between our maximum flare flux models and the previous ones based on Heidke Skill Score (HSS) shows that our all models for X-class flare are much better than the other models. According to the Hitting Fraction (HF), which is defined as a fraction of events satisfying that the absolute differences of predicted and observed flare flux in logarithm scale are less than equal to 0.5, our models successfully forecast the maximum flare flux of about two-third events for strong flares. Since all input parameters for our models are easily available, the models can be operated steadily and automatically on daily basis for space weather service.

  2. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models

    NASA Astrophysics Data System (ADS)

    Piani, C.; Weedon, G. P.; Best, M.; Gomes, S. M.; Viterbo, P.; Hagemann, S.; Haerter, J. O.

    2010-12-01

    SummaryA statistical bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures. The bias correction is based on a fitted histogram equalization function. This function is defined daily, as opposed to earlier published versions in which they were derived yearly or seasonally at best, while conserving properties of robustness and eliminating unrealistic jumps at seasonal or monthly transitions. The methodology is tested using the newly available global dataset of observed hydrological forcing data of the last 50 years from the EU project WATCH (WATer and global CHange) and an initial conditions ensemble of simulations performed with the ECHAM5 global climate model for the same period. Bias corrections are derived from 1960 to 1969 observed and simulated data and then applied to 1990-1999 simulations. Results confirm the effectiveness of the methodology for all tested variables. Bias corrections are also derived using three other non-overlapping decades from 1970 to 1999 and all members of the ECHAM5 initial conditions ensemble. A methodology is proposed to use the resulting "ensemble of bias corrections" to quantify the error in simulated scenario projections of components of the hydrological cycle.

  3. Incorporating Daily Flood Control Objectives Into a Monthly Stochastic Dynamic Programing Model for a Hydroelectric Complex

    NASA Astrophysics Data System (ADS)

    Druce, Donald J.

    1990-01-01

    A monthly stochastic dynamic programing model was recently developed and implemented at British Columbia (B.C.) Hydro to provide decision support for short-term energy exports and, if necessary, for flood control on the Peace River in northern British Columbia. The model establishes the marginal cost of supplying energy from the B.C. Hydro system, as well as a monthly operating policy for the G.M. Shrum and Peace Canyon hydroelectric plants and the Williston Lake storage reservoir. A simulation model capable of following the operating policy then determines the probability of refilling Williston Lake and possible spill rates and volumes. Reservoir inflows are input to both models in daily and monthly formats. The results indicate that flood control can be accommodated without sacrificing significant export revenue.

  4. Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

    PubMed Central

    Trnka, Miroslav; Eitzinger, Josef; Kapler, Pavel; Dubrovský, Martin; Semerádová, Daniela; Žalud, Zden ěk; Formayer, Herbert

    2007-01-01

    The results of previous studies have suggested that estimated daily global radiation (RG) values contain an error that could compromise the precision of subsequent crop model applications. The following study presents a detailed site and spatial analysis of the RG error propagation in CERES and WOFOST crop growth models in Central European climate conditions. The research was conducted i) at the eight individual sites in Austria and the Czech Republic where measured daily RG values were available as a reference, with seven methods for RG estimation being tested, and ii) for the agricultural areas of the Czech Republic using daily data from 52 weather stations, with five RG estimation methods. In the latter case the RG values estimated from the hours of sunshine using the Ångström-Prescott formula were used as the standard method because of the lack of measured RG data. At the site level we found that even the use of methods based on hours of sunshine, which showed the lowest bias in RG estimates, led to a significant distortion of the key crop model outputs. When the Ångström-Prescott method was used to estimate RG, for example, deviations greater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 per cent of cases. The precision of the yield estimates and other crop model outputs was lower when RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that RG estimates based on

  5. Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian

    NASA Astrophysics Data System (ADS)

    Teneng, Dean

    2013-09-01

    We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.

  6. Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes

    NASA Astrophysics Data System (ADS)

    Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

    2009-04-01

    It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will

  7. Simulation And Forecasting of Daily Pm10 Concentrations Using Autoregressive Models In Kagithane Creek Valley, Istanbul

    NASA Astrophysics Data System (ADS)

    Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

    2015-04-01

    A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built

  8. A hybrid orographic plus statistical model for downscaling daily precipitation in Northern California

    USGS Publications Warehouse

    Pandey, G.R.; Cayan, D.R.; Dettinger, M.D.; Georgakakos, K.P.

    2000-01-01

    A hybrid (physical-statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely distributed station measurements; thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea. The model is driven by the 2.5?? ?? 2.5?? gridded National Oceanic and Atmospheric Administration-National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region. Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January-March over the period of 1988-95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation. Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

  9. Support of total maximum daily load programs using spatially referenced regression models

    USGS Publications Warehouse

    McMahon, G.; Alexander, R.B.; Qian, S.

    2003-01-01

    The spatially referenced regressions on watershed attributes modeling approach, as applied to predictions of total nitrogen flux in three North Carolina river basins, addresses several information needs identified by a National Research Council evaluation of the total maximum daily load program. The model provides reach-level predictions of the probability of exceeding water-quality criteria, and estimates of total nitrogen budgets. Model estimates of point- and diffuse-source contributions and nitrogen loss rates in streams and reservoirs compared moderately well with literature estimates. Maps of reach-level predictions of nutrient inputs and delivery provide an intuitive and spatially detailed summary of the origins and fate of nutrients within a basin.

  10. Operationalizing Pain Treatment in the Biopsychosocial Model: Take a Daily "SWEM"--Socialize, Work, Exercise, Meditate.

    PubMed

    Collen, Mark

    2015-09-01

    In the United States, chronic pain is often poorly treated at an exceedingly high cost. The use of the biomedical model to manage pain is frequently ineffective, and evidence suggests that the biopsychosocial (BPS) model is a better choice. A problem with the BPS model is that it has not been operationalized in terms of patient behavior. This commentary addresses that issue by suggesting that people with chronic pain and illness participate daily in four self-management health behaviors: socialize, work, exercise, and meditation, and discusses evidence that supports these recommendations. These self-management behaviors may decrease pain and thus reduce the need for pain medications and other medical interventions. Additional topics include patient adherence and health coaching. PMID:26367791

  11. Estimation and prediction of maximum daily rainfall at Sagar Island using best fit probability models

    NASA Astrophysics Data System (ADS)

    Mandal, S.; Choudhury, B. U.

    2015-07-01

    Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.

  12. Space-time modeling of a rainfall field ; Application to daily rainfall in the Loire basin

    NASA Astrophysics Data System (ADS)

    Lepioufle, Jean-Marie; Leblois, Etienne; Creutin, Jean-Dominique

    2010-05-01

    Water resources management for a watershed necessitates to assess both high flow volumes and the impact of the management practice for different stakeholders (hydropower, irrigation, ecology...). To test different management strategies, hydrologists have developed hydrological distributed models incorporating several computational objects such as digital elevation model, sub-basins, and distances to the basin outlet. A good characterization of rainfall variability in space and time is crucial for the relevance of a hydrological model as a basis for the choice of water management strategy. Climatological references of rainfall hazard must be built from observation over decades. Daily rainfall measurements from raingauge networks are therefore still an invaluable source of information for a precise representation of precipitation hazard despite the recent availability of radar estimates. Based on either raingauge or radar observations, it is possible to mathematically model rainfall field as a space-time intermittent process (superposition of inner variability field and rainfall indicator field, both influenced by advection). Geostatistics enables to investigate the link between an instantaneous process space-time structure and the evolution of spatial structure with time aggregation.. A method is proposed to infer a relevant instantaneous process from observed rainfall statistics. After fitting the parameters of the instantaneous space-time variogram with the simplex method, spatial variograms for different duration respecting time aggregated variograms is calculated. With this basis, an avenue is open to simulate homogeneous rainfall fields which respect major statistical characteristics for hydrologists: expectation and variance of rainfall distribution and spatial variogram for different durations. Benefits and limits of this approach are investigated using daily rainfall data from the Loire basin in France. Two sub-regions are highlighted. A downstream zone

  13. Stochastic Modeling based on Dictionary Approach for the Generation of Daily Precipitation Occurrences

    NASA Astrophysics Data System (ADS)

    Panu, U. S.; Ng, W.; Rasmussen, P. F.

    2009-12-01

    The modeling of weather states (i.e., precipitation occurrences) is critical when the historical data are not long enough for the desired analysis. Stochastic models (e.g., Markov Chain and Alternating Renewal Process (ARP)) of the precipitation occurrence processes generally assume the existence of short-term temporal-dependency between the neighboring states while implying the existence of long-term independency (randomness) of states in precipitation records. Existing temporal-dependent models for the generation of precipitation occurrences are restricted either by the fixed-length memory (e.g., the order of a Markov chain model), or by the reining states in segments (e.g., persistency of homogenous states within dry/wet-spell lengths of an ARP). The modeling of variable segment lengths and states could be an arduous task and a flexible modeling approach is required for the preservation of various segmented patterns of precipitation data series. An innovative Dictionary approach has been developed in the field of genome pattern recognition for the identification of frequently occurring genome segments in DNA sequences. The genome segments delineate the biologically meaningful ``words" (i.e., segments with a specific patterns in a series of discrete states) that can be jointly modeled with variable lengths and states. A meaningful “word”, in hydrology, can be referred to a segment of precipitation occurrence comprising of wet or dry states. Such flexibility would provide a unique advantage over the traditional stochastic models for the generation of precipitation occurrences. Three stochastic models, namely, the alternating renewal process using Geometric distribution, the second-order Markov chain model, and the Dictionary approach have been assessed to evaluate their efficacy for the generation of daily precipitation sequences. Comparisons involved three guiding principles namely (i) the ability of models to preserve the short-term temporal-dependency in

  14. Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

    NASA Astrophysics Data System (ADS)

    Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; Reeves, Geoffrey D.; Clilverd, Mark

    2016-04-01

    The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). A path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current (Dst), AE, and wave activity.

  15. Reconstruction of daily erythemal UV radiation values for the last century - The benefit of modelled ozone

    NASA Astrophysics Data System (ADS)

    Junk, J.; Feister, U.; Rozanov, E.; Krzyścin, J. W.

    2013-05-01

    Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long-term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVER time series. Therefore, we combined groundbased measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVER for the period from 1901 to 1999. Additionally, analyses of the long-term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVER provide a long-term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN-approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.

  16. Ecological Interventionist Causal Models in Psychosis: Targeting Psychological Mechanisms in Daily Life.

    PubMed

    Reininghaus, Ulrich; Depp, Colin A; Myin-Germeys, Inez

    2016-03-01

    Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals' daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. PMID:26707864

  17. Modeling daily average stream temperature from air temperature and watershed area

    NASA Astrophysics Data System (ADS)

    Butler, N. L.; Hunt, J. R.

    2012-12-01

    Habitat restoration efforts within watersheds require spatial and temporal estimates of water temperature for aquatic species especially species that migrate within watersheds at different life stages. Monitoring programs are not able to fully sample all aquatic environments within watersheds under the extreme conditions that determine long-term habitat viability. Under these circumstances a combination of selective monitoring and modeling are required for predicting future geospatial and temporal conditions. This study describes a model that is broadly applicable to different watersheds while using readily available regional air temperature data. Daily water temperature data from thirty-eight gauges with drainage areas from 2 km2 to 2000 km2 in the Sonoma Valley, Napa Valley, and Russian River Valley in California were used to develop, calibrate, and test a stream temperature model. Air temperature data from seven NOAA gauges provided the daily maximum and minimum air temperatures. The model was developed and calibrated using five years of data from the Sonoma Valley at ten water temperature gauges and a NOAA air temperature gauge. The daily average stream temperatures within this watershed were bounded by the preceding maximum and minimum air temperatures with smaller upstream watersheds being more dependent on the minimum air temperature than maximum air temperature. The model assumed a linear dependence on maximum and minimum air temperature with a weighting factor dependent on upstream area determined by error minimization using observed data. Fitted minimum air temperature weighting factors were consistent over all five years of data for each gauge, and they ranged from 0.75 for upstream drainage areas less than 2 km2 to 0.45 for upstream drainage areas greater than 100 km2. For the calibration data sets within the Sonoma Valley, the average error between the model estimated daily water temperature and the observed water temperature data ranged from 0.7

  18. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects

    EPA Science Inventory

    Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregn...

  19. HIGH-RESOLUTION SPATIAL MODELING OF DAILY WEATHER ELEMENTS FOR A CATCHMENT IN THE OREGON CASCADE MOUNTAINS, UNITED STATES

    EPA Science Inventory

    High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the development, application, and assessment of ...

  20. Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature date over the continental US

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Agricultural research increasingly is expected to provide precise, quantitative information with an explicit geographic coverage. Limited availability of continuous daily meteorological records often constrains efforts to provide such information through integrated use of simulation models, spatial ...

  1. Genetic parameters for functional traits in dairy cattle from daily random regression models.

    PubMed

    Karacaören, B; Jaffrézic, F; Kadarmideen, H N

    2006-02-01

    The objective of the research was to estimate genetic parameters, such as heritabilities and genetic correlations, using daily test day data for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) using random regression methodology. Data were from first lactation dairy cows (n = 320) from the Chamau research farm of the Swiss Federal Institute of Technology, Switzerland over the period from April 1994 to 2004. All traits were recorded daily using automated machines. Estimated heritabilities (h(2)) varied from 0.18 to 0.30 (mean h(2) = 0.24) for MY, 0.003 to 0.098 (mean h(2) = 0.03) for MS, 0.22 to 0.53 (mean h(2) = 0.43) for BW, and 0.12 to 0.34 (mean h(2) = 0.23) for DMI. A permanent environmental effect was included in both the univariate and bivariate models, but was assumed constant in estimating some genetic correlations because of convergence problems. Estimated genetic correlations varied from 0.31 to 0.41 between MY and MS, from -0.47 to 0.29 between MY and DMI, from -0.60 to 0.54 between MY and BW, from 0.17 to 0.26 between MS and DMI, from -0.18 to 0.25 between MS and BW, and from -0.89 to 0.29 between DMI and BW. Genetic correlations for MY, MS, DMI, and BW from calving to midlactation decreased similarly to 0.40, 0.36, 0.14, and 0.36 and, at the end of the lactation, decreased to -0.06, 0.23, -0.07, and 0.09, respectively. Daily genetic variance-covariance of many functional traits are reported for the first time and will be useful when constructing selection indexes for more than one trait based on longitudinal genetic parameters. PMID:16428647

  2. Modeling trait and state variation using multilevel factor analysis with PANAS daily diary data.

    PubMed

    Merz, Erin L; Roesch, Scott C

    2011-02-01

    This study used daily diary data to model trait and state Positive Affect (PA) and Negative Affect (NA) using the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). Data were collected from 364 college students over five days. Intraclass correlation coefficients suggested approximately equal amounts of variability at the trait and state levels. Multilevel factor analysis revealed that the model specifying two correlated factors (PA, NA) and correlated uniqueness terms among redundant items provided the best fit. Trait and state PA and NA were generally associated with stress, anxiety, depression, and three types of self-esteem (performance, academic, social). The coefficients describing these relationships differed somewhat, suggesting that trait and state measurement may have different predictive utility. PMID:21516166

  3. Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT

    NASA Astrophysics Data System (ADS)

    Schliep, E. M.; Gelfand, A. E.; Holland, D. M.

    2015-12-01

    There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.

  4. A Modeling Study of Oceanic Response to Daily and Monthly Surface Forcing

    NASA Technical Reports Server (NTRS)

    Sui, Chung-Hsiung; Li, Xiao-Fan; Rienecker, Michele M.; Lau, William K.-M.; Einaudi, Franco (Technical Monitor)

    2001-01-01

    The goal of this study is to investigate the effect of high-frequency surface forcing (wind stresses and heat fluxes) on upper-ocean response. We use the reduced-gravity quasi-isopycnal ocean model by Schopf and Loughe (1995) for this study. Two experiments are performed: one with daily and the other with monthly surface forcing. The two experiments are referred to as DD and MM, respectively. The daily surface wind stress is produced from the SSM/I wind data (Atlas et al. 1991) using the drag coefficient of Large and Pond (1982). The surface latent and sensible heat fluxes are estimated using the atmospheric mixed layer model by Seager et al. (1995) with the time-varying air temperature and specific humidity from the NCEP-NCAR reanalysis (Kalnay et al. 1996). The radiation is based on climatological shortwave radiation from the Earth Radiation Budget Experiment (ERBE) [Harrison et al. 1993] and the daily GEWEX SRB data. The ocean model domain is restricted to the Pacific Ocean with realistic land boundaries. At the southern boundary the model temperature and salinity are relaxed to the Levitus (1994) climatology. The time-mean SST distribution from MM is close to the observed SST climatology while the mean SST field from DD is about 1.5 C cooler. To identify the responsible processes, we examined the mean heat budgets and the heat balance during the first year (when the difference developed) in the two experiments. The analysis reveals that this is contributed by two factors. One is the difference in latent heat flux. The other is the difference in mixing processes. To further evaluate the responsible processes, we repeated the DD experiment by reducing the based vertical diffusion from 1e-4 to 0.5e-5. The resultant SST field becomes quite closer to the observed SST field. SST variability from the two experiments is generally similar, but the equatorial SST differences between the two experiments show interannual variations. We are investigating the possible

  5. Evaluating regional climate models for simulating sub-daily rainfall extremes

    NASA Astrophysics Data System (ADS)

    Cortés-Hernández, Virginia Edith; Zheng, Feifei; Evans, Jason; Lambert, Martin; Sharma, Ashish; Westra, Seth

    2015-11-01

    Sub-daily rainfall extremes are of significant societal interest, with implications for flash flooding and the design of urban stormwater systems. It is increasingly recognised that extreme subdaily rainfall will intensify as a result of global temperature increases, with regional climate models (RCMs) representing one of the principal lines of evidence on the likely magnitude and spatiotemporal characteristics of these changes. To evaluate the ability of RCMs to simulate subdaily extremes, it is common to compare the simulated statistical characteristics of the extreme rainfall events with those from observational records. While such analyses are important, they provide insufficient insight into whether the RCM reproduces the correct underlying physical processes; in other words, whether the model "gets the right answers for the right reasons". This paper develops a range of metrics to assess the performance of RCMs in capturing the physical mechanisms that produce extreme rainfall. These metrics include the diurnal and seasonal cycles, relationship between rainfall intensity and temperature, temporal scaling, and the spatial structure of extreme rainfall events. We evaluate a high resolution RCM—the Weather Research Forecasting model—over the Greater Sydney region, using three alternative parametrization schemes. The model shows consistency with the observations for most of the proposed metrics. Where differences exist, these are dependent on both the rainfall duration and model parameterization strategy. The use of physically meaningful performance metrics not only enhances the confidence in model simulations, but also provides better diagnostic power to assist with future model improvement.

  6. Enhancing fecal coliform total maximum daily load models through bacterial source tracking

    USGS Publications Warehouse

    Hyer, K.E.; Moyer, D.L.

    2004-01-01

    Surface water impairment by fecal coliform bacteria is a water quality issue of national scope and importance. In Virginia, more than 400 stream and river segments are on the Commonwealth's 2002 303(d) list because of fecal coliform impairment. Total maximum daily loads (TMDLs) will be developed for most of these listed streams and rivers. Information regarding the major fecal coliform sources that impair surface water quality would enhance the development of effective watershed models and improve TMDLs. Bacterial source tracking (BST) is a recently developed technology for identifying the sources of fecal coliform bacteria and it may be helpful in generating improved TMDLs. Bacterial source tracking was performed, watershed models were developed, and TMDLs were prepared for three streams (Accotink Creek, Christians Creek, and Blacks Run) on Virginia's 303(d) list of impaired waters. Quality assurance of the BST work suggests that these data adequately describe the bacteria sources that are impairing these streams. Initial comparison of simulated bacterial sources with the observed BST data indicated that the fecal coliform sources were represented inaccurately in the initial model simulation. Revised model simulations (based on BST data) appeared to provide a better representation of the sources of fecal coliform bacteria in these three streams. The coupled approach of incorporating BST data into the fecal coliform transport model appears to reduce model uncertainty and should result in an improved TMDL.

  7. Sleep and affect in older adults: using multilevel modeling to examine daily associations

    PubMed Central

    McCRAE, CHRISTINA S.; McNAMARA, JOSEPH P. H.; ROWE, MEREDETH A.; DZIERZEWSKI, JOSEPH M.; DIRK, JUDITH; MARSISKE, MICHAEL; CRAGGS, JASON G.

    2010-01-01

    SUMMARY The main objective of the present study was to examine daily associations (intraindividual variability or IIV) between sleep and affect in older adults. Greater understanding of these associations is important, because both sleep and affect represent modifiable behaviors that can have a major influence on older adults’ health and well-being. We collected sleep diaries, actigraphy, and affect data concurrently for 14 days in 103 community-dwelling older adults. Multilevel modeling was used to assess the sleep–affect relationship at both the group (between-persons) and individual (within-person or IIV) levels. We hypothesized that nights characterized by better sleep would be associated with days characterized by higher positive affect and lower negative affect, and that the inverse would be true for poor sleep. Daily associations were found between affect and subjective sleep, only and were in the hypothesized direction. Specifically, nights with greater reported awake time or lower sleep quality ratings were associated with days characterized by less positive affect and more negative affect. Gender was not a significant main effect in the present study, despite previous research suggesting gender differences in the sleep–affect relationship. The fact that self-ratings of sleep emerged as the best predictors of affect may suggest that perceived sleep is a particularly important predictor. Finally, our results suggest exploration of affect as a potential intervention target in late-life insomnia is warranted. PMID:18275554

  8. Investigating daily summertime circulation and precipitation over West Africa with the WRF model: a regional climate model adaptation study

    NASA Astrophysics Data System (ADS)

    Noble, Erik Ulysses

    This dissertation a) evaluates the performance of the NCAR Weather and Research Forecasting (WRF) model as a West African Sahel regional-atmospheric model and b) investigates the utility of regional modeling to meeting user-needs. This work represents the beginning of an effort to adapt the model as a regional climate model (RCM) for the Sahel. Two independent studies test WRF sensitivity to 64 configurations of alternative parameterizations in a series of September simulations. In all, 104 12-day simulations during 11 consecutive years are examined. Simulated daily and mean circulation results are validated against NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP Reanalysis-2. Modeled daily and total precipitation results are validated against NASA's Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with transient African Easterly Waves (AEWs). A wide range of 700-hPa vorticity and daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve circulation correlations against reanalysis of 0.40-0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year, but they get time-longitude precipitation correlations (against GPCP) of between 0.35-0.42. A parallel-benchmark-simulation by the NASA Regional Model-3 (RM3) achieves higher correlations but less realistic spatiotemporal variability. The largest favorable impact on WRF vorticity and precipitation validation is achieved by selecting the Grell-Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis and GPCP than simulations using the Kain-Fritch convection. Other parameterizations have less-obvious impact. A comparison of reanalysis circulation against two NASA-radiosonde stations confirms that both reanalyses represent

  9. Ability in daily activities after early supported discharge models of stroke rehabilitation

    PubMed Central

    Taule, Tina; Strand, Liv Inger; Assmus, Jörg; Skouen, Jan Sture

    2015-01-01

    Abstract More knowledge is needed about how different rehabilitation models in the municipality influence stroke survivors’ ability in activities of daily living (ADL). Objectives: To compare three models of outpatient rehabilitation; early supported discharge (ESD) in a day unit, ESD at home and traditional treatment in the municipality (control group), regarding change in ADL ability during the first three months after stroke. Methods: A group comparison study was designed within a randomized controlled trial. Included participants were tested with the Assessment of Motor and Process Skills (AMPS) at baseline and discharged directly home. Primary and secondary outcomes were the AMPS and the modified Rankin Scale (mRS). Results and conclusions: Included were 154 participants (57% men, median age 73 years), and 103 participants completed the study. There were no significant group differences in pre–post changed ADL ability measured by the AMPS. To find the best rehabilitation model to improve the quality of stroke survivors’ motor and process skills needs further research. Patients participating in the ESD rehabilitation models were, compared with traditional treatment, significantly associated with improved ADL ability measured by the mRS when controlling for confounding factors, indicating that patients with social needs and physical impairment after stroke may benefit from ESD rehabilitation models. PMID:26005768

  10. On the use of gridded daily temperature data to calculate the extended spring indices phenological models

    NASA Astrophysics Data System (ADS)

    Zurita-Milla, Raul; Mehdipoor, Hamed; Batarseh, Sana; Ault, Toby; Schwartz, Mark D.

    2014-05-01

    Models that predict the timing of recurrent biological events play an important role in supporting the systematic study of phenological changes at a variety of spatial and temporal scales. One set of such models are the extended Spring indices (SI-x). These models predicts a suite of phenological metrics ("first leaf" and "first bloom," "last freeze" and the "damage index") from temperature data and geographic location (to model the duration of the day). The SI-x models were calibrated using historical phenological and weather observations from the continental US. In particular, the models relied on first leaf and first bloom observations for lilac and honeysuckle and on daily minimum and maximum temperature values from a number of weather stations located near to the sites where phenological observations were made. In this work, we study the use of DAYMET (http://daymet.ornl.gov/) to calculate the SI-x models over the continental USA. DAYMET offers daily gridded maximum and minimum temperature values for the period 1980 to 2012. Using an automatic downloader, we downloaded complete DAYMET temperature time series for the over 1100 geographic locations where historical lilac observations were made. The temperature values were parsed and, using the recently available MATLAB code, the SI-x indices were calculated. Subsequently, the predicted first leaf and first bloom dates were compared with historical lilac observations. The RMSE between predicted and observed lilac leaf/bloom dates was calculated after identifying data from the same geographic location and year. Results were satisfactory for the lilac observations in the Eastern US (e.g. the RMSE for the blooming date was of about 5 days). However, the correspondence between the observed and predicted lilac values in the West was rather week (e.g. RMSE for the blooming date of about 22 days). This might indicate that DAYMET temperature data in this region of the US might contain larger uncertainties due to a more

  11. Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece

    NASA Astrophysics Data System (ADS)

    Voukantsis, Dimitris; Niska, Harri; Karatzas, Kostas; Riga, Marina; Damialis, Athanasios; Vokou, Despoina

    2010-12-01

    Airborne pollen have been associated with allergic symptoms in sensitized individuals, having a direct impact on the overall quality of life of a considerable fraction of the population. Therefore, forecasting elevated airborne pollen concentrations and communicating this piece of information to the public are key issues in prophylaxis and safeguarding the quality of life of the overall population. In this study, we adopt a data-oriented approach in order to develop operational forecasting models (1-7 days ahead) of daily average airborne pollen concentrations of the highly allergenic taxa: Poaceae, Oleaceae and Urticaceae. The models are developed using a representative dataset consisting of pollen and meteorological time-series recorded during the years 1987-2002, in the city of Thessaloniki, Greece. The input variables (features) of the models have been optimized by making use of genetic algorithms, whereas we evaluate the performance of three algorithms: i) multi-Layer Perceptron, ii) support vector regression and iii) regression trees originating from distinct domains of Computational Intelligence (CI), and compare the resulting models with traditional multiple linear regression models. Results show the superiority of CI methods, especially when forecasting several days ahead, compared to traditional multiple linear regression models. Furthermore, the CI models complement each other, resulting to a combined model that performs better than each one separately. The overall performance ranges, in terms of the index of agreement, from 0.85 to 0.93 clearly suggesting the potential operational use of the models. The latter ones can be utilized in provision of personalized and on-time information services, which can improve quality of life of sensitized citizens.

  12. Modelling soil water repellency at the daily scale in Portuguese burnt and unburnt eucalypt stands

    NASA Astrophysics Data System (ADS)

    Nunes, João Pedro; van der Slik, Bart; Marisa Santos, Juliana; Malvar Cortizo, Maruxa; Keizer, Jan Jacob

    2014-05-01

    Soil water repellency can impact soil hydrology, especially soil wetting. This creates a challenge for hydrological modelling in repellency-prone regions, since current models are generally unable to take it into account. This communication focuses on the development and evaluation of a daily water balance model which takes repellency into account, adapted for eucalypt forest plantations in the north-western Iberian Peninsula. The model was developed and tested using data from three eucalypt stands. Two were burnt in 2005, and the data included bi-weekly measurements of soil moisture and water repellency along a transect, during two years. The third was not burnt, and the data included both weekly measurements of soil water repellency and soil moisture along transects, and continuous measurements of soil moisture at one point, performed for one year between 2011 and 2012. All sites showed low repellency during the wet winter season (although less in the unburnt site, as the winter of 2011/12 was comparatively dry) and high repellency during the dry summer season; this seasonal pattern was strongly related with soil moisture fluctuations. The water balance model was based on the Thornthwaite-Mather method. Interception and tree potential evapotranspiration were estimated using satellite imagery (MODIS NDVI), the first by estimating LAI and applying the Gash interception model, and the second using the SAMIR approach. The model itself was modified by first estimating soil water repellency from soil moisture, using an empirical relation taking into account repellent and non-repellent moisture thresholds for each site; and afterwards using soil water repellency as a limiting factor on soil wettability, by limiting the fraction of infiltration which could replenish soil moisture. Results indicate that this simple approach to simulate repellency can provide adequate model performance and can be easily included in hydrological models.

  13. An Evidence-Based Adoption of Technology Model for Remote Monitoring of Elders’ Daily Activities

    PubMed Central

    2011-01-01

    What benefit will new technologies offer if they are inadequately or not used? This work presents a meta-synthesis of adoption of technology related findings from four innovative monitoring intervention research studies with older adults and their informal and/or formal caregivers. Each study employed mixed methods analyses that lead to an understanding of the key variables that influenced adoption of telephone and Internet based wireless remote monitoring technologies by elders and their caregivers. The studies were all conducted in “real world” homes ranging from solo residences to multi-story independent living residential buildings. Insights gained came from issues not found in controlled laboratory environments but in the complex interplay of family-elder-staff dynamics around balancing safety and independence. Findings resulted in an adoption of technology model for remote monitoring of elders’ daily activities derived from evidence based research to advance both practical and theoretical development in the field of gerontechnology. PMID:21423843

  14. Global changes in seasonal means and extremes of precipitation from daily climate model data

    NASA Astrophysics Data System (ADS)

    Russo, Simone; Sterl, Andreas

    2012-01-01

    We investigate simulated changes of seasonal precipitation maxima and means in a future, warmer climate. We use data from the ESSENCE project, in which a 17-member ensemble of climate change simulations in response to the SRES A1b scenario has been carried out using the ECHAM5/MPI-OM climate model. The large size of the data set gives the opportunity to detect the changes of climate extremes and means with high statistical confidence. Daily precipitation data are used to calculate the seasonal precipitation maximum and the seasonal mean. Modeled precipitation data appear consistent with observation-based data from the Global Precipitation Climatology Project. The data are split into six time periods of 25 years to get independent time series. The seasonal peaks are modeled by using the generalized extreme value distribution, while empirical distributions are used to study changes of the seasonal precipitation mean. Finally, we use an empirical method to detect changes of occurrence of very wet and dry periods. Results from these model simulations indicate that over most of the world precipitation maxima will increase in the future. Seasonal means behave differently. In many regions they are decreasing or not increasing. The occurrence of very wet periods is strongly increasing during boreal winter in the extratropics and decreasing in the tropics. In summary, wet regions become wetter and dry regions become drier.

  15. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy

    NASA Astrophysics Data System (ADS)

    Kurtulus, Bedri; Razack, Moumtaz

    2010-02-01

    SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.

  16. BIOMAP A Daily Time Step, Mechanistic Model for the Study of Ecosystem Dynamics

    NASA Astrophysics Data System (ADS)

    Wells, J. R.; Neilson, R. P.; Drapek, R. J.; Pitts, B. S.

    2010-12-01

    BIOMAP simulates competition between two Plant Functional Types (PFT) at any given point in the conterminous U.S. using a time series of daily temperature (mean, minimum, maximum), precipitation, humidity, light and nutrients, with PFT-specific rooting within a multi-layer soil. The model employs a 2-layer canopy biophysics, Farquhar photosynthesis, the Beer-Lambert Law for light attenuation and a mechanistic soil hydrology. In essence, BIOMAP is a re-built version of the biogeochemistry model, BIOME-BGC, into the form of the MAPSS biogeography model. Specific enhancements are: 1) the 2-layer canopy biophysics of Dolman (1993); 2) the unique MAPSS-based hydrology, which incorporates canopy evaporation, snow dynamics, infiltration and saturated and unsaturated percolation with ‘fast’ flow and base flow and a ‘tunable aquifer’ capacity, a metaphor of D’Arcy’s Law; and, 3) a unique MAPSS-based stomatal conductance algorithm, which simultaneously incorporates vapor pressure and soil water potential constraints, based on physiological information and many other improvements. Over small domains the PFTs can be parameterized as individual species to investigate fundamental vs. potential niche theory; while, at more coarse scales the PFTs can be rendered as more general functional groups. Since all of the model processes are intrinsically leaf to plot scale (physiology to PFT competition), it essentially has no ‘intrinsic’ scale and can be implemented on a grid of any size, taking on the characteristics defined by the homogeneous climate of each grid cell. Currently, the model is implemented on the VEMAP 1/2 degree, daily grid over the conterminous U.S. Although both the thermal and water-limited ecotones are dynamic, following climate variability, the PFT distributions remain fixed. Thus, the model is currently being fitted with a ‘reproduction niche’ to allow full dynamic operation as a Dynamic General Vegetation Model (DGVM). While global simulations

  17. A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

    NASA Astrophysics Data System (ADS)

    Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

    2016-07-01

    Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

  18. Daily streamflow modelling and assessment based on the curve-number technique

    NASA Astrophysics Data System (ADS)

    Choi, Jin-Yong; Engel, Bernard A.; Chung, Ha Woo

    2002-11-01

    A cell-based long-term hydrological model (CELTHYM) that can be integrated with a geographical information system (GIS) was developed to predict continuous stream flow from small agricultural watersheds. The CELTHYM uses a cell-by-cell soil moisture balance approach. For surface runoff estimation, the curve number technique considering soil moisture on a daily basis was used, and release rate was used to estimate baseflow. Evapotranspiration was computed using the FAO modified Penman equation that considered land-use-based crop coefficients, soil moisture and the influence of topography on radiation. A rice paddy field water budget model was also adapted for the specific application of the model to East Asia. Model sensitivity analysis was conducted to obtain operational information about the model calibration parameters. The CELTHYM was calibrated and verified with measured runoff data from the WS#1 and WS#3 watersheds of the Seoul National University, Department of Agricultural Engineering, in Hwaseong County, Kyounggi Province, South Korea. The WS#1 watershed is comprised of about 35·4% rice paddy fields and 42·3% forest, whereas the WS#3 watershed is about 85·0% forest and 11·5% rice paddy fields. The CELTHYM was calibrated for the parameter release rate, K, and soil moisture storage coefficient, STC, and results were compared with the measured runoff data for 1986. The validation results for WS#1 considering all daily stream flow were poor with R2, E2 and RMSE having values of 0·40, -6·63 and 9·69 (mm), respectively, but validation results for days without rainfall were statistically significant (R2 = 0·66). Results for WS#3 showed good agreement with observed data for all days, and R2, E2 and RMSE were 0·92, 0·91 and 2·23 (mm), respectively, suggesting potential for CELTHYM application to other watersheds. The direct runoff and water balance components for watershed WS#1 with significant areas of paddy fields did not perform well, suggesting that

  19. Estimating the daily course of Konza Prairie latent Heat fluxes using a modified Tergra model

    NASA Astrophysics Data System (ADS)

    Hope, Allen S.

    1992-11-01

    The Tergra model simulates the daily course of water and energy flows through the soil-plantatmosphere system and was intended for use with remotely sensed data. In its original form, the model is not well suited to estimating spatial patterns of latent heat flux (λE) in the Konza Prairie since the determination of canopy resistance requires knowledge of vegetation height, and the defined relationship between leaf water potential and rc is specific to C3 plants. The canopy resistance component of Tergra was replaced by a routine that includes the calculation of minimum canopy resistance (rcm) from the normalized difference vegetation index (NDVI) and stress adjustment factors for leaf water potential and vapor pressure deficit to determine actual canopy resistance (rc). The relationship between rc and leaf water potential is defined for both C3 and C4 plants, and total λE is obtained from the sum of the proportional contributions from these two vegetation classes. The modified Tergra model (Tergra-2) was tested using input and flux data collected at four First ISLSCP Field Experiment (FIFE) sites during three periods characterized by different soil moisture conditions. Tergra-2 was found to be a good simulator of λE and in most cases gave substantially better results than those obtained using the original model. The greatest inaccuracy using Tergra-2 occurred under extremely dry soil moisture conditions, whereas absolute errors for both models tended to increase around solar noon. Leaf water potential was the dominant stress factor affecting modeled rc. It was concluded that vapor pressure deficit and leaf water potential should not be regarded as completely independent factors affecting rc. A brief comparison of modeled and observed canopy temperatures is presented and discussed.

  20. Statistical modeling of daily and subdaily stream temperatures: Application to the Methow River Basin, Washington

    NASA Astrophysics Data System (ADS)

    Caldwell, R. J.; Gangopadhyay, S.; Bountry, J.; Lai, Y.; Elsner, M. M.

    2013-07-01

    Management of water temperatures in the Columbia River Basin (Washington) is critical because water projects have substantially altered the habitat of Endangered Species Act listed species, such as salmon, throughout the basin. This is most important in tributaries to the Columbia, such as the Methow River, where the spawning and rearing life stages of these cold water fishes occurs. Climate change projections generally predict increasing air temperatures across the western United States, with less confidence regarding shifts in precipitation. As air temperatures rise, we anticipate a corresponding increase in water temperatures, which may alter the timing and availability of habitat for fish reproduction and growth. To assess the impact of future climate change in the Methow River, we couple historical climate and future climate projections with a statistical modeling framework to predict daily mean stream temperatures. A K-nearest neighbor algorithm is also employed to: (i) adjust the climate projections for biases compared to the observed record and (ii) provide a reference for performing spatiotemporal disaggregation in future hydraulic modeling of stream habitat. The statistical models indicate the primary drivers of stream temperature are maximum and minimum air temperature and stream flow and show reasonable skill in predictability. When compared to the historical reference time period of 1916-2006, we conclude that increases in stream temperature are expected to occur at each subsequent time horizon representative of the year 2020, 2040, and 2080, with an increase of 0.8 ± 1.9°C by the year 2080.

  1. Models for obtaining daily global solar irradiation from air temperature data

    NASA Astrophysics Data System (ADS)

    Paulescu, M.; Fara, L.; Tulcan-Paulescu, E.

    2006-03-01

    The study presents a critical assessment of the possibility of global solar irradiation computation by using air temperature instead of sunshine duration with the classical Ångström equations. The reason for this approach comes from the fact that, although the air temperature is a worldwide measured meteorological parameter, this is rarely used in solar radiation estimation techniques. More than that, the literature is very silent concerning the testing of such models in Eastern Europe. Two new global solar irradiation models (to be called AEAT) related to solar irradiation under clear sky conditions and having the minimum and maximum daily air temperature as input parameters were tested and compared with others from the literature against data measured at five stations in Romania in the year 2000. The accuracy of AEAT is acceptable and comparable to that of the models which use sunshine duration or cloud amount as input parameters. Since temperature-based Ångström correlations are strongly sensitive to origin, the approach for AEAT as a tool for potential users is presented in detail. Additionally reported is a new method to increase the generality of AEAT concerning the extension of the geographical application area. Based on overall results it was concluded that air temperature successfully substitutes sunshine duration in the estimation of the available solar energy.

  2. Analysis and modelling of spatio-temporal properties of daily rainfall over the Danube basin

    NASA Astrophysics Data System (ADS)

    Serinaldi, F.; Kilsby, C. G.

    2012-04-01

    Central and Eastern Europe are prone to severe floods due to heavy rainfall that cause societal and economic damages, ranging from agriculture to water resources, from the insurance/reinsurance sector to the energy industry. To improve the flood risk analysis, a better characterisation and modelling of the rainfall patterns over this area, which involves the Danube river watershed, is strategically important. In this study, we analyse the spatio-temporal properties of a large data set of daily rainfall time series from 15 countries in the Central Eastern Europe through different lagged and non-lagged indices of associations that quantify both the overall dependence and extreme dependence of pairwise observations. We also show that these measures are linked to each other and can be written in a unique and coherent notation within the copula framework. Moreover, the lagged version of these measures allows exploring some important spatio-temporal properties of the rainfall fields. The exploratory analysis is complemented by the preliminary results of a spatio-temporal rainfall simulation performed via a compound model based upon the Generalized Additive Models for Location, Scale and Shape (GAMLSS) and meta-elliptical multivariate distributions.

  3. Comparison of Daily GRACE Gravity Field and Numerical Water Storage Models for De-aliasing of Satellite Gravimetry Observations

    NASA Astrophysics Data System (ADS)

    Zenner, L.; Bergmann-Wolf, I.; Dobslaw, H.; Gruber, T.; Güntner, A.; Wattenbach, M.; Esselborn, S.; Dill, R.

    2014-11-01

    Reducing aliasing effects of insufficiently modelled high-frequent, non-tidal mass variations of the atmosphere, the oceans and the hydrosphere in gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission is the topic of this study. The signal content of the daily GRACE gravity field model series (ITG-Kalman) is compared to high-frequency bottom pressure variability and terrestrially stored water variations obtained from recent numerical simulations from an ocean circulation model (OMCT) and two hydrological models (WaterGAP Global Hydrology Model, Land Surface Discharge Model). Our results show that daily estimates of ocean bottom pressure from the most recent OMCT simulations and the daily ITG-Kalman solutions are able to explain up to 40 % of extra-tropical sea-level variability in the Southern Ocean. In contrast to this, the daily ITG-Kalman series and simulated continental total water storage variability largely disagree at periods below 30 days. Therefore, as long as no adequate hydrological model will become available, the daily ITG-Kalman series can be regarded as a good initial proxy for high-frequency mass variations at a global scale. As a second result of this study, based on monthly solutions as well as daily observation residuals, it is shown that applying this GRACE-derived de-aliasing model supports the determination of the time-variable gravity field from GRACE data and the subsequent geophysical interpretation. This leads us to the recommendation that future satellite concepts for determining mass variations in the Earth system should be capable of observing higher frequeny signals with sufficient spatial resolution.

  4. Coping with Daily Stressors: Modeling Intraethnic Variation in Mexican American Adolescents

    ERIC Educational Resources Information Center

    Aldridge, Arianna A.; Roesch, Scott C.

    2008-01-01

    Using daily diary methodology, 67 Mexican American adolescents completed measures assessing daily stressors experienced, specific coping strategies employed with reference to these stressors, and indices of psychological health over 5 consecutive days. With respect to coping usage, adolescents reported they most often used planning and least often…

  5. Differential Associations and Daily Smoking of Adolescents: The Importance of Same-Sex Models

    ERIC Educational Resources Information Center

    Nofziger, Stacey; Lee, Hye-Ryeon

    2006-01-01

    This article examines whether the importance of parents, siblings, best friends, and romantic interests are sex-specific in predicting daily juvenile smoking. Juveniles who smoke daily are strongly influenced by prosmoking attitudes and behaviors of same-sex family members. However, peers remain the most important associations in predicting daily…

  6. Crop yield monitoring based on a photosynthetic sterility model using NDVI and daily meteorological data

    NASA Astrophysics Data System (ADS)

    Kaneko, Daijiro

    2007-10-01

    This research is intended to develop a model to monitor rice yields using the photosynthetic yield index, which integrates solar radiation and air temperature effects on photosynthesis and grain-filling from heading to ripening. Monitoring crop production using remotely sensed and daily meteorological data can provide an important early warning of poor crop production to Asian countries, with their still-growing populations, and also to Japan, which produces insufficient grain for its population. The author improved a photosynthesis-and-sterility-based crop production CPI index to crop yield index CYI, which estimates rice yields, in place of the crop situation index CSI. The CSI gives a percentage of rice yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature sterility, and high-temperature injury by incorporating: solar radiation, effective air temperature, normalized difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. The method is based on routine observation data, enabling automated monitoring of crop production at arbitrary regions without special observations. The method aims to quantity grain production at an early stage to raise the alarm in Asian countries, which are facing climate fluctuation through this century of global warming.

  7. Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models

    NASA Astrophysics Data System (ADS)

    Asong, Zilefac E.; Khaliq, M. N.; Wheater, H. S.

    2016-02-01

    Based on the Generalized Linear Model (GLM) framework, a multisite stochastic modelling approach is developed using daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. Temperature is modeled using a two-stage normal-heteroscedastic model by fitting mean and variance components separately. Likewise, precipitation occurrence and conditional precipitation intensity processes are modeled separately. The relationship between precipitation and temperature is accounted for by using transformations of precipitation as covariates to predict temperature fields. Large scale atmospheric covariates from the National Center for Environmental Prediction Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate these models for the 1971-2000 period. Validation of the developed models is performed on both pre- and post-calibration period data. Results of the study indicate that the developed models are able to capture spatiotemporal characteristics of observed precipitation and temperature fields, such as inter-site and inter-variable correlation structure, and systematic regional variations present in observed sequences. A number of simulated weather statistics ranging from seasonal means to characteristics of temperature and precipitation extremes and some of the commonly used climate indices are also found to be in close agreement with those derived from observed data. This GLM-based modelling approach will be developed further for multisite statistical downscaling of Global Climate Model outputs to explore climate variability and change in this region of Canada.

  8. LAPSUS-D: testing a new daily sediment delivery model in a meso-scale Mediterranean catchment in Northern Israel

    NASA Astrophysics Data System (ADS)

    Keesstra, S.; Temme, A.; Wittenberg, L.; Greenbaum, N.

    2011-12-01

    LAPSUS-D is a new sediment delivery model that simulates sediment and water discharge at the temporal scale of one day and the spatial scale of a meso-scale catchment, using only generally available data, such as a DEM, a soil map, a land use map, daily discharge and precipitation data and a general idea of the soil depths in the catchment. The landscape evolution model LAPSUS (Schoorl, 2002) was adapted to model sediment yield on a daily basis instead of the original annual basis. LAPSUS-D uses the water balance per cell and for the entire catchment to calculate water and sediment transport and this feature enables calibration with measured daily discharge at the outlet. With this information the model can be calibrated for the water flow part which will give a good indication of the possibilities for sediment transport. First testing of the model in catchments with a temperate climate in SW Poland and SE Germany showed that the model is able to postdict the daily outflow well, when focusing on peak discharge characteristics. These hydrological features are the main determining factor for the generation of sediment outflow, and therefore most important to be able predict sediment delivery in a catchment well. In a Mediterranean catchment in Israel, Nahal Oren, the model was tested for this new climate setting. Moreover, the first testing of the models sediment module was conducted. This indicated that the model post-dicts the sediment yield within the right order of magnitude and has potential to function as a tool for catchment managers. Keywords: LAPSUS-D, daily sediment yield model, meso-scale catchment, Mediterranean climate, Nahal Oren, Israel

  9. Stochastic modelling of daily rainfall in Nigeria: intra-annual variation of model parameters

    NASA Astrophysics Data System (ADS)

    Jimoh, O. D.; Webster, P.

    1999-09-01

    A Markov model of order 1 may be used to describe the occurrence of wet and dry days in Nigeria. Such models feature two parameter sets; P01 to characterise the probability of a wet day following a dry day and P11 to characterise the probability of a wet day following a wet day. The model parameter sets, when estimated from historical records, are characterised by a distinctive seasonal behaviour. However, the comparison of this seasonal behaviour between rainfall stations is hampered by the noise reflecting the high variability of parameters on successive days. The first part of this article is concerned with methods for smoothing these inherently noisy parameter sets. Smoothing has been approached using Fourier series, averaging techniques, or a combination thereof. It has been found that different methods generally perform well with respect to estimation of the average number of wet events and the frequency duration curves of wet and dry events. Parameterisation of the P01 parameter set is more successful than the P11 in view of the relatively small number of wet events lasting two or more days. The second part of the article is concerned with describing the regional variation in smoothed parameter sets. There is a systematic variation in the P01 parameter set as one moves northwards. In contrast, there is limited regional variation in the P11 set. Although this regional variation in P01 appears to be related to the gradual movement of the Inter Tropical Convergence Zone, the contrasting behaviour of the two parameter sets is difficult to explain on physical grounds.

  10. A general model for estimation of daily global solar radiation using air temperatures and site geographic parameters in Southwest China

    NASA Astrophysics Data System (ADS)

    Li, Mao-Fen; Fan, Li; Liu, Hong-Bin; Guo, Peng-Tao; Wu, Wei

    2013-01-01

    Estimation of daily global solar radiation (Rs) from routinely measured temperature data has been widely developed and used in many different areas of the world. However, many of them are site specific. It is assumed that a general model for estimating daily Rs using temperature variables and geographical parameters could be achieved within a climatic region. This paper made an attempt to develop a general model to estimate daily Rs using routinely measured temperature data (maximum (Tmax, °C) and minimum (Tmin, °C) temperatures) and site geographical parameters (latitude (La, °N), longitude (Ld, °E) and altitude (Alt, m)) for Guizhou and Sichuan basin of southwest China, which was classified into the hot summer and cold winter climate zone. Comparison analysis was carried out through statistics indicators such as root mean squared error of percentage (RMSE%), modeling efficiency (ME), coefficient of residual mass (CRM) and mean bias error (MBE). Site-dependent daily Rs estimating models were calibrated and validated using long-term observed weather data. A general formula was then obtained from site geographical parameters and the better fit site-dependent models with mean RMSE% of 38.68%, mean MBE of 0.381 MJ m-2 d-1, mean CRM of 0.04 and mean ME value of 0.713.

  11. Development of models to inform a national Daily Landslide Hazard Assessment for Great Britain

    NASA Astrophysics Data System (ADS)

    Dijkstra, Tom A.; Reeves, Helen J.; Dashwood, Claire; Pennington, Catherine; Freeborough, Katy; Mackay, Jonathan D.; Uhlemann, Sebastian S.; Chambers, Jonathan E.; Wilkinson, Paul B.

    2015-04-01

    were combined with records of observed landslide events to establish which antecedent effective precipitation (AEP) signatures of different duration could be used as a pragmatic proxy for the occurrence of landslides. It was established that 1, 7, and 90 days AEP provided the most significant correlations and these were used to calculate the probability of at least one landslide occurring. The method was then extended over the period 2006 to 2014 and the results evaluated against observed occurrences. It is recognised that AEP is a relatively poor proxy for simulating effective stress conditions along potential slip surfaces. However, the temporal pattern of landslide probability compares well to the observed occurrences and provides a potential benefit to assist with the DLHA. Further work is continuing to fine-tune the model for landslide type, better spatial resolution of effective precipitation input and cross-reference to models that capture changes in water balance and conditions along slip surfaces. The latter is facilitated by intensive research at several field laboratories, such as the Hollin Hill site in Yorkshire, England. At this site, a decade of activity has generated a broad range of research and a wealth of data. This paper reports on one example of recent work; the characterisation of near surface hydrology using infiltration experiments where hydrological pathways are captured, among others, by electrical resistivity tomography. This research, which has further developed our understanding of soil moisture movement in a heterogeneous landslide complex, has highlighted the importance of establishing detailed ground models to enable determination of landslide potential at high resolution. In turn, the knowledge gained through this research is used to enhance the expertise for the daily landslide hazard assessments at a national scale.

  12. Evaluation of satellite-based, modeled-derived daily solar radiation data for the continental U.S.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these to...

  13. Modified Daily Undulating Periodization Model Produces Greater Performance Than a Traditional Configuration in Powerlifters.

    PubMed

    Zourdos, Michael C; Jo, Edward; Khamoui, Andy V; Lee, Sang-Rok; Park, Bong-Sup; Ormsbee, Michael J; Panton, Lynn B; Contreras, Robert J; Kim, Jeong-Su

    2016-03-01

    The primary aim of this study was to compare 2 daily undulating periodization (DUP) models on one-repetition maximum (1RM) strength in the squat, bench press, deadlift, total volume (TV) lifted, and temporal hormone response. Eighteen male, college-aged (21.1 ± 1.9 years) powerlifters participated in this study and were assigned to one of 2 groups: (a) traditional DUP training with a weekly training order: hypertrophy-specific, strength-specific, and power-specific training (HSP, n = 9) or (b) modified DUP training with a weekly training order: hypertrophy-specific, power-specific, and strength-specific training (HPS, n = 9). Both groups trained 3 nonconsecutive days per week for 6 weeks and performed the squat, bench press, and deadlift exercises. During hypertrophy and power sessions, subjects performed a fixed number of sets and repetitions but performed repetitions until failure at a given percentage during strength sessions to compare TV. Testosterone and cortisol were measured at pretesting and posttesting and before each strength-specific day. Hypertrophy, power, and strength produced greater TV in squat and bench press (p ≤ 0.05) than HSP, but not for deadlift (p > 0.05). For squat and deadlift, there was no difference between groups for 1RM (p > 0.05); however, HPS exhibited greater increases in 1RM bench press than HSP (p ≤ 0.05). Effect sizes (ES) showed meaningful differences (ES > 0.50) in favor of HPS for squat and bench press 1RM. Testosterone decreased (p ≤ 0.05) at weeks 5 and 6 and cortisol decline at weeks 3 and 4. However, neither hormone was different at posttesting compared with pretesting (p > 0.05). Our findings suggest that an HPS configuration of DUP has enhanced performance benefits compared with HSP. PMID:26332783

  14. Evaluation of Once-Daily Vancomycin against Methicillin-Resistant Staphylococcus aureus in a Hollow-Fiber Infection Model

    PubMed Central

    Bulitta, Jürgen B.; Lodise, Thomas P.; D'Hondt, Rebecca E.; Kulawy, Robert; Louie, Arnold; Drusano, George L.

    2012-01-01

    For methicillin-resistant Staphylococcus aureus (MRSA) infections, data suggest that the clinical response is significantly better if the total vancomycin area under the concentration-time curve (AUC)/MIC ratio is ≥400. While the AUC/MIC ratio is the accepted pharmacokinetic/pharmacodynamic (PK/PD) index for vancomycin, this target has been achieved using multiple daily doses. We are unaware of a systematically designed dose fractionation study to compare the bactericidal activity of once-daily administration to that of traditional twice-daily administration. A dose fractionation study was performed with vancomycin in an in vitro hollow-fiber infection model against an MRSA USA300 strain (MIC of 0.75 μg/ml) using an inoculum of ∼106 CFU/ml. The three vancomycin regimens evaluated for 168 h were 2 g every 24 h (q24h) as a 1-h infusion, 1 g q12h as a 1-h infusion, and 2 g q24h as a continuous infusion. Free steady-state concentrations (assuming 45% binding) for a total daily AUC/MIC ratio of ≥400 were simulated for all regimens. A validated liquid chromatography-tandem mass spectrometry method was used to determine vancomycin concentrations. Although once-daily and twice-daily dosage regimens exhibited total trough concentrations of <15 μg/ml, all regimens achieved similar bactericidal activities between 24 and 168 h and suppressed the amplification of nonsusceptible subpopulations. No colonies were found on agar plates with 3× MIC for any of the treatment arms. Overall, the results suggest that once-daily vancomycin administration is feasible from a PK/PD perspective and merits further inquiry in the clinical arena. PMID:22083484

  15. Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment

    NASA Astrophysics Data System (ADS)

    Redman, Jeremiah D.; Holmes, Heather A.; Balachandran, Sivaraman; Maier, Marissa L.; Zhai, Xinxin; Ivey, Cesunica; Digby, Kyle; Mulholland, James A.; Russell, Armistead G.

    2016-09-01

    The impacts of emissions sources on air quality in St. Louis, Missouri are assessed for use in acute health effects studies. However, like many locations in the United States, the speciated particulate matter (PM) measurements from regulatory monitoring networks in St. Louis are only available every third day. The power of studies investigating acute health effects of air pollution is reduced when using one-in-three day source impacts compared to daily source impacts. This paper presents a temporal interpolation model to estimate daily speciated PM2.5 mass concentrations and source impact estimates using one-in-three day measurements. The model is used to interpolate 1-in-3 day source impact estimates and to interpolate the 1-in-3 day PM species concentrations prior to source apportionment (SA). Both approaches are compared and evaluated using two years (June 2001-May 2003) of daily data from the St. Louis Midwest Supersite (STL-SS). Data withholding is used to simulate a 1-in-3 day data set from the daily data to evaluate interpolated estimates. After evaluation using the STL-SS data, the model is used to estimate daily source impacts at another site approximately seven kilometers (7 km) northwest of the STL-SS (Blair); results between the sites are compared. For interpolated species concentrations, the model performs better for secondary species (sulfate, nitrate, ammonium, and organic carbon) than for primary species (metals and elemental carbon), likely due to the greater spatial autocorrelation of secondary species. Pearson correlation (R) values for sulfate, nitrate, ammonium, elemental carbon, and organic carbon ranged from 0.61 (elemental carbon, EC2) to 0.97 (sulfate). For trace metals, the R values ranged from 0.31 (Ba) to 0.81 (K). The interpolated source impact estimates also indicated a stronger correlation for secondary sources. Correlations of the secondary source impact estimates based on measurement data and interpolation data ranged from 0.68 to 0

  16. Daily fluctuations in teachers' well-being: a diary study using the Job Demands-Resources model.

    PubMed

    Simbula, Silvia

    2010-10-01

    The study tests the dynamic nature of the Job Demands-Resources model with regard to both motivational and health impairment processes. It does so by examining whether daily fluctuations in co-workers' support (i.e., a typical job resource) and daily fluctuations in work/family conflict (i.e., a typical job demand) predict day-levels of job satisfaction and mental health through work engagement and exhaustion, respectively. A total of 61 schoolteachers completed a general questionnaire and a daily survey over a period of five consecutive work days. Multilevel analyses provided evidence for both the above processes. Consistently with the hypotheses, our results showed that day-level work engagement mediated the impact of day-level co-workers' support on day-level job satisfaction and day-level mental health, after general levels of work engagement and outcome variables had been controlled for. Moreover, day-level exhaustion mediated the relationship between day-level work/family conflict and day-level job satisfaction and day-level mental health after general levels of exhaustion and outcome variables had been controlled for. These findings provide new insights into the dynamic psychological processes that determine daily fluctuations in employee well-being. Such insights may be transformed into job redesign strategies and other interventions designed to enhance work-related psychological well-being on a daily level. PMID:20352542

  17. One-Day Offset between Simulated and Observed Daily Hydrographs: An Exploration of the Issue in Automatic Model Calibration

    NASA Astrophysics Data System (ADS)

    Asadzadeh, M.; Leon, L.; Yang, W.

    2014-12-01

    The literature of hydrologic modelling shows that in daily simulation of the rainfall-runoff relationship, the simulated hydrograph response to some rainfall events happens one day earlier than the observed one. This one-day offset issue results in significant residuals between the simulated and observed hydrographs and adversely impacts the model performance metrics that are based on the aggregation of daily residuals. Based on the analysis of sub-daily rainfall and runoff data sets in this study, the one-day offset issue appears to be inevitable when the same time interval, e.g. the calendar day, is used to measure daily rainfall and runoff data sets. This is an error introduced through data aggregation and needs to be properly addressed before calculating the model performance metrics. Otherwise, the metrics would not represent the modelling quality and could mislead the automatic calibration of the model. In this study, an algorithm is developed to scan the simulated hydrograph against the observed one, automatically detect all one-day offset incidents and shift the simulated hydrograph of those incidents one day forward before calculating the performance metrics. This algorithm is employed in the automatic calibration of the Soil and Water Assessment Tool that is set up for the Rouge River watershed in Southern Ontario, Canada. Results show that with the proposed algorithm, the automatic calibration to maximize the daily Nash-Sutcliffe (NS) identifies a solution that accurately estimates the magnitude of peak flow rates and the shape of rising and falling limbs of the observed hydrographs. But, without the proposed algorithm, the same automatic calibration finds a solution that systematically underestimates the peak flow rates in order to perfectly match the timing of simulated and observed peak flows.

  18. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

    USGS Publications Warehouse

    Yuan, W.; Liu, S.; Zhou, G.; Tieszen, L.L.; Baldocchi, D.; Bernhofer, C.; Gholz, H.; Goldstein, Allen H.; Goulden, M.L.; Hollinger, D.Y.; Hu, Y.; Law, B.E.; Stoy, P.C.; Vesala, T.; Wofsy, S.C.

    2007-01-01

    The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks. ?? 2007 Elsevier B.V. All rights reserved.

  19. Suitability of global circulation model downscaled BCCA daily precipitation for local hydrologic applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Monthly precipitation projections for various climate change scenarios have been available for over a decade. More recently, Bias Corrected Constructed Analogue (BCCA) daily precipitation projections have been available for climate change investigations. In this study, the direct use of BCCA precipi...

  20. Prioritization of pesticides based on daily dietary exposure potential as determined from the SHEDS model

    EPA Science Inventory

    A major pathway for exposure to many pesticides is through diet. The objectives were to rank pesticides by comparing their calculated daily dietary exposure as determined by EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) to single pesticides for different age groups ...

  1. A NEW MODEL TO ESTIMATE DAILY ENERGY EXPENDITURE FOR WINTERING WATERFOWL

    EPA Science Inventory

    Activity budgets of wintering waterfowl have been widely used to assess habitat quality. However, when factors such as prey abundance or protection from exposure to cold or wind determine quality, measures of daily energy expenditure (DEE) may be more appropriate for this purpos...

  2. A model-based approach for the evaluation of once daily dosing of lamivudine in HIV-infected children

    PubMed Central

    Piana, Chiara; Zhao, Wei; Adkison, Kimberly; Burger, David; Jacqz-Aigrain, Evelyne; Danhof, Meindert; Della Pasqua, Oscar

    2014-01-01

    Aim Little attention has been paid to the effects of compliance and prescription practice on treatment outcome in HIV-infected children. In this context, an evaluation of the role of covariates on pharmacokinetics is required to establish the impact of differences in dosing regimens. Here we investigate whether a once daily dosing regimen of lamivudine provides comparable exposure to the currently approved paediatric regimen. Methods A hypothetical group of 180 patients between 3 months and 12 years old was used to evaluate the impact of body weight on systemic exposure to lamivudine. Simulation scenarios were evaluated using AUC and Cmax as parameters of interest. The analysis was performed using a population pharmacokinetic model previously implemented in nonmem v.6.2. Results The simulations show that once daily dosing of lamivudine yields comparable exposure to historical values observed in children and adults, both for liquid and solid dosage forms. Simulated steady-state AUC(0–24 h) and Cmax values after once daily doses ranged respectively from 9.95 mg l−1 h and 1.9 mg l−1 for children lighter than 14 kg to 13.75 mg l−1 h and 3.0 mg l−1 for children heavier than 30 kg. These values are comparable or higher than historical values observed after once daily dosing in children and adults. Conclusions Our findings illustrate how dosing regimens can be evaluated taking into account the effects of developmental growth on drug disposition. Most importantly, they suggest that the reduction in dosing frequency to once daily leads to comparable lamivudine exposure, as observed after administration of a twice daily dosing regimen. PMID:24118047

  3. Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

    NASA Technical Reports Server (NTRS)

    Mu, M.; Randerson, J. T.; vanderWerf, G. R.; Giglio, L.; Kasibhatla, P.; Morton, D.; Collatz, G. J.; DeFries, R. S.; Hyer, E. J.; Prins, E. M.; Griffith, D. W. T.; Wunch, D.; Toon, G. C.; Sherlock, V.; Wennberg, P. O.

    2011-01-01

    Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003.2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS) ]derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top ]down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from

  4. Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide

    NASA Astrophysics Data System (ADS)

    Mu, M.; Randerson, J. T.; van der Werf, G. R.; Giglio, L.; Kasibhatla, P.; Morton, D.; Collatz, G. J.; Defries, R. S.; Hyer, E. J.; Prins, E. M.; Griffith, D. W. T.; Wunch, D.; Toon, G. C.; Sherlock, V.; Wennberg, P. O.

    2011-12-01

    Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003-2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top-down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from

  5. Evaluation of Downscaled CMIP5 Model Skill in Simulating Daily Maximum Temperature Over the Southeastern United States

    NASA Astrophysics Data System (ADS)

    Keellings, D.

    2015-12-01

    Downscaled CMIP5 climate projections of maximum daily temperature from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections archive are examined regionally over the southeastern U.S. Three measures of model skill (means-based, distribution-based, extreme-based) are utilized to assess the ability of 15 downscaled models to simulate daily maximum temperature observations. A new test is proposed to determine statistical significance of the probability density function based skill measures. Skill scores are found to be generally high for all three measures throughout the study region, but lower scores are present in coastal and mountainous areas. Application of the significance test shows that while the skill scores may be high they are not significantly higher than could be expected at random in some areas. The distribution-based skill scores are not significant in much of Florida and the Appalachians. The extreme-based skill scores are not significant in more than 90% of the region for all models investigated. The findings suggest that although the downscaled models have simulated observed means well and are a good match to the entire distribution of observations, they are not simulating the occurrence of extreme (above 90th percentile) maximum daily temperatures.

  6. A Comparison of Satellite Based, Modeled Derived Daily Solar Radiation Data with Observed Data for the Continental US

    NASA Technical Reports Server (NTRS)

    White, Jeffrey W.; Hoogenboom, Gerrit; Wilkens, Paul W.; Stackhouse, Paul W., Jr.; Hoell, James M.

    2010-01-01

    Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these tools. Daily solar radiation (SRAD) data are especially problematic because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. The Prediction Of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project at the NASA Langley Research Center estimates daily solar radiation based on data that are derived from satellite observations of outgoing visible radiances and atmospheric parameters based upon satellite observations and assimilation models. The solar data are available for a global 1 degree x 1 degree coordinate grid. SRAD can also be estimated based on attenuation of extraterrestrial radiation (Q0) using daily temperature and rainfall data to estimate the optical thickness of the atmosphere. This study compares daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (SRADOB), as well as with values that were estimated with the WGENR solar generator. WGENR was used both with daily temperature and precipitation records from the stations reporting solar data and records from the NOAA Cooperative Observer Program (COOP), thus providing two additional sources of solar data, SRADWG and SRADCO. Values of SRADNP for different grid cells consistently showed higher correlations (typically 0.85 to 0.95) with SRADOB data than did SRADWG or SRADCO for sites within the corresponding cells. Mean values of SRADOB, SRADWG and SRADNP for sites within a grid cell usually were within 1 MJm-2d-1 of each other, but NASA/POWER values averaged 1.1 MJm-2d-1 lower than SRADOB. The magnitude of this bias was greater at lower latitudes and during summer months and may be at

  7. Daily and Hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

    NASA Technical Reports Server (NTRS)

    Mu, M.; Randerson, J. T.; van der Werf, G. R.; Giglio, L.; Kasibhatla, P.; Morton, D.; Collatz, G. J.; DeFries, R. S.; Hyer, E. J.; Prins, E. M.; Griffith, D. W. T.; Wunch, D.; Toon, G. C.; Sherlock, V.; Wennberg, P. O.

    2011-01-01

    Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We distributed monthly GFED3 emissions during 2003-2009 on a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) active fire observations. We found that patterns of daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of bunting in savannas. On diurnal timescales, our analysis of the GOES active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top-down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

  8. Achieving Consistent Multiple Daily Low-Dose Bacillus anthracis Spore Inhalation Exposures in the Rabbit Model

    PubMed Central

    Barnewall, Roy E.; Comer, Jason E.; Miller, Brian D.; Gutting, Bradford W.; Wolfe, Daniel N.; Director-Myska, Alison E.; Nichols, Tonya L.; Taft, Sarah C.

    2012-01-01

    Repeated low-level exposures to biological agents could occur before or after the remediation of an environmental release. This is especially true for persistent agents such as B. anthracis spores, the causative agent of anthrax. Studies were conducted to examine aerosol methods needed for consistent daily low aerosol concentrations to deliver a low-dose (less than 106 colony forming units (CFU) of B. anthracis spores) and included a pilot feasibility characterization study, acute exposure study, and a multiple 15 day exposure study. This manuscript focuses on the state-of-the-science aerosol methodologies used to generate and aerosolize consistent daily low aerosol concentrations and resultant low inhalation doses to rabbits. The pilot feasibility characterization study determined that the aerosol system was consistent and capable of producing very low aerosol concentrations. In the acute, single day exposure experiment, targeted inhaled doses of 1 × 102, 1 × 103, 1 × 104, and 1 × 105 CFU were used. In the multiple daily exposure experiment, rabbits were exposed multiple days to targeted inhaled doses of 1 × 102, 1 × 103, and 1 × 104 CFU. In all studies, targeted inhaled doses remained consistent from rabbit-to-rabbit and day-to-day. The aerosol system produced aerosolized spores within the optimal mass median aerodynamic diameter particle size range to reach deep lung alveoli. Consistency of the inhaled dose was aided by monitoring and recording respiratory parameters during the exposure with real-time plethysmography. Overall, the presented results show that the animal aerosol system was stable and highly reproducible between different studies and over multiple exposure days. PMID:22919662

  9. Coping with Daily Thermal Variability: Behavioural Performance of an Ectotherm Model in a Warming World

    PubMed Central

    Rojas, José M.; Castillo, Simón B.; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

    2014-01-01

    Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24±0, 24±4 and 24±8°C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, – i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance. PMID:25207653

  10. Coping with daily thermal variability: behavioural performance of an ectotherm model in a warming world.

    PubMed

    Rojas, José M; Castillo, Simón B; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

    2014-01-01

    Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24 ± 0, 24 ± 4 and 24 ± 8 °C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, --i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance. PMID:25207653

  11. Prioritization of pesticides based on daily dietary exposure potential as determined from the SHEDS model.

    PubMed

    Melnyk, Lisa Jo; Wang, Zhaohui; Li, Zhilin; Xue, Jianping

    2016-10-01

    A major pathway for exposure to many pesticides is through diet. The objectives were to rank pesticides by comparing their calculated daily dietary exposure as determined by EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) to single pesticides for different age groups to acceptable daily intakes (ADI), characterize pesticide trends in exposures over different time periods, and determine commodities contributing to pesticide exposures. SHEDS was applied, using Pesticide Data Program (PDP) (1991-2011) and pesticide usage data on crops from USDA combined with NHANES dietary consumption data, to generate exposure estimates by age group. ADI data collected from EPA, WHO, and other sources were used to rank pesticides based on relativeness of the dietary exposure potential to ADI by age groups. Sensitivity analysis provided trends in pesticide exposures. Within SHEDS, commodities contributing the majority of pesticides with greatest exposure potential were determined. The results indicated that the highest ranking pesticides were methamidophos and diazinon which exceeded 100% of the ADI. Sensitivity analysis indicated that exposure to methamidophos, diazinon, malathion, ethion and formetanate hydrochloride had a marked decrease from 1991-1999 to 2000-2011. Contributions analysis indicated that apples, mushroom, carrots, and lettuce contributed to diazinon exposure. Beans and pepper contributed to methamidophos exposure. PMID:27497764

  12. UNITED STATES METEOROLOGICAL DATA - DAILY AND HOURLY FILES TO SUPPORT PREDICTIVE EXPOSURE MODELING

    EPA Science Inventory

    ORD numerical models for pesticide exposure include a model of spray drift (AgDisp), a cropland pesticide persistence model (PRZM), a surface water exposure model (EXAMS), and a model of fish bioaccumulation (BASS). A unified climatological database for these models has been asse...

  13. A Count Model to Study the Correlates of 60 Min of Daily Physical Activity in Portuguese Children

    PubMed Central

    Borges, Alessandra; Gomes, Thayse Natacha; Santos, Daniel; Pereira, Sara; dos Santos, Fernanda K.; Chaves, Raquel; Katzmarzyk, Peter T.; Maia, José

    2015-01-01

    This study aimed to present data on Portuguese children (aged 9–11 years) complying with moderate-to-vigorous physical activity (MVPA) guidelines, and to identify the importance of correlates from multiple domains associated with meeting the guidelines. Physical activity (PA) was objectively assessed by accelerometry throughout seven days on 777 children. A count model using Poisson regression was used to identify the best set of correlates that predicts the variability in meeting the guidelines. Only 3.1% of children met the recommended daily 60 min of MVPA for all seven days of the week. Further, the Cochrane–Armitage chi-square test indicated a linear and negative trend (p < 0.001) from none to all seven days of children complying with the guidelines. The count model explained 22% of the variance in meeting MVPA guidelines daily. Being a girl, having a higher BMI, belonging to families with higher income, sleeping more and taking greater time walking from home to a sporting venue significantly reduced the probability of meeting daily recommended MVPA across the seven days. Furthermore, compared to girls, increasing sleep time in boys increased their chances of compliance with the MVPA recommendations. These results reinforce the relevance of considering different covariates’ roles on PA compliance when designing efficient intervention strategies to promote healthy and active lifestyles in children. PMID:25730296

  14. A count model to study the correlates of 60 min of daily physical activity in Portuguese children.

    PubMed

    Borges, Alessandra; Gomes, Thayse Natacha; Santos, Daniel; Pereira, Sara; dos Santos, Fernanda K; Chaves, Raquel; Katzmarzyk, Peter T; Maia, José

    2015-03-01

    This study aimed to present data on Portuguese children (aged 9-11 years) complying with moderate-to-vigorous physical activity (MVPA) guidelines, and to identify the importance of correlates from multiple domains associated with meeting the guidelines. Physical activity (PA) was objectively assessed by accelerometry throughout seven days on 777 children. A count model using Poisson regression was used to identify the best set of correlates that predicts the variability in meeting the guidelines. Only 3.1% of children met the recommended daily 60 min of MVPA for all seven days of the week. Further, the Cochrane-Armitage chi-square test indicated a linear and negative trend (p<0.001) from none to all seven days of children complying with the guidelines. The count model explained 22% of the variance in meeting MVPA guidelines daily. Being a girl, having a higher BMI, belonging to families with higher income, sleeping more and taking greater time walking from home to a sporting venue significantly reduced the probability of meeting daily recommended MVPA across the seven days. Furthermore, compared to girls, increasing sleep time in boys increased their chances of compliance with the MVPA recommendations. These results reinforce the relevance of considering different covariates' roles on PA compliance when designing efficient intervention strategies to promote healthy and active lifestyles in children. PMID:25730296

  15. Optimal vitamin D3 daily intake of 2000IU inferred from modeled solar exposure of ancestral humans in Northern Tanzania.

    PubMed

    Krzyścin, Janusz W; Guzikowski, Jakub; Rajewska-Więch, Bonawentura

    2016-06-01

    Recently, high serum 25-hydroxyvitamin D concentration (~110nmol/L) was found in the Hadza tribe still keeping ancient hunter-gather life style. This level could serve as optimal vitamin D level that was built during millennia of human evolution. The personal vitamin D3 effective solar exposures of the Hadza adults are estimated using radiative model simulations with input from the satellite observations over lake Eyasi (3.7°S, 35.0°E). The calculations are carried out assuming the Hadza typical clothing habits and specific scenarios of the out-door activity comprising early morning and late afternoon working time in sun and prolonged midday siesta in the shade. The modeled doses received by the Hadza are converted to the vitamin D3 effective daily doses pertaining to the lighter skinned persons. We propose a novel formula to get adequate vitamin D level - exposure of 1/3 MED around local noon to 1/3 part of the whole body during warm sub-period of the year in the low- and mid-latitude regions. Such daily solar exposure is equivalent to ~2000IU of vitamin D3 taken orally. For many contemporary humans with limited out-door activity habit achieving such daily norm requires vitamin D3 supplementation of 2000IU throughout the whole year. PMID:27043260

  16. A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

    NASA Astrophysics Data System (ADS)

    Duveiller, G.; Donatelli, M.; Fumagalli, D.; Zucchini, A.; Nelson, R.; Baruth, B.

    2015-10-01

    Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure

  17. A Procedure for Inter-Comparing the Skill of Regional-Scale Air Quality Model Simulations of Daily Maximum 8-Hour Ozone Concentrations

    EPA Science Inventory

    An operational model evaluation procedure is described to quantitatively assess the relative skill among several regionalscale air quality models simulating various percentiles of the cumulative frequency distribution of observed daily maximum 8-h ozone concentrations. Bootstrap ...

  18. Estimating daily air temperature across the Southeastern United States using high-resolution satellite data: A statistical modeling study.

    PubMed

    Shi, Liuhua; Liu, Pengfei; Kloog, Itai; Lee, Mihye; Kosheleva, Anna; Schwartz, Joel

    2016-04-01

    Accurate estimates of spatio-temporal resolved near-surface air temperature (Ta) are crucial for environmental epidemiological studies. However, values of Ta are conventionally obtained from weather stations, which have limited spatial coverage. Satellite surface temperature (Ts) measurements offer the possibility of local exposure estimates across large domains. The Southeastern United States has different climatic conditions, more small water bodies and wetlands, and greater humidity in contrast to other regions, which add to the challenge of modeling air temperature. In this study, we incorporated satellite Ts to estimate high resolution (1km×1km) daily Ta across the southeastern USA for 2000-2014. We calibrated Ts-Ta measurements using mixed linear models, land use, and separate slopes for each day. A high out-of-sample cross-validated R(2) of 0.952 indicated excellent model performance. When satellite Ts were unavailable, linear regression on nearby monitors and spatio-temporal smoothing was used to estimate Ta. The daily Ta estimations were compared to the NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) model. A good agreement with an R(2) of 0.969 and a mean squared prediction error (RMSPE) of 1.376°C was achieved. Our results demonstrate that Ta can be reliably predicted using this Ts-based prediction model, even in a large geographical area with topography and weather patterns varying considerably. PMID:26717080

  19. Comparing an annual and daily time-step model for predicting field-scale P loss

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Several models with varying degrees of complexity are available for describing P movement through the landscape. The complexity of these models is dependent on the amount of data required by the model, the number of model parameters needed to be estimated, the theoretical rigor of the governing equa...

  20. The Daily Erosion Project - lessons learned by expanding a statewide erosion and runoff model beyond state boundaries

    NASA Astrophysics Data System (ADS)

    Gelder, Brian; James, David; Herzmann, Daryl; Scott, Victoria; Cruse, Richard; Laflen, John; Flanagan, Dennis; Frankenberger, Jim; Opsomer, Jean

    2015-04-01

    The Daily Erosion Project (DEP) model is an extension of the Iowa Daily Erosion Project (IDEP) 2.0 model to additional states in the US, initially Kansas and Minnesota. DEP provides comprehensive and dynamic estimates of sediment delivery, soil erosion, and hill slope runoff for agricultural land areas across the area of interest. The integration of high spatial and temporal resolution precipitation and climate data, high resolution LiDAR topography, spatially variable soil properties from current SSURGO information, remotely sensed crop rotation and residue management data, provides increased spatial resolution of runoff and erosion estimates over IDEP 1.0, the previous version derived from land management survey data. The reasoning used to define a representative measurement unit, subcatchments of Hydrologic Unit Code (HUC) 12 watersheds (each approximately 1000 hectares) throughout the modeled area along with methods used to incorporate disparate LiDAR datasets as well as varying crop rotations and management practices and their effects on model accuracy will be discussed.

  1. El Niño-Southern Oscillation influence on winter maximum daily precipitation in California in a spatial model

    NASA Astrophysics Data System (ADS)

    Shang, Hongwei; Yan, Jun; Zhang, Xuebin

    2011-11-01

    Recent studies have found that the El Niño-Southern Oscillation (ENSO) has statistically significant influences on extreme precipitation. A limitation of most existing work is that a separate generalized extreme value (GEV) distribution is fitted for each individual site. Such models cannot address important questions that involve events jointly defined across multiple sites; for instance, what is the probability that the 50 year return levels of three sites in the vicinity of a city occur in the same season? With the latest statistical methodology for spatial extremes, we fit max-stable process models to winter maximum daily precipitation of 192 sites in California over 55 years. A composite likelihood approach is used since the full likelihood is unavailable either analytically or numerically. In addition to latitude, longitude, and elevation, the Southern Oscillation Index (SOI) is incorporated into the parameters of the marginal GEV models. We find that, in a spatial context, the ENSO has a significant influence on the extreme precipitation in California by shifting the location parameter of the GEV distributions, with higher values of the SOI corresponding to lower maximum winter daily precipitation. The joint spatial model is used to assess risks concerning joint extremal events at network of sites with spatial dependence properly accounted for. The probability of extremal events occurring at multiple sites in the same season is found to be much higher than what would be expected under the independence assumption.

  2. VT-1161 Dosed Once Daily or Once Weekly Exhibits Potent Efficacy in Treatment of Dermatophytosis in a Guinea Pig Model

    PubMed Central

    Hoekstra, W. J.; Moore, W. R.; Schotzinger, R. J.; Long, L.

    2015-01-01

    Current therapies used to treat dermatophytoses such as onychomycosis are effective but display room for improvement in efficacy, safety, and convenience of dosing. We report here that the investigational agent VT-1161 displays potent in vitro antifungal activity against dermatophytes, with MIC values in the range of ≤0.016 to 0.5 μg/ml. In pharmacokinetic studies supporting testing in a guinea pig model of dermatophytosis, VT-1161 plasma concentrations following single oral doses were dose proportional and persisted at or above the MIC values for at least 48 h, indicating potential in vivo efficacy with once-daily and possibly once-weekly dosing. Subsequently, in a guinea pig dermatophytosis model utilizing Trichophyton mentagrophytes and at oral doses of 5, 10, or 25 mg/kg of body weight once daily or 70 mg/kg once weekly, VT-1161 was statistically superior to untreated controls in fungal burden reduction (P < 0.001) and improvement in clinical scores (P < 0.001). The efficacy profile of VT-1161 was equivalent to those for doses and regimens of itraconazole and terbinafine except that VT-1161 was superior to itraconazole when each drug was dosed once weekly (P < 0.05). VT-1161 was distributed into skin and hair, with plasma and tissue concentrations in all treatment and regimen groups ranging from 0.8 to 40 μg/ml (or μg/g), at or above the MIC against the isolate used in the model (0.5 μg/ml). These data strongly support the clinical development of VT-1161 for the oral treatment of onychomycosis using either once-daily or once-weekly dosing regimens. PMID:25605358

  3. VT-1161 dosed once daily or once weekly exhibits potent efficacy in treatment of dermatophytosis in a guinea pig model.

    PubMed

    Garvey, E P; Hoekstra, W J; Moore, W R; Schotzinger, R J; Long, L; Ghannoum, M A

    2015-04-01

    Current therapies used to treat dermatophytoses such as onychomycosis are effective but display room for improvement in efficacy, safety, and convenience of dosing. We report here that the investigational agent VT-1161 displays potent in vitro antifungal activity against dermatophytes, with MIC values in the range of ≤0.016 to 0.5 μg/ml. In pharmacokinetic studies supporting testing in a guinea pig model of dermatophytosis, VT-1161 plasma concentrations following single oral doses were dose proportional and persisted at or above the MIC values for at least 48 h, indicating potential in vivo efficacy with once-daily and possibly once-weekly dosing. Subsequently, in a guinea pig dermatophytosis model utilizing Trichophyton mentagrophytes and at oral doses of 5, 10, or 25 mg/kg of body weight once daily or 70 mg/kg once weekly, VT-1161 was statistically superior to untreated controls in fungal burden reduction (P < 0.001) and improvement in clinical scores (P < 0.001). The efficacy profile of VT-1161 was equivalent to those for doses and regimens of itraconazole and terbinafine except that VT-1161 was superior to itraconazole when each drug was dosed once weekly (P < 0.05). VT-1161 was distributed into skin and hair, with plasma and tissue concentrations in all treatment and regimen groups ranging from 0.8 to 40 μg/ml (or μg/g), at or above the MIC against the isolate used in the model (0.5 μg/ml). These data strongly support the clinical development of VT-1161 for the oral treatment of onychomycosis using either once-daily or once-weekly dosing regimens. PMID:25605358

  4. Daily uranium excretion in German peacekeeping personnel serving on the Balkans compared to ICRP model prediction.

    PubMed

    Oeh, U; Li, W B; Höllriegl, V; Giussani, A; Schramel, P; Roth, P; Paretzke, H G

    2007-01-01

    An investigation was performed to assess a possible health risk of depleted uranium (DU) for residents and German peacekeeping personnel serving on the Balkans. In order to evaluate a possible DU intake, the urinary uranium excretions of volunteers were collected and analysed using inductively coupled plasma mass spectrometry (ICP-MS). In total, more than 1300 urine samples from soldiers, civil servants and unexposed controls of different genders and ages were analysed to determine uranium excretion parameters. All participating volunteers, aged 3-92 y, were grouped according to their gender and age for evaluation. The results of the investigation revealed no significant difference between the unexposed controls and the peacekeeping personnel. In addition, the geometric means of the daily urinary excretion in peacekeeping personnel, ranging from 3 to 23 ng d(-1) for different age groups, fall toward the lower end of renal uranium excretion values published for unexposed populations in literature. The measured data were compared with the International Commission on Radiological Protection prediction for the intake of natural uranium by unexposed members of the public. The two data sets are in good agreement, indicating that no relevant intake of additional uranium, either natural or DU, has appeared for German peacekeeping personnel serving on the Balkans. PMID:17567762

  5. Effects of Model Resolution and Subgrid-Scale Physics on the Simulation of Daily Precipitation in the Continental United States

    SciTech Connect

    Duffy, P B; Iorio, J P; Govindasamy, B; Thompson, S L; Khairoutdinov, M; Randall, D

    2004-07-28

    We analyze simulations of the global climate performed at a range of spatial resolutions to assess the effects of horizontal spatial resolution on the ability to simulate precipitation in the continental United States. The model investigated is the CCM3 general circulation model. We also preliminarily assess the effect of replacing cloud and convective parameterizations in a coarse-resolution (T42) model with an embedded cloud-system resolving model (CSRM). We examine both spatial patterns of seasonal-mean precipitation and daily-timescale temporal variability of precipitation in the continental United States. For DJF and SON, high-resolution simulations produce spatial patterns of seasonal-mean precipitation that agree more closely with observed precipitation patterns than do results from the same model (CCM3) at coarse resolution. However, in JJA and MAM, there is little improvement in spatial patterns of seasonal-mean precipitation with increasing resolution, particularly in the Southeast. This is owed to the dominance of convective (i.e., parameterized) precipitation in these two seasons. We further find that higher-resolution simulations have more realistic daily precipitation statistics. In particular, the well-known tendency at coarse resolution to have too many days with weak precipitation and not enough intense precipitation is partially eliminated in higher-resolution simulations. However, even at the highest resolution examined here (T239), the simulated intensity of the mean and of high-percentile daily precipitation amounts is too low. This is especially true in the Southeast, where the most extreme events occur. A new GCM, in which a cloud-resolving model (CSRM) is embedded in each grid cell and replaces convective and stratiform cloud parameterizations, solves this problem, and actually produces too much precipitation in the form of extreme events. However, in contrast to high-resolution versions of CCM3, this model produces little improvement in

  6. Age-related Changes in Respiratory Function and Daily Living. A Tentative Model Including Psychosocial Variables, Respiratory Diseases and Cognition.

    PubMed

    Facal, David; González-Barcala, Francisco-Javier

    2016-01-01

    Changes in respiratory function are common in older populations and affect quality of life, social relationships, cognitive function and functional capacity. This paper reviews evidence reported in medical and psychological journals between 2000 and 2014 concerning the impact of changes in respiratory function on daily living in older adults. A tentative model establishes relationships involving respiratory function, cognitive function and functional capacities. The conclusion stresses the need for both longitudinal studies, to establish causal pathways between respiratory function and psychosocial aspects in aging, and intervention studies. PMID:26593253

  7. Estimating the daily course of Konza Prairie latent heat fluxes using a modified Tergra model

    NASA Technical Reports Server (NTRS)

    Hope, Allen S.

    1992-01-01

    Experimental tests of the Tergra-2 model are based on data collected under moderately wet to wet and very dry soil moisture conditions. Further testing of the model under intermediate soil moisture conditions is required and additional testing under very dry conditions may lead to modifications that make the model more suitable to water-stressed conditions. Combining the Tergra model with a soil evaporation routine should enhance the accuracy of the model and allow it to be employed in situations where vapor fluxes are not almost solely attributable to transpiration.

  8. Adapting remotely sensed snow data for daily flow modeling on the Upper Humber River, Newfoundland and Labrador

    NASA Astrophysics Data System (ADS)

    Tom, Melissa

    This thesis investigated the use of remotely sensed snow information to help improve flood forecasting in western Newfoundland's Humber River Basin. Flood forecasting on the Humber River is important because of the large population settlements within the Humber Valley. In this research, two types of remotely sensed snow data were considered for analysis: (1) snow cover (or snow extent) and (2) snow water equivalent (SWE). The majority of this thesis focuses on the remotely sensed snow cover data. Moderate Resolution Imaging Spectroradiometer (MODIS) Terra snow cover images were acquired over the Humber Valley watershed throughout the snowmelt period, from March to June, for the years 2000 to 2009. MODIS is an optical sensor on NASA's (National Aeronautics and Space Administration) Earth Observing System (EOS) Terra and Aqua satellites. Its daily temporal data are advantageous and the data are free and easily accessible. Daily snow cover data were extracted from the National Snow and Ice Data Center (NSIDC) daily snow product, specifically MOD10A1: a product derived from MODIS data, using a custom EASI script run in PCI Geomatica. PCI Geomatica is a robust remote sensing and image processing software. One major obstacle, regarding the acquisition of MODIS imagery over the Humber Valley watershed, is the presence of over 50% cloud cover for 80% of the days on average from March to June every year. This was a concern for data collection: affecting the sample size of acquired data and the accuracy of the snow cover data. When cloud cover is high there is a greater chance that it may be misclassified as snow and/or snow is misclassified as cloud cover. For this reason, a cloud-cover threshold was determined. The Rango-Martinec snowmelt runoff model, a widely used degree-day model which incorporates snow cover data as a direct input, was evaluated. It was found that the next day's flow is highly dependent on the previous day's flow and less dependent on the

  9. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects.

    PubMed

    Warren, Joshua L; Stingone, Jeanette A; Herring, Amy H; Luben, Thomas J; Fuentes, Montserrat; Aylsworth, Arthur S; Langlois, Peter H; Botto, Lorenzo D; Correa, Adolfo; Olshan, Andrew F

    2016-07-20

    Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post-conception gestational weeks 2-8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5 ) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post-conception gestational weeks 2-8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post-conception gestational weeks 2-8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post-conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26853919

  10. Revision and proposed modification for a total maximum daily load model for Upper Klamath Lake, Oregon

    USGS Publications Warehouse

    Wherry, Susan A.; Wood, Tamara M.; Anderson, Chauncey W.

    2015-01-01

    Using the extended 1991–2010 external phosphorus loading dataset, the lake TMDL model was recalibrated following the same procedures outlined in the Phase 1 review. The version of the model selected for further development incorporated an updated sediment initial condition, a numerical solution method for the chlorophyll a model, changes to light and phosphorus factors limiting algal growth, and a new pH-model regression, which removed Julian day dependence in order to avoid discontinuities in pH at year boundaries. This updated lake TMDL model was recalibrated using the extended dataset in order to compare calibration parameters to those obtained from a calibration with the original 7.5-year dataset. The resulting algal settling velocity calibrated from the extended dataset was more than twice the value calibrated with the original dataset, and, because the calibrated values of algal settling velocity and recycle rate are related (more rapid settling required more rapid recycling), the recycling rate also was larger than that determined with the original dataset. These changes in calibration parameters highlight the uncertainty in critical rates in the Upper Klamath Lake TMDL model and argue for their direct measurement in future data collection to increase confidence in the model predictions.

  11. Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers.

    PubMed

    Chellali, M R; Abderrahim, H; Hamou, A; Nebatti, A; Janovec, J

    2016-07-01

    Neural network (NN) models were evaluated for the prediction of suspended particulates with aerodynamic diameter less than 10-μm (PM10) concentrations. The model evaluation work considered the sequential hourly concentration time series of PM10, which were measured at El Hamma station in Algiers. Artificial neural network models were developed using a combination of meteorological and time-scale as input variables. The results were rather satisfactory, with values of the coefficient of correlation (R (2)) for independent test sets ranging between 0.60 and 0.85 and values of the index of agreement (IA) between 0.87 and 0.96. In addition, the root mean square error (RMSE), the mean absolute error (MAE), the normalized mean squared error (NMSE), the absolute relative percentage error (ARPE), the fractional bias (FB), and the fractional variance (FS) were calculated to assess the performance of the model. It was seen that the overall performance of model 3 was better than models 1 and 2. PMID:27040548

  12. Modelling wet and dry spells for daily rainfall data series: an application to irrigation management in North-West Italy

    NASA Astrophysics Data System (ADS)

    Ferraris, Stefano; Agnese, Carmelo; Baiamonte, Giorgio; Cat Berro, Daniele; Mercalli, Luca

    2016-04-01

    Rainfall time variability is relevant for agricultural production. The daily time scale is often used in modelling crop and soil water balance. In this work a novel statistical analysis of wet and dry spells is presented, together with an application in an Italian area characterised by a relevant climate spatial variability, due to the presence of both high mountains (e.g.: Mont Blanc) and of the Mediterranean Sea. Statistical analysis of the sequences of rainy days, wet spells (WS), and that of no-rainy days, dry spells (DS), could be carried out separately (as widely applied in the past) or jointly, by introducing the inter-arrival time (IT), representing the time elapsed between two subsequent rainy days. Investigating on daily rainfall data series recorded in Sicily, Agnese et al. (2014) found that IT statistics can be described by the 3-parameter Lerch distribution; in turn, WS and DS distributions can be easily derived from IT distribution. Alternatively, the knowledge of both WS and DS distributions allow deriving IT distribution; in this case, WScan be described by the well-accepted geometric distribution, whereas the 2-parameter polylogarithm distribution can be used for DS, as recently suggested (Agnese et al., 2012) in place of the previously used 1-parameter logarithmic distribution (Chatfield, 1966). In this work, by using some daily rainfall data series recorded in Alpine and Sub-Alpine Areas, the equivalence between the above-mentioned approaches is showed. Furthermore, some interesting relationships between respective parameters are also illustrated. A simple soil water model is then used, using this rainfall statistical model, in order to evaluate the irrigation efficiency as a consequence of variations in the timing of surface irrigation, following the approach described in the paper of Canone et al. (2015). Agnese C., Baiamonte G., Cammalleri C., Cat Berro D., Ferraris S., Mercalli L. (2012). "Statistical analysis of inter-arrival times of

  13. Daily Stress and Alcohol Consumption: Modeling Between-Person and Within-Person Ethnic Variation in Coping Behavior*

    PubMed Central

    Aldridge-Gerry, Arianna A.; Roesch, Scott C.; Villodas, Feion; McCabe, Cameron; Leung, Queenie K.; Da Costa, Morgan

    2011-01-01

    Objective: Using a daily diary approach, the current study evaluated the relationship between coping and alcohol consumption using a large, multiethnic sample. The primary goals of this study were to (a) identify coping strategies that are either protective or risk factors for alcohol consumption and (b) model between-ethnic and within-ethnic group variation for these relations. Method: College students (N = 365, 69.0% female) were recruited via flyers, course/club presentations, and university seminars. Participants completed Internet-based daily diaries over the course of 5 days and reported specifically on a target stressful event, how they coped with the stressful event, and the amount of alcohol consumed on a daily level. Results: Use of more avoidance-oriented coping strategies (minimization of stressor, emotional rumination) and social support were significantly associated with more alcohol consumption. Ethnicity, however, did moderate some coping—alcohol associations. Use of religious coping was associated with less alcohol consumption and minimization of the stressor was associated with more alcohol consumption in African Americans; use of social support was associated with more alcohol consumption in Asian Americans; and use of problem-focused coping was associated with less alcohol consumption in Whites. Conclusions: Three maladaptive or risky coping strategies with respect to alcohol consumption were identified using an ecologically valid methodology. However, ethnic-specific variation of these risky (and protective) coping factors was identified. The findings highlight the importance of considering both between-ethnic and within-ethnic group variation with respect to the stress/coping and alcohol consumption. PMID:21138719

  14. Daily water and sediment discharges from selected rivers of the eastern United States; a time-series modeling approach

    USGS Publications Warehouse

    Fitzgerald, Michael G.; Karlinger, Michael R.

    1983-01-01

    Time-series models were constructed for analysis of daily runoff and sediment discharge data from selected rivers of the Eastern United States. Logarithmic transformation and first-order differencing of the data sets were necessary to produce second-order, stationary time series and remove seasonal trends. Cyclic models accounted for less than 42 percent of the variance in the water series and 31 percent in the sediment series. Analysis of the apparent oscillations of given frequencies occurring in the data indicates that frequently occurring storms can account for as much as 50 percent of the variation in sediment discharge. Components of the frequency analysis indicate that a linear representation is reasonable for the water-sediment system. Models that incorporate lagged water discharge as input prove superior to univariate techniques in modeling and prediction of sediment discharges. The random component of the models includes errors in measurement and model hypothesis and indicates no serial correlation. An index of sediment production within or between drain-gage basins can be calculated from model parameters.

  15. Combination of spaceborne sensor(s) and 3-D aerosol models to assess global daily near-surface air quality

    NASA Astrophysics Data System (ADS)

    Kacenelenbogen, M.; Redemann, J.; Russell, P. B.

    2009-12-01

    Aerosol Particulate Matter (PM), measured by ground-based monitoring stations, is used as a standard by the EPA (Environmental Protection Agency) to evaluate daily air quality. PM monitoring is particularly important for human health protection because the exposure to suspended particles can contribute, among others, to lung and respiratory diseases and even premature death. However, most of the PM monitoring stations are located close to cities, leaving large areas without any operational data. Satellite remote sensing is well suited for a global coverage of the aerosol load and can provide an independent and supplemental data source to in situ monitoring. Nevertheless, PM at the ground cannot easily be determined from satellite AOD (Aerosol Optical Depth) without additional information on the optical/microphysical properties and vertical distribution of the aerosols. The objective of this study is to explore the efficacy and accuracy of combining a 3-D aerosol transport model and satellite remote sensing as a cost-effective approach for estimating ground-level PM on a global and daily basis. The estimation of the near-surface PM will use the vertical distribution (and, if possible, the physicochemical properties) of the aerosols inferred from a transport model and the measured total load of particles in the atmospheric column retrieved by satellite sensor(s). The first step is to select a chemical transport model (CTM) that provides “good” simulated aerosol vertical profiles. A few global (e.g., WRF-Chem-GOCART) or regional (e.g., MM5-CMAQ, PM-CAMx) CTM will be compared during selected airborne campaigns like ARCTAS-CARB (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites- California Air Resources Board). The next step will be to devise an algorithm that combines the satellite and model data to infer PM mass estimates at the ground, after evaluating different spaceborne instruments and possible multi-sensor combinations.

  16. Estimation of genetic parameters for average daily gain using models with competition effects

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Components of variance for ADG with models including competition effects were estimated from data provided by Pig Improvement Company on 11,235 pigs from 4 selected lines of swine. Fifteen pigs with average age of 71 d were randomly assigned to a pen by line and sex and taken off test after approxi...

  17. Daily evapotranspiration estimates by scaling instantaneous latent heat flux derived from a two-source model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Radiometric brightness temperature can be used in energy balance models that estimate sensible and latent heat fluxes of the land surface. However, brightness temperature is usually available only at one time of day when acquired from aircraft, fine-scale satellite platforms, or infrared thermometer...

  18. Evaluation of statistical and rainfall-runoff models for predicting historical daily streamflow time series in the Des Moines and Iowa River watersheds

    USGS Publications Warehouse

    Farmer, William H.; Knight, Rodney R.; Eash, David A.; Kasey J. Hutchinson; Linhart, S. Mike; Christiansen, Daniel E.; Archfield, Stacey A.; Over, Thomas M.; Kiang, Julie E.

    2015-01-01

    Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.

  19. A spatial agent-based model for the simulation of adults' daily walking within a city.

    PubMed

    Yang, Yong; Diez Roux, Ana V; Auchincloss, Amy H; Rodriguez, Daniel A; Brown, Daniel G

    2011-03-01

    Environmental effects on walking behavior have received attention in recent years because of the potential for policy interventions to increase population levels of walking. Most epidemiologic studies describe associations of walking behavior with environmental features. These analyses ignore the dynamic processes that shape walking behaviors. A spatial agent-based model (ABM) was developed to simulate people's walking behaviors within a city. Each individual was assigned properties such as age, SES, walking ability, attitude toward walking and a home location. Individuals perform different activities on a regular basis such as traveling for work, for basic needs, and for leisure. Whether an individual walks and the amount she or he walks is a function of distance to different activities and her/his walking ability and attitude toward walking. An individual's attitude toward walking evolves over time as a function of past experiences, walking of others along the walking route, limits on distances walked per day, and attitudes toward walking of the other individuals within her/his social network. The model was calibrated and used to examine the contributions of land use and safety to socioeconomic differences in walking. With further refinement and validation, ABMs may help to better understand the determinants of walking and identify the most promising interventions to increase walking. PMID:21335269

  20. A Spatial Agent-Based Model for the Simulation of Adults’ Daily Walking Within a City

    PubMed Central

    Yang, Yong; Roux, Ana V. Diez; Auchincloss, Amy H.; Rodriguez, Daniel A.; Brown, Daniel G.

    2012-01-01

    Environmental effects on walking behavior have received attention in recent years because of the potential for policy interventions to increase population levels of walking. Most epidemiologic studies describe associations of walking behavior with environmental features. These analyses ignore the dynamic processes that shape walking behaviors. A spatial agent-based model (ABM) was developed to simulate peoples’ walking behaviors within a city. Each individual was assigned properties such as age, SES, walking ability, attitude toward walking and a home location. Individuals perform different activities on a regular basis such as traveling for work, for shopping, and for recreation. Whether an individual walks and the amount she or he walks is a function distance to different activities and her or his walking ability and attitude toward walking. An individual’s attitude toward walking evolves over time as a function of past experiences, walking of others along the walking route, limits on distances walked per day, and attitudes toward walking of the other individuals within her/his social network. The model was calibrated and used to examine the contributions of land use and safety to socioeconomic differences in walking. With further refinement and validation, ABMs may help to better understand the determinants of walking and identify the most promising interventions to increase walking. PMID:21335269

  1. Evaluating daily and extreme seasonal precipitations over continental Africa from a Regional Climate Model Simulation

    NASA Astrophysics Data System (ADS)

    Bamba Sylla, Mouhamadou; Mariotti, Laura; Coppola, Erika; Giorgi, Filippo

    2010-05-01

    Spatial and temporal variability of rainfall over Africa offers considerable challenges on climate change over the region. This is because of the complexity of regional climates in Africa and their associated geographic features. Adding to that complexity are deserts, vegetation variations, numerous mountain chains that can alter regional climate and weather patterns, the influence of the land-sea contrast due to the presence of several large lakes and the surrounding Indian and Atlantic Oceans. This leads to strong fluctuations of rainfall that may cause drought and flood in the region. Therefore, being able to simulate the spatial distribution of mean precipitation is quite important but also capturing their occurrences and intensities is critical for Africa whose economy relies on rain-fed agriculture. The International Centre for Theoretical Physics (ICTP) Regional Climate Model (RegCM3), driven by the newly produced ERA-Interim reanalysis, is used to investigate this issue. Several indices, such as the number of wet days and their intensity, maximum dry and wet spells length and the frequency of heavy precipitation days, are used to characterize the spatial variability of seasonal extreme rainfall over continental Africa. Model results are compared to both TRMM and FEWS rainfall data. They indicate that although the model captures the location of longest and shortest wet and dry spells, it tends to extend slightly the wet spell length around mountainous regions and along the ITCZ and the dry spell length over northern and southern Africa during austral and boreal summer respectively. This is mainly visible when compared to FEWS. Extension of the wet spell length may be partly related to the overestimation of the number of wet days. As a result, the intensity due to the wet days only is slightly overpredicted in these regions. This is, in turn, linked to the tendency of the RegCM3 to produce more intense and convective rainfall events in the ITCZ and the ZAB as

  2. [Inversion Model and Daily Variation of Total Phosphorus Concentrations in Taihu Lake Based on GOCI Data].

    PubMed

    Du, Cheng-gong; Li, Yun-mei; Wang, Qiao; Zhu, Li; Lü, Heng

    2016-03-15

    The TP concentration is an important index of water quality and an important influencing factor of eutrophication and algae blooms. Remote sensing technology has advantages of wide scope and high time limited efficacy. Monitoring the concentration of TP by satellite remote sensing is important for the study of water quality and eutrophication. In situ datasets collected during the three times of experiments in Taihu Lake between 2013 and 2014 were used to develop the TP inversion model based on GOCI data. The GOCI data in spring, summer, autumn and winter in 2014 were selected to analyze the time and space changes of TP concentration in Taihu Lake. The results showed that the TP algorithm was built up based on the variables, which was to use the eight band combination of GOCI data as variable, and build model using Multi factor linear regression method. The algorithm achieved more accurate TP estimation with R² = 0.898, MAPE = 14.296%, RMSE = 0.026 mg · L⁻¹. Meantime, a analysis on the precision of the model by using the measured sample points and the synchronous satellite images with MAPE = 33.642%, 22.551%, RMSE = 0.076 mg · L⁻¹, 0.028 mg · L⁻¹ on August 5, 2014 and October 24, 2014. Through the analysis of the 30 images on the four days of the four seasons, it showed that the absolute concentration of total phosphorus was different in different seasons. But temporal and spatial distribution of total phosphorus concentration was similar in the morning and afternoon. In spatial distribution, the TP concentration in Meiliang Bay, Zhushan Bay, Gonghu Bay, Xiaomei Port and Changdou Port in the southwest coast was at a continuously high position. The TP concentration change in different regions was influenced by wind direction, wind speed and other factors. The TP concentration highest in the morning, and then gradually decreased, this phenomenon reflected that the TP concentration was affected by temperature and light. PMID:27337876

  3. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    NASA Technical Reports Server (NTRS)

    Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and

  4. West African monsoon intraseasonal activity and its daily precipitation indices in regional climate models: diagnostics and challenges

    NASA Astrophysics Data System (ADS)

    Poan, E. D.; Gachon, P.; Dueymes, G.; Diaconescu, E.; Laprise, R.; Seidou Sanda, I.

    2016-02-01

    The West African monsoon intraseasonal variability has huge socio-economic impacts on local populations but understanding and predicting it still remains a challenge for the weather prediction and climate scientific community. This paper analyses an ensemble of simulations from six regional climate models (RCMs) taking part in the coordinated regional downscaling experiment, the ECMWF ERA-Interim reanalysis (ERAI) and three satellite-based and observationally-constrained daily precipitation datasets, to assess the performance of the RCMs with regard to the intraseasonal variability. A joint analysis of seasonal-mean precipitation and the total column water vapor (also called precipitable water—PW) suggests the existence of important links at different timescales between these two variables over the Sahel and highlights the relevance of using PW to follow the monsoon seasonal cycle. RCMs that fail to represent the seasonal-mean position and amplitude of the meridional gradient of PW show the largest discrepancies with respect to seasonal-mean observed precipitation. For both ERAI and RCMs, spectral decompositions of daily PW as well as rainfall show an overestimation of low-frequency activity (at timescales longer than 10 days) at the expense of the synoptic (timescales shorter than 10 days) activity. Consequently, the effects of the African Easterly Waves and the associated mesoscale convective systems are substantially underestimated, especially over continental regions. Finally, the study investigates the skill of the models with respect to hydro-climatic indices related to the occurrence, intensity and frequency of precipitation events at the intraseasonal scale. Although most of these indices are generally better reproduced with RCMs than reanalysis products, this study indicates that RCMs still need to be improved (especially with respect to their subgrid-scale parameterization schemes) to be able to reproduce the intraseasonal variance spectrum adequately.

  5. The perfectionism model of binge eating: testing unique contributions, mediating mechanisms, and cross-cultural similarities using a daily diary methodology.

    PubMed

    Sherry, Simon B; Sabourin, Brigitte C; Hall, Peter A; Hewitt, Paul L; Flett, Gordon L; Gralnick, Tara M

    2014-12-01

    The perfectionism model of binge eating (PMOBE) is an integrative model explaining the link between perfectionism and binge eating. This model proposes socially prescribed perfectionism confers risk for binge eating by generating exposure to 4 putative binge triggers: interpersonal discrepancies, low interpersonal esteem, depressive affect, and dietary restraint. The present study addresses important gaps in knowledge by testing if these 4 binge triggers uniquely predict changes in binge eating on a daily basis and if daily variations in each binge trigger mediate the link between socially prescribed perfectionism and daily binge eating. Analyses also tested if proposed mediational models generalized across Asian and European Canadians. The PMOBE was tested in 566 undergraduate women using a 7-day daily diary methodology. Depressive affect predicted binge eating, whereas anxious affect did not. Each binge trigger uniquely contributed to binge eating on a daily basis. All binge triggers except for dietary restraint mediated the relationship between socially prescribed perfectionism and change in daily binge eating. Results suggested cross-cultural similarities, with the PMOBE applying to both Asian and European Canadian women. The present study advances understanding of the personality traits and the contextual conditions accompanying binge eating and provides an important step toward improving treatments for people suffering from eating binges and associated negative consequences. PMID:25243835

  6. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

    USGS Publications Warehouse

    Letcher, Benjamin; Hocking, Daniel; O'Neill, K.; Whiteley, Andrew R.; Nislow, Keith H.; O'Donnell, Matthew

    2016-01-01

    Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.63 °C · decade-1) and a widening of the synchronized period (29 d · decade-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

  7. Changes in sub-daily precipitation extremes in a global climate model with super-parameterization under CO2 warming

    NASA Astrophysics Data System (ADS)

    Khairoutdinov, Marat; Zhou, Xin

    2015-04-01

    Virtually all of the projections for future change of extreme precipitation statistics under CO2 warming have been made using global climate models (GCMs) in which clouds and, in particular, convective cloud systems are not explicitly resolved, but rather parameterized. In our study, a different kind of a GCM, a super-parameterized Community Atmosphere Model (SP-CAM), is employed. In SP-CAM, all the conventional cloud parameterizations are replaced with a small-domain cloud resolving model (CRM), called super-parameterization (SP). The SP is embedded in each grid column of the host GCM. The resolution of each embedded CRM is 4 km, which is generally sufficient to explicitly represent deep convection, which is mostly responsible for extreme precipitation events. In this study, we use the SP-CAM to contrast to the present and to conventional climate model, CAM, the sub-daily extreme precipitation statistics in response to the sea-surface temperatures (SSTs) and CO2 levels as projected for the end of 21st century in response to the IPCC AR5 RCP8.5 emission scenario. Different mechanisms for extreme precipitation changes are discussed.

  8. Testing the perfectionism model of binge eating in mother-daughter dyads: a mixed longitudinal and daily diary study.

    PubMed

    Mushquash, Aislin R; Sherry, Simon B

    2013-04-01

    The perfectionism model of binge eating is an integrative model explaining why perfectionism is tied to binge eating. This study extended and tested this emerging model by proposing daughters' socially prescribed perfectionism (i.e., perceiving one's mother is harshly demanding perfection of oneself) and mothers' psychological control (i.e., a negative parenting style involving control and demandingness) contribute indirectly to daughters' binge eating by generating situations or experiences that trigger binge eating. These binge triggers include discrepancies (i.e., viewing oneself as falling short of one's mother's expectations), depressive affect (i.e., feeling miserable and sad), and dietary restraint (i.e., behaviors aimed at reduced caloric intake). This model was tested in 218 mother-daughter dyads studied using a mixed longitudinal and daily diary design. Daughters were undergraduate students. Results largely supported hypotheses, with bootstrapped tests of mediation suggesting daughters' socially prescribed perfectionism and mothers' psychological control contribute to binge eating through binge triggers. For undergraduate women who believe their mothers rigidly require them to be perfect and whose mothers are demanding and controlling, binge eating may provide a means of coping with or escaping from an unhealthy, unsatisfying mother-daughter relationship. PMID:23557815

  9. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags.

    PubMed

    Letcher, Benjamin H; Hocking, Daniel J; O'Neil, Kyle; Whiteley, Andrew R; Nislow, Keith H; O'Donnell, Matthew J

    2016-01-01

    Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade(-1)) and a widening of the synchronized period (29 d decade(-1)). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. PMID:26966662

  10. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

    PubMed Central

    Hocking, Daniel J.; O’Neil, Kyle; Whiteley, Andrew R.; Nislow, Keith H.; O’Donnell, Matthew J.

    2016-01-01

    Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. PMID:26966662

  11. Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series

    NASA Astrophysics Data System (ADS)

    Amisigo, B. A.; van de Giesen, N. C.

    2005-09-01

    A spatio-temporal linear dynamic model has been developed for patching short gaps in daily river runoff series. The model was cast in a state-space form in which the state variable was estimated using the Kalman smoother (RTS smoother). The EM algorithm was used to concurrently estimate both parameter and missing runoff values. Application of the model to daily runoff series in the Volta Basin of West Africa showed that the model was capable of providing good estimates of missing runoff values at a gauging station from the remaining time series at the station and at spatially correlated stations in the same sub-basin.

  12. Validating the simulation of optical reflectance by a vertically resolved canopy biophysics model with MODIS daily observations

    NASA Astrophysics Data System (ADS)

    Drewry, D. T.; Duveiller, G.

    2012-12-01

    , hydrological and energetic (ie. biochemical, water stress, energy balance) states and fluxes and remotely sensed reflectances across the visible and near infrared bands observed by MODIS. Special care is made to ensure that the satellite observations match adequately, in both time and space, with the coupled model simulations. To do so, daily MODIS observations are used for which the exact time when they have been calculated is known by the satellite orbits. A previously-developed model of the spatial response of the sensor is also used to ensure that each footprint of every observation is within the target fields. This latter step is necessary for using daily MODIS data, as the observation footprint is often much larger than the 250 meter pixel it is encoded in, with further variation according to the orbit. This study focuses on the agro-ecosystem of the Central US, characterized by large-scale annual plantations of soybean and maize, for which MLCan has been rigorously validated.

  13. Sources of solar radiation estimates and their effect on daily potential evaporation for use in streamflow modeling

    NASA Astrophysics Data System (ADS)

    Lindsey, Scott D.; Farnsworth, Richard K.

    1997-12-01

    The accurate estimation of potential evaporation (PE), to in turn calculate evapotranspiration, is an important step in many hydrologic models. The National Weather Service (NWS) has used PE to obtain daily estimates of mean evapotranspiration in continuous rainfall-runoff models for river forecasting. The daily PE estimates are derived mainly from meteorological data gathered on a regular basis throughout the country. Solar radiation is one of the required input variables. Because of its widespread availability, sky cover is now used almost exclusively by NWS to estimate solar radiation. Over a period of time, a bias has developed between the long-term mean PE (computed using a combination of historical observed pan evaporation data and meteorological data) and PE estimated operationally using real-time meteorological data. This difference is a result of the use of sky cover based solar radiation estimates. These biased solar radiation estimates translate into long-term means of PE which are significantly lower than values using corresponding direct measurements of solar radiation or estimates of solar radiation using percent sunshine. A standard for PE has been established and verified to which long-term means can be compared. PE estimates derived from sky cover can be corrected to the standard using a ratio of long-term means. Many meteorological variables which have been measured or observed manually in the past are being converted to automatic observations. With the advent of automated sensors, which do not duplicate the manual sky cover observations, another source of solar radiation is necessary to model PE for use in river forecasting. Satellite estimates of solar radiation are compared with other means of measuring and estimating solar radiation. Available on a nationwide basis, satellite estimates produce values of solar radiation comparable to those obtained by direct measurement. Based on availability and accuracy, satellite estimates of solar radiation

  14. The Role of Competence and Value Beliefs in Students' Daily Emotional Experiences: A Multilevel Test of a Transactional Model

    ERIC Educational Resources Information Center

    Ahmed, Wondimu; Minnaert, Alexander; van der Werf, Greetje; Kuyper, Hans

    2010-01-01

    This study examined the mutual influence of relatively stable personal competence and value beliefs and lesson specific appraisals of competence and value on daily emotional experiences of students in the classroom context. Personal competence and value beliefs were measured by means of questionnaire whereas appraisals and daily emotions were…

  15. A Conceptual Model for Teaching the Relationship of Daily Life and Human Environmental Impact to Ecological Function

    ERIC Educational Resources Information Center

    Wyner, Yael

    2013-01-01

    In the general activity of daily life, it is easy to miss our dependency on the Earth's ecology. At the same time that people are living apparently separate from the environment, our impact on the Earth is increasing. This study seeks to understand how teachers can bridge this persistent disconnect of daily life from ecology and human impact.…

  16. Daily and compulsive internet use and well-being in adolescence: a diathesis-stress model based on big five personality traits.

    PubMed

    van der Aa, Niels; Overbeek, Geertjan; Engels, Rutger C M E; Scholte, Ron H J; Meerkerk, Gert-Jan; Van den Eijnden, Regina J J M

    2009-07-01

    This study examined the associations between adolescents' daily Internet use and low well-being (i.e., loneliness, low self-esteem, and depressive moods). We hypothesized that (a) linkages between high levels of daily Internet use and low well-being would be mediated by compulsive Internet use (CIU), and (b) that adolescents with low levels of agreeableness and emotional stability, and high levels of introversion would be more likely to develop CIU and lower well-being. Data were used from a sample of 7888 Dutch adolescents (11-21 years). Results from structural equation modeling analyses showed that daily Internet use was indirectly related to low well-being through CIU. In addition, daily Internet use was found to be more strongly related to CIU in introverted, low-agreeable, and emotionally less-stable adolescents. In turn, again, CIU was more strongly linked to loneliness in introverted, emotionally less-stable, and less agreeable adolescents. PMID:19636779

  17. From daily to sub-daily time steps - Creating a high temporal and spatial resolution climate reference data set for hydrological modeling and bias-correction of RCM data

    NASA Astrophysics Data System (ADS)

    Willkofer, Florian; Wood, Raul R.; Schmid, Josef; von Trentini, Fabian; Ludwig, Ralf

    2016-04-01

    The ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) focuses on the effects of climate change on hydro-meteorological extreme events and their implications for water management in Bavaria and Québec. It builds on the conjoint analysis of a large ensemble of the CRCM5, driven by 50 members of the CanESM2, and the latest information provided through the CORDEX-initiative, to better assess the influence of natural climate variability and climatic change on the dynamics of extreme events. A critical point in the entire project is the preparation of a meteorological reference dataset with the required temporal (1-6h) and spatial (500m) resolution to be able to better evaluate hydrological extreme events in mesoscale river basins. For Bavaria a first reference data set (daily, 1km) used for bias-correction of RCM data was created by combining raster based data (E-OBS [1], HYRAS [2], MARS [3]) and interpolated station data using the meteorological interpolation schemes of the hydrological model WaSiM [4]. Apart from the coarse temporal and spatial resolution, this mosaic of different data sources is considered rather inconsistent and hence, not applicable for modeling of hydrological extreme events. Thus, the objective is to create a dataset with hourly data of temperature, precipitation, radiation, relative humidity and wind speed, which is then used for bias-correction of the RCM data being used as driver for hydrological modeling in the river basins. Therefore, daily data is disaggregated to hourly time steps using the 'Method of fragments' approach [5], based on available training stations. The disaggregation chooses fragments of daily values from observed hourly datasets, based on similarities in magnitude and behavior of previous and subsequent events. The choice of a certain reference station (hourly data, provision of fragments) for disaggregating daily station data (application

  18. Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model

    NASA Astrophysics Data System (ADS)

    Kooperman, Gabriel J.; Pritchard, Michael S.; Burt, Melissa A.; Branson, Mark D.; Randall, David A.

    2016-03-01

    This study evaluates several important statistics of daily rainfall based on frequency and amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization—Super-Parameterized Community Atmosphere Model (SPCAM). Three superparameterized and conventional versions of CAM, coupled within the Community Earth System Model (CESM1 and CCSM4), are compared against two modern rainfall products (GPCP 1DD and TRMM 3B42) to discriminate robust effects of superparameterization that emerge across multiple versions. The geographic pattern of annual-mean rainfall is mostly insensitive to superparameterization, with only slight improvements in the double-ITCZ bias. However, unfolding intensity distributions reveal several improvements in the character of rainfall simulated by SPCAM. The rainfall rate that delivers the most accumulated rain (i.e., amount mode) is systematically too weak in all versions of CAM relative to TRMM 3B42 and does not improve with horizontal resolution. It is improved by superparameterization though, with higher modes in regions of tropical wave, Madden-Julian Oscillation, and monsoon activity. Superparameterization produces better representations of extreme rates compared to TRMM 3B42, without sensitivity to horizontal resolution seen in CAM. SPCAM produces more dry days over land and fewer over the ocean. Updates to CAM's low cloud parameterizations have narrowed the frequency peak of light rain, converging toward SPCAM. Poleward of 50°, where more rainfall is produced by resolved-scale processes in CAM, few differences discriminate the rainfall properties of the two models. These results are discussed in light of their implication for future rainfall changes in response to climate forcing.

  19. Daily data - obtain modeled daily data

    Atmospheric Science Data Center

    2016-02-19

    ... list Select one parameter of those just chosen from the "Plot one parameter" list. Select "Submit". The resulting page displays a plot of the selected parameter with an option to view the parameters chosen for ...

  20. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    NASA Technical Reports Server (NTRS)

    Bell, Jordan R.; Case, Jonathan L.; Molthan, Andrew L.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true

  1. Effect of lower-limb joint models on subject-specific musculoskeletal models and simulations of daily motor activities.

    PubMed

    Valente, Giordano; Pitto, Lorenzo; Stagni, Rita; Taddei, Fulvia

    2015-12-16

    Understanding the validity of using musculoskeletal models is critical, making important to assess how model parameters affect predictions. In particular, assumptions on joint models can affect predictions from simulations of movement, and the identification of image-based joints is unavoidably affected by uncertainty that can decrease the benefits of increasing model complexity. We evaluated the effect of different lower-limb joint models on muscle and joint contact forces during four motor tasks, and assessed the sensitivity to the uncertainties in the identification of anatomical four-bar-linkage joints. Three MRI-based musculoskeletal models having different knee and ankle joint models were created and used for the purpose. Model predictions were compared against a baseline model including simpler and widely-adopted joints. In addition, a probabilistic analysis was performed by perturbing four-bar-linkage joint parameters according to their uncertainty. The differences between models depended on the motor task analyzed, and there could be marked differences at peak loading (up to 2.40 BW at the knee and 1.54 BW at the ankle), although they were rather small over the motor task cycles (up to 0.59 BW at the knee and 0.31 BW at the ankle). The model including more degrees of freedom showed more discrepancies in predicted muscle activations compared to measured muscle activity. Further, including image-based four-bar-linkages was robust to simulate walking, chair rise and stair ascent, but not stair descent (peak standard deviation of 2.66 BW), suggesting that joint model complexity should be set according to the imaging dataset available and the intended application, performing sensitivity analyses. PMID:26506255

  2. Stochastic Downscaling of Daily Rainfall: Analysis of future hydroclimatic changes and their impact on the Pontinia plain using Nonhomogeneous Hidden Markov Model and Dynamic Hierarchical Bayesian Network Model.

    NASA Astrophysics Data System (ADS)

    Cioffi, Francesco; Devineni, Naresh; Monti, Alessandro; Lall, Upmanu

    2013-04-01

    The Nonhomogeneous Hidden Markov Model is an established technique that usually provides excellent results for the downscaling of multi-site precipitation. However, the selection of the number of states is subjective and results in a model that can be over parameterized and overfit leading to por performance in applications. A dynamic hierarchical Bayesian network model (DHBN) that is continuous and is not based on discretization into states is tested here and compared against NHMM for the downscaling of daily precipitation for Pontinia Plain. This región is a relevant example of coastal area particularly vulnerable to hydrological changes. The winter (October-March) wet season is considered. Weather states and atmospheric variables from CMIP5 GCM are used as exogenous predictors. The daily rainfall occurrence and amount at 32 stations over the region for the winters of 1916-2004 is used as the primary data. Rainfall variability is described in terms of occurrence of 'weather state' as classified by a Hidden Markov Model, and associated to variables representing the main characteristics of large scale atmospheric circulation as obtained by reanalysis data. A nonhomogeneous hidden Markov model (NHHM) and a DHBN model are used to make future projections of the downscaled precipitation as by using the GCM's simulations under different global warming scenarios.The spatial interaction between the sites is modeled through the underlying covariance function and the uncertainty in the model parameters is explicitly represented in their posterior distribution. Preliminary results show that the seasonal statistics are adequately captures for the 20th century runs. The structural differences between the two models are discussed.

  3. Parameterizations of daily temperature standard deviation for modeling ice sheet mass balances using a temperature-index method under paleoclimate conditions

    NASA Astrophysics Data System (ADS)

    Erokhina, Olga; Rogozhina, Irina

    2016-04-01

    A number of recent studies have suggested time-dependent parameterizations of daily temperature standard deviation for modelling surface mass balances of ice sheets and glaciers using a temperature-index method. These have been inferred from in-situ measurements and climate reanalysis data, which are only available on yearly to decadal time scales. To date, the existing literature has not explored their applicability to climate conditions that are different from those of today. This study presents an ensemble of simulations of the Greenland Ice Sheet's history since the Last Glacial Maximum to assess the performance of existing parameterizations of daily temperature standard deviation on millennial time scales. To limit the influence of the uncertainties arising from poorly constrained external and internal factors we adopt climate strategies of different complexities and a sensitivity analysis of ice sheet model parameters. Our study reveals that previously proposed parameterizations of daily temperature standard deviation have a limited performance during the deglaciation stage, failing to simulate the retreat of ice masses as suggested by geological reconstructions. In contrast multiple studies that use constant values of daily temperature standard deviation within the range of 4 to 5°C receive support from our analysis, implying that either the ice sheet model used is missing the fundamental physics necessary to capture complex processes associated with rapid deglaciation or the values of daily temperature standard deviation suggested by parameterizations based on present-day observations are too low to ensure the consistent Wisconsin-to-Holocene ice sheet retreat.

  4. A Hybrid Framework to Bias Correct and Empirically Downscale Daily Temperature and Precipitation from Regional Climate Models

    NASA Astrophysics Data System (ADS)

    Tan, P.; Abraham, Z.; Winkler, J. A.; Perdinan, P.; Zhong, S. S.; Liszewska, M.

    2013-12-01

    Bias correction and statistical downscaling are widely used approaches for postprocessing climate simulations generated by global and/or regional climate models. The skills of these approaches are typically assessed in terms of their ability to reproduce historical climate conditions as well as the plausibility and consistency of the derived statistical indicators needed by end users. Current bias correction and downscaling approaches often do not adequately satisfy the two criteria of accurate prediction and unbiased estimation. To overcome this limitation, a hybrid regression framework was developed to both minimize prediction errors and preserve the distributional characteristics of climate observations. Specifically, the framework couples the loss functions of standard (linear or nonlinear) regression methods with a regularization term that penalizes for discrepancies between the predicted and observed distributions. The proposed framework can also be extended to generate physically-consistent outputs across multiple response variables, and to incorporate both reanalysis-driven and GCM-driven RCM outputs into a unified learning framework. The effectiveness of the framework is demonstrated using daily temperature and precipitation simulations from the North American Regional Climate Change Program (NARCCAP) . The accuracy of the framework is comparable to standard regression methods, but, unlike the standard regression methods, the proposed framework is able to preserve many of the distribution properties of the response variables, akin to bias correction approaches such as quantile mapping and bivariate geometric quantile mapping.

  5. Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

    NASA Astrophysics Data System (ADS)

    Ly, S.; Charles, C.; Degré, A.

    2011-07-01

    Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms

  6. Development of total maximum daily loads for bacteria impaired watershed using the comprehensive hydrology and water quality simulation model.

    PubMed

    Kim, Sang M; Brannan, Kevin M; Zeckoski, Rebecca W; Benham, Brian L

    2014-01-01

    The objective of this study was to develop bacteria total maximum daily loads (TMDLs) for the Hardware River watershed in the Commonwealth of Virginia, USA. The TMDL program is an integrated watershed management approach required by the Clean Water Act. The TMDLs were developed to meet Virginia's water quality standard for bacteria at the time, which stated that the calendar-month geometric mean concentration of Escherichia coli should not exceed 126 cfu/100 mL, and that no single sample should exceed a concentration of 235 cfu/100 mL. The bacteria impairment TMDLs were developed using the Hydrological Simulation Program-FORTRAN (HSPF). The hydrology and water quality components of HSPF were calibrated and validated using data from the Hardware River watershed to ensure that the model adequately simulated runoff and bacteria concentrations. The calibrated and validated HSPF model was used to estimate the contributions from the various bacteria sources in the Hardware River watershed to the in-stream concentration. Bacteria loads were estimated through an extensive source characterization process. Simulation results for existing conditions indicated that the majority of the bacteria came from livestock and wildlife direct deposits and pervious lands. Different source reduction scenarios were evaluated to identify scenarios that meet both the geometric mean and single sample maximum E. coli criteria with zero violations. The resulting scenarios required extreme and impractical reductions from livestock and wildlife sources. Results from studies similar to this across Virginia partially contributed to a reconsideration of the standard's applicability to TMDL development. PMID:24798906

  7. Validation of modeled daily erythemal exposure along tropical and subtropical shipping routes by ship-based and satellite-based measurements

    NASA Astrophysics Data System (ADS)

    Feister, Uwe; Meyer, Gabriele; Laschewski, Gudrun; Boettcher, Christopher

    2015-05-01

    The Personal ERythemal EXposure (PEREX) model for seafarers working on decks of vessels has been developed to be used for retrospective estimates of personal occupational erythemal exposure in dependence of work profile, time period, and sea route. Extremely high UV index values up to 22 and daily erythemal exposure up to 89 standard erythemal dose have been derived from ship-based measurements in tropical oceans. Worldwide climatological maps of daily solar erythemal exposure derived from 10 year (2004-2013) hourly grid point radiative transfer model calculations for both cloudless sky and cloudy sky serve as the database of PEREX. The PEREX database is compared with ship-based measurements taken along four routes of merchant vessels, continuous UV radiation measurements taken on the research vessel Meteor on its mainly tropical and subtropical routes for 2 years, daily cloudless-sky erythemal exposure derived from 10 min LibRadtran radiative transfer model calculations, and 2 years of satellite-based erythemal exposure data of the Ozone Monitoring Instrument on the Aura satellite along the ship routes. Systematic differences between PEREX model data, ship-based data, and satellite-based daily erythemal exposure for all-sky conditions are only 1 to 3%, while short-term variations of cloudiness result in standard deviations of differences around 30%. Measured ratios between cloudless-sky erythemal radiation at vertical to horizontal incidence decrease with decreasing solar zenith angle, while clouds flatten their diurnal course.

  8. Toward the Development of an Inventory of Daily Widowed Life (IDWL): Guided by the Dual Process Model of Coping with Bereavement

    ERIC Educational Resources Information Center

    Caserta, Michael S.; Lund, Dale A.

    2007-01-01

    "The Dual Process Model of Coping with Bereavement" (M. Stroebe & H. Schut, 1999) suggests that the most effective adaptation involves oscillation between 2 coping processes: loss-orientation (LO) and restoration-orientation (RO). A 22-item Inventory of Daily Widowed Life (IDWL) was developed to measure these processes and the oscillation between…

  9. Estimation of Daily Reference Evapotranspiration using Temperature Based Models and Remotely Sensed Data over Indian River Basin

    NASA Astrophysics Data System (ADS)

    R, Shwetha H.; D, Nagesh Kumar

    2015-04-01

    Reference evapotranspiration (ETo) is the most significant component of the hydrological budget. Accurate quantification of ETo is vital for proper water management, efficient agricultural activities, irrigation planning and irrigation scheduling. FAO Penman Montieth (FAO-PM) is the widely accepted and used method for the ETo estimation under all climatic conditions, but needs numerous inputs which are difficult to acquire in developing countries. In such conditions, temperature based models such as Hargreaves-Samani (HS) equation and Penman Montieth temperature (PMT) can be used, where only maximum and minimum temperatures are required. Spatial interpolation of meteorological parameters to calculate spatial variation of ETo results in inaccurate estimations at lowly densed weather stations. Hence, there is a necessity of simple and easy method to estimate spatial distribution of ETo. In this regard, remotely sensed data provides viable alternative approach to obtain continuous spatio-temporal ETo. In this study, we used temperature based ETo models with remotely sensed LST data to estimate spatio-temporal variation of ETo. Day and night LST (MYD11A1) data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS sensor of Aqua satellite. Firstly, day and night land surface temperatures (LST) with HS and PMT methods were applied to estimate ETo. Secondly, maximum and minimum air temperatures were estimated from day and night LST respectively using simple linear regression and these air temperature data were used to estimate ETo. Estimated results were validated with the ETo calculated using meteorological data obtained from Automatic Weather Stations (AWS) by applying standard FAO-PM. The preliminary results revealed that, HS method with LST overestimated ETo in the study region. Statistical analysis showed PMT method with both LST and air temperatures performed better than the HS method. These two temperature based methods are often used for

  10. Model-Based Once-Daily Darunavir/Ritonavir Dosing Recommendations in Pediatric HIV-1-Infected Patients Aged ≥3 to <12 Years.

    PubMed

    Brochot, A; Kakuda, T N; Van De Casteele, T; Opsomer, M; Tomaka, F L; Vermeulen, A; Vis, P

    2015-07-01

    An existing population pharmacokinetic model of darunavir in adults was updated using pediatric data from two studies evaluating weight-based, once-daily dosing of darunavir/ritonavir (ARIEL, NCT00919854 and DIONE, NCT00915655). The model was then used to provide once-daily dosing recommendations for darunavir/ritonavir in pediatric patients aged ≥3 to <12 years. The final model comprised two compartments with first-order absorption and apparent clearance dependent on the concentration of α1-acid glycoprotein. The recommended darunavir/ritonavir once-daily dosing regimens in children aged ≥3 to <12 years are: 35/7 mg/kg from 10 to <15 kg, 600/100 mg from 15 to <30 kg, 675/100 mg from 30 to <40 kg, and 800/100 mg for ≥40 kg. These doses should result in exposures similar to the adult exposure after treatment with darunavir/ritonavir 800/100 mg once daily, while minimizing pill burden and allowing a switch from suspension to tablet(s) as early as possible. PMID:26312164

  11. Model-Based Once-Daily Darunavir/Ritonavir Dosing Recommendations in Pediatric HIV-1-Infected Patients Aged ≥3 to <12 Years

    PubMed Central

    Brochot, A; Kakuda, TN; Van De Casteele, T; Opsomer, M; Tomaka, FL; Vermeulen, A; Vis, P

    2015-01-01

    An existing population pharmacokinetic model of darunavir in adults was updated using pediatric data from two studies evaluating weight-based, once-daily dosing of darunavir/ritonavir (ARIEL, NCT00919854 and DIONE, NCT00915655). The model was then used to provide once-daily dosing recommendations for darunavir/ritonavir in pediatric patients aged ≥3 to <12 years. The final model comprised two compartments with first-order absorption and apparent clearance dependent on the concentration of α1-acid glycoprotein. The recommended darunavir/ritonavir once-daily dosing regimens in children aged ≥3 to <12 years are: 35/7 mg/kg from 10 to <15 kg, 600/100 mg from 15 to <30 kg, 675/100 mg from 30 to <40 kg, and 800/100 mg for ≥40 kg. These doses should result in exposures similar to the adult exposure after treatment with darunavir/ritonavir 800/100 mg once daily, while minimizing pill burden and allowing a switch from suspension to tablet(s) as early as possible. PMID:26312164

  12. Combining a two-sourcepatch model with satellite data to monitor daily evapotranspiration at a regional scale

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this work, we present a micro-meteorological approach for estimating surface energy fluxes that can be operationally used together with satellite images to monitor surface energy fluxes at a regional scale. In particular we will focus on the retrieval of daily evapotranspiration. The feasibility ...

  13. Calibration procedure for a daily flow model of small watersheds with snowmelt runoff in the Green River coal region of Colorado

    USGS Publications Warehouse

    Norris, J.M.; Parker, R.S.

    1985-01-01

    A calibration procedure was developed for the U.S. Geological Survey 's Precipitation-Runoff Modeling System for watersheds in which snowmelt is the major contributor to runoff. The model uses daily values of air temperature and precipitation as input and the output is mean daily discharge. The procedure appears sufficient to calibrate both streamflow volume and the timing of mean daily discharge if other model parameters are reasonably estimated. Model structure and sensitivity analysis suggest that one of the most important parameters is the available water-holding capacity of the soil (SMAX). Changing this parameter through a series of iterations, the calibration procedure minimizes the error between observed and predicted annual discharge. The calibration suggests that the single parameter SMAX may be sufficient for optimizing both the volumes and the timing of runoff, assuming other model parameters are adequately estimated. Additional optimization on parameters sensitive to timing does not appear to improve prediction. This indicates that these parameters were estimated accurately prior to calibration. Further investigation is needed on more watersheds to determine SMAX 's ability to calibrate volume and timing with a constant set of other model parameter values. (USGS)

  14. Modeling Integrated Cave Drip Recharge Data using DReAM (Daily Recharge Assessment Model) in a Dry Eastern Mediterranean Area, Sif Cave - Israel

    NASA Astrophysics Data System (ADS)

    Anker, Y.; Sheffer, N. A.; Scanlon, B. R.; Gimburg, A.; Morin, E.

    2010-12-01

    Understanding recharge mechanisms and controls in karst regions is extremely important for managing water resources because of the dynamic nature of the system. To better understand this mechanism, a cave in the recharge area of the karstic Western Mountain Aquifer (WMA) of Israel was equipped to measure precipitation infiltration (2006-2008) by collecting integrated water drips from three areas in the cave (14, 46, and 52 m2 areas). Barrels equipped with pressure transducers record drip rate and volume for each of the three areas and enable estimation of recharge. A water budget model - DReAM (Daily Recharge Assessment Model) was used to quantify and predict infiltration behavior at the cave. DReAM includes calculations of all water cycle components - precipitation, evapotranspiration, runoff and recharge. The model was calibrated and validated using two independent sets of values, providing good agreement between calculated and observed data. Modeling results agree with previous studies that show: 1) three distinct flow paths (slow, intermediate, and fast flows) of water infiltrating at the cave; 2) a threshold of ~100 mm rain at the beginning of the rainy season for infiltration to begin; and 3) a decrease in lag time between rain events and infiltration response throughout the rainy season. This modeling tool and analysis approach can translate precipitation to groundwater recharge which will be very important for projecting future water resources in response to climate variability.

  15. Exposure-response modeling of average daily pain score, and dizziness and somnolence, for mirogabalin (DS-5565) in patients with diabetic peripheral neuropathic pain.

    PubMed

    Hutmacher, Matthew M; Frame, Bill; Miller, Raymond; Truitt, Kenneth; Merante, Domenico

    2016-01-01

    Mirogabalin (DS-5565) is an α2δ-1 ligand being developed for pain associated with diabetic peripheral neuropathy, fibromyalgia, and postherpetic neuralgia. Nonlinear mixed-effects analyses were performed on average daily pain and on the incidence of the adverse events dizziness and somnolence. These models were used to predict the dose of mirogabalin equivalent to pregabalin and the probability of meaningful reduction in pain compared with placebo and pregabalin. In addition, regimen effects were evaluated for reductions of adverse events. Mirogabalin was estimated to be 17-fold more potent than pregabalin. The effectiveness of 150 mg pregabalin, dosed twice daily, attenuated by week 5. Therefore, the estimated mechanism-based equivalent dose (ED) of 17.7 mg mirogabalin was higher than that predicted to achieve comparable pain reduction. If attenuation of the pregabalin effect is real, mirogabalin doses lower than the ED could yield comparable pain reduction, albeit with less differentiation in pain from placebo. The incidence rate of dizziness and somnolence decreased over time. Twice-daily dosing of mirogabalin was predicted to yield a lower incidence rate of dizziness than once-daily dosing; thus, titration of dosages should reduce adverse event rates. These model results were used to influence phase 3 dosing selection. PMID:26073181

  16. Myocardial infarction, ST-elevation and non-ST-elevation myocardial infarction and modelled daily pollution concentrations: a case-crossover analysis of MINAP data

    PubMed Central

    Butland, Barbara K; Atkinson, Richard W; Milojevic, Ai; Heal, Mathew R; Doherty, Ruth M; Armstrong, Ben G; MacKenzie, Ian A; Vieno, Massimo; Lin, Chun; Wilkinson, Paul

    2016-01-01

    Objectives To investigate associations between daily concentrations of air pollution and myocardial infarction (MI), ST-elevation MI (STEMI) and non-ST-elevation MI (NSTEMI). Methods Modelled daily ground-level gaseous, total and speciated particulate pollutant concentrations and ground-level daily mean temperature, all at 5 km×5 km horizontal resolution, were linked to 202 550 STEMI and 322 198 NSTEMI events recorded on the England and Wales Myocardial Ischaemia National Audit Project (MINAP) database. The study period was 2003–2010. A case-crossover design was used, stratified by year, month and day of the week. Data were analysed using conditional logistic regression, with pollutants modelled as unconstrained distributed lags 0–2 days. Results are presented as percentage change in risk per 10 µg/m3 increase in the pollutant relevant metric, having adjusted for daily mean temperature, public holidays, weekly influenza consultation rates and a sine-cosine annual cycle. Results There was no evidence of an association between MI or STEMI and any of O3, NO2, PM2.5, PM10 or selected PM2.5 components (sulfate and elemental carbon). For NSTEMI, there was a positive association with daily maximum 1-hour NO2 (0.27% (95% CI 0.01% to 0.54%)), which persisted following adjustment for O3 and adjustment for PM2.5. The association appeared to be confined to the midland and southern regions of England and Wales. Conclusions The study found no evidence of an association between the modelled pollutants (including components) investigated and STEMI but did find some evidence of a positive association between NO2 and NSTEMI. Confirmation of this association in other studies is required. PMID:27621827

  17. Non-Gaussian Winter Daily Minimum and Maximum Temperatures in a Regional Climate Model: Downscaling of Reanalysis, Historical Simulations and Future Projections for the Southeast United States

    NASA Astrophysics Data System (ADS)

    Stefanova, L. B.; Sura, P.; Griffin, M.; Chan, S.; Misra, V.

    2011-12-01

    There is a marked interest in possible changes of the climate variability under future emission scenarios, and, in particular, in the potential for changes of the statistics of extreme weather. One statistical measure of extreme events is the non-Gaussianity of the variable under consideration. For the Southeast US, the non-Gaussianity of the local wintertime temperature distributions is of considerable interest to agriculture, energy, and ecosystem management. Therefore, our goal is to evaluate the expected changes of wintertime daily minimum and maximum temperature distributions in regional climate change projections in response to increased radiative forcing. First, we assess the ability of the regional model that we use - the National Centers for Environmental Prediction (NCEP)/Experimental Climate Prediction Center (ECPC) Regional Spectral Model (RSM) - to represent the observed distributions and their response to the ENSO phase. This analysis is based on the daily minimum and maximum temperatures from the COAPS Land-Atmosphere Regional Reanalysis for the Southeast at 10-km resolution (CLARReS10) obtained by dynamically downscaling the NCEP - Department of Energy (DOE) Reanalysis II (R2) and the European Centre for Medium-Range Weather Forecast (ECMWF) 40-year Reanalysis (ERA40) with the Regional Spectral Model (RSM) over the Southeast United States at a horizontal resolution of 10 km for the period 1979-2001. We demonstrate that with the near-perfect lateral boundary conditions provided by the R2 or ERA40, RSM produces daily min/max temperature distributions and distributions' sensitivity to ENSO in very good agreement with station observations. We then assess the winter daily min/max temperatures distribution generated by dynamically downscaling with RSM the historical (1970-2000) and projected (2040-2070) coupled ocean-atmosphere climate model simulations from select models from the Coupled Model Intercomparison Project Phase 3 (CMIP3).

  18. Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model.

    PubMed

    Yamazaki, T; Hagiya, K; Takeda, H; Osawa, T; Yamaguchi, S; Nagamine, Y

    2016-08-01

    Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open

  19. Comparison between two statistically based methods, and two physically based models developed to compute daily mean streamflow at ungaged locations in the Cedar River Basin, Iowa

    USGS Publications Warehouse

    Linhart, S. Mike; Nania, Jon F.; Christiansen, Daniel E.; Hutchinson, Kasey J.; Sanders, Curtis L., Jr.; Archfield, Stacey A.

    2013-01-01

    A variety of individuals from water resource managers to recreational users need streamflow information for planning and decisionmaking at locations where there are no streamgages. To address this problem, two statistically based methods, the Flow Duration Curve Transfer method and the Flow Anywhere method, were developed for statewide application and the two physically based models, the Precipitation Runoff Modeling-System and the Soil and Water Assessment Tool, were only developed for application for the Cedar River Basin. Observed and estimated streamflows for the two methods and models were compared for goodness of fit at 13 streamgages modeled in the Cedar River Basin by using the Nash-Sutcliffe and the percent-bias efficiency values. Based on median and mean Nash-Sutcliffe values for the 13 streamgages the Precipitation Runoff Modeling-System and Soil and Water Assessment Tool models appear to have performed similarly and better than Flow Duration Curve Transfer and Flow Anywhere methods. Based on median and mean percent bias values, the Soil and Water Assessment Tool model appears to have generally overestimated daily mean streamflows, whereas the Precipitation Runoff Modeling-System model and statistical methods appear to have underestimated daily mean streamflows. The Flow Duration Curve Transfer method produced the lowest median and mean percent bias values and appears to perform better than the other models.

  20. Proposed Hydrodynamic Model Increases the Ability of Land-Surface Models to Capture Intra-Daily Dynamics of Transpiration and Canopy Structure Effects

    NASA Astrophysics Data System (ADS)

    Matheny, A. M.; Bohrer, G.; Mirfenderesgi, G.; Schafer, K. V.; Ivanov, V. Y.

    2014-12-01

    Hydraulic limitations are known to control transpiration in forest ecosystems when the soil is drying or when the vapor pressure deficit between the air and stomata is very large, but they can also impact stomatal apertures under conditions of adequate soil moisture and lower evaporative demand. We use the NACP dataset of latent heat flux measurements and model observations for multiple sites and models to demonstrate models' difficulties in capturing intra-daily hysteresis. We hypothesize that this is a result of un-resolved afternoon stomata closure due to hydrodynamic stresses. The current formulations for stomatal conductance and the empirical coupling between stomatal conductance and soil moisture used by these models does not resolve the hydrodynamic process of water movement from the soil to the leaves. This approach does not take advantage of advances in our understanding of water flow and storage in the trees, or of tree and canopy structure. A more thorough representation of the tree-hydrodynamic processes could potentially remedy this significant source of model error. In a forest plot at the University of Michigan Biological Station, we use measurements of sap flux and leaf water potential to demonstrate that trees of similar type - late successional deciduous trees - have very different hydrodynamic strategies that lead to differences in their temporal patterns of stomatal conductance and thus hysteretic cycles of transpiration. These differences will lead to large differences in conductance and water use based on the species composition of the forest. We also demonstrate that the size and shape of the tree branching system leads to differences in extent of hydrodynamic stress, which may change the forest respiration patterns as the forest grows and ages. We propose a framework to resolve tree hydrodynamics in global and regional models based on the Finite-Elements Tree-Crown Hydrodynamics model (FETCH) -a hydrodynamic model that can resolve the fast

  1. Daily exercise routines

    NASA Technical Reports Server (NTRS)

    Anderson, Patrick L.; Amoroso, Michael T.

    1990-01-01

    Viewgraphs on daily exercise routines are presented. Topics covered include: daily exercise and periodic stress testings; exercise equipment; physiological monitors; exercise protocols; physiological levels; equipment control; control systems; and fuzzy logic control.

  2. A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

    NASA Technical Reports Server (NTRS)

    Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

    2014-01-01

    The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions

  3. Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951-2005 using a regional climate model

    NASA Astrophysics Data System (ADS)

    Halder, Subhadeep; Saha, Subodh K.; Dirmeyer, Paul A.; Chase, Thomas N.; Nath Goswami, Bhupendra

    2016-05-01

    Daily moderate rainfall events, which constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 through 2005. On the other hand, mean and extreme near-surface daily temperature during the monsoon season have increased by a maximum of 1-1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) and prescribed land cover of years 1950 and 2005, it is demonstrated that part of the changes in moderate rainfall events and temperature have been caused by land-use/land-cover change (LULCC), which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decrease in forest and increase in crop cover. Our results also show that LULCC alone causes warming in the extremes of daily mean and maximum temperatures by a maximum of 1-1.2 °C, which is comparable with the observed increasing trend in the extremes. Decrease in forest cover and simultaneous increase in crops not only reduces the evapotranspiration over land and large-scale convective instability, but also contributes toward decrease in moisture convergence through reduced surface roughness. These factors act together in reducing significantly the moderate rainfall events and the amount of rainfall in that category over central India. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures over the Indian Ocean. As a result, enhanced warming at the surface and greater decrease in moderate rainfall events over central India compared to the earlier set of simulations are noticed. Results from these additional experiments corroborate our initial findings and confirm the contribution of LULCC in the decrease in moderate rainfall events and increase in daily mean and extreme temperature over India. Therefore, this study demonstrates the important implications of LULCC over

  4. A model for developing job rotation schedules that eliminate sequential high workloads and minimize between-worker variability in cumulative daily workloads: Application to automotive assembly lines.

    PubMed

    Yoon, Sang-Young; Ko, Jeonghan; Jung, Myung-Chul

    2016-07-01

    The aim of study is to suggest a job rotation schedule by developing a mathematical model in order to reduce cumulative workload from the successive use of the same body region. Workload assessment using rapid entire body assessment (REBA) was performed for the model in three automotive assembly lines of chassis, trim, and finishing to identify which body part exposed to relatively high workloads at workstations. The workloads were incorporated to the model to develop a job rotation schedule. The proposed schedules prevent the exposure to high workloads successively on the same body region and minimized between-worker variance in cumulative daily workload. Whereas some of workers were successively assigned to high workload workstation under no job rotation and serial job rotation. This model would help to reduce the potential for work-related musculoskeletal disorders (WMSDs) without additional cost for engineering work, although it may need more computational time and relative complex job rotation sequences. PMID:26995031

  5. Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate

    NASA Astrophysics Data System (ADS)

    Bhattarai, Nishan; Shaw, Stephen B.; Quackenbush, Lindi J.; Im, Jungho; Niraula, Rewati

    2016-07-01

    In the last two decades, a number of single-source surface energy balance (SEB) models have been proposed for mapping evapotranspiration (ET); however, there is no clear guidance on which models are preferable under different conditions. In this paper, we tested five models-Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), Surface Energy Balance System (SEBS), and operational Simplified Surface Energy Balance (SSEBop)-to identify the single-source SEB models most appropriate for use in the humid southeastern United States. ET predictions from these models were compared with measured ET at four sites (marsh, grass, and citrus surfaces) for 149 cloud-free Landsat image acquisition days between 2000 and 2010. The overall model evaluation statistics showed that SEBS generally outperformed the other models in terms of estimating daily ET from different land covers (e.g., the root mean squared error (RMSE) was 0.74 mm day-1). SSEBop was consistently the worst performing model and overestimated ET at all sites (RMSE = 1.67 mm day-1), while the other models typically fell in between SSEBop and SEBS. However, for short grass conditions, SEBAL, METRIC, and S-SEBI appear to work much better than SEBS. Overall, our study suggests that SEBS may be the best SEB model in humid regions, although it may require modifications to work better over short vegetation.

  6. Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability.

    PubMed

    Han, Jing-Cheng; Huang, Guohe; Huang, Yuefei; Zhang, Hua; Li, Zhong; Chen, Qiuwen

    2015-08-15

    Lack of hydrologic process representation at the short time-scale would lead to inadequate simulations in distributed hydrological modeling. Especially for complex mountainous watersheds, surface runoff simulations are significantly affected by the overland flow generation, which is closely related to the rainfall characteristics at a sub-time step. In this paper, the sub-daily variability of rainfall intensity was considered using a probability distribution, and a chance-constrained overland flow modeling approach was proposed to capture the generation of overland flow within conceptual distributed hydrologic simulations. The integrated modeling procedures were further demonstrated through a watershed of China Three Gorges Reservoir area, leading to an improved SLURP-TGR hydrologic model based on SLURP. Combined with rainfall thresholds determined to distinguish various magnitudes of daily rainfall totals, three levels of significance were simultaneously employed to examine the hydrologic-response simulation. Results showed that SLURP-TGR could enhance the model performance, and the deviation of runoff simulations was effectively controlled. However, rainfall thresholds were so crucial for reflecting the scaling effect of rainfall intensity that optimal levels of significance and rainfall threshold were 0.05 and 10 mm, respectively. As for the Xiangxi River watershed, the main runoff contribution came from interflow of the fast store. Although slight differences of overland flow simulations between SLURP and SLURP-TGR were derived, SLURP-TGR was found to help improve the simulation of peak flows, and would improve the overall modeling efficiency through adjusting runoff component simulations. Consequently, the developed modeling approach favors efficient representation of hydrological processes and would be expected to have a potential for wide applications. PMID:25889540

  7. Selecting the optimal method to calculate daily global reference potential evaporation from CFSR reanalysis data for application in a hydrological model study

    NASA Astrophysics Data System (ADS)

    Sperna Weiland, F. C.; Tisseuil, C.; Dürr, H. H.; Vrac, M.; van Beek, L. P. H.

    2012-03-01

    Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET daily time series from Climate Forecast System Reanalysis (CFSR) data are compared: Penman-Monteith, Priestley-Taylor and original and re-calibrated versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. A major finding is that for part of the investigated basins the selection of a PET method may have only a minor influence on the resulting river flow. Within the hydrological model used in this study the bias related to the PET method tends to decrease while going from PET, AET and runoff to discharge calculations. However, the performance of individual PET methods appears to be spatially variable, which stresses the necessity to select the most accurate and spatially stable PET method. The lowest root mean squared differences and the least significant deviations (95% significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for a cell-specific re-calibrated Blaney-Criddle equation. However, results show that this re-calibrated form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation applied to the CFSR data did not outperform the other methods in a evaluation against PET derived with the Penman-Monteith equation from CRU data. In arid regions (e.g. Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g. Orange, Murray and

  8. Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951-2005 using a regional climate model

    NASA Astrophysics Data System (ADS)

    Halder, S.; Saha, S. K.; Dirmeyer, P. A.; Chase, T. N.; Goswami, B. N.

    2015-07-01

    Daily moderate rainfall events, that constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 till 2005. Mean and extreme near surface daily temperature during the monsoon season have also increased by a maximum of 1-1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) with prescribed vegetation cover of 1950 and 2005, it is demonstrated that part of the above observed changes in moderate rainfall events and temperature have been caused by land-use land-cover change (LULCC) which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decreased (increased) forest (crop) cover. The results also show that land-use land-cover alone causes warming in the extremes of daily mean and maximum temperatures by maximum of 1-1.2 °C, that is comparable with the observed increasing trend in the extremes. Decrease (increase) in forest (crop) cover reduces the evapotranspiration over land and large-scale convective instability, apart from decreasing the moisture convergence. These factors act together not only in reducing the moderate rainfall events over central India but also the amount of rainfall in that category, significantly. This is the most interesting result of this study. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures. As a result, there is enhanced warming at the surface and decrease in moderate rainfall events over central India. Results from the additional experiments corroborate our initial findings and confirm the contribution of land-use land-cover change on increase in daily mean and extreme temperature and decrease in moderate rainfall events. This study not only demonstrates the important implications of LULCC over India, but also shows the necessity for inclusion of projected anthropogenic

  9. Developing a model for understanding patient collection of observations of daily living: A qualitative meta-synthesis of the Project HealthDesign Program

    PubMed Central

    Cohen, Deborah J.; Keller, Sara R.; Hayes, Gillian R.; Dorr, David A.; Ash, Joan S.; Sittig, Dean F.

    2016-01-01

    We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting Observations of Daily Living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients’ collection of ODL data: usability, illness experience, relevance of ODLs, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health. PMID:26949381

  10. Teaching Daily Living Skills to Seven Individuals with Severe Intellectual Disabilities: A Comparison of Video Prompting to Video Modeling

    ERIC Educational Resources Information Center

    Cannella-Malone, Helen I.; Fleming, Courtney; Chung, Yi-Cheih; Wheeler, Geoffrey M.; Basbagill, Abby R.; Singh, Angella H.

    2011-01-01

    We conducted a systematic replication of Cannella-Malone et al. by comparing the effects of video prompting to video modeling for teaching seven students with severe disabilities to do laundry and wash dishes. The video prompting and video modeling procedures were counterbalanced across tasks and participants and compared in an alternating…

  11. Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition.

    PubMed

    Villar, José R; Vergara, Paula; Menéndez, Manuel; de la Cal, Enrique; González, Víctor M; Sedano, Javier

    2016-09-01

    The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems. PMID:27354194

  12. Daily modelling of total electron content with ionosonde observation to the benefit of the open source users

    NASA Astrophysics Data System (ADS)

    Tamara, Gulyaeva; Poustovalova, Ljubov; Stanislawska, Iwona

    It is clearly beneficial if the open source databases and models are available free for the users. However a newcomer needs a guidance concerning reliability/consistency of a suitable model and database. Such guidance is provided by the International Standardization Organization which has published Technical Specification, ISO TS16457 on "The Earth's ionosphere model: international reference ionosphere (IRI) model and extensions to the plasmasphere", 2009. One of the ISO TS16457 options is IRI-IZMIRAN ionosphere-plasmasphere model. This model is applied currently to generate vertical total electron content, TEC, at the altitude range [80:20,000] km at (http://www.izmiran.ru/services/iweather/). This approach uses solar activ-ity and geomagnetic activity driving the ionosphere-plasmasphere model, and the integration of electron density profile fitted to ionosonde observation of the F2 layer peak density and height. The paper provides validation of proposed technique and comparisons with GPS-TEC observations and topside ionospheric electron density extrapolation technique providing the ionospheric TEC.

  13. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff

    NASA Astrophysics Data System (ADS)

    Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

    2007-04-01

    SummaryModeling of rainfall-runoff dynamics is one of the most studied topics in hydrology due to its essential application to water resources management. Recently, artificial intelligence has gained much popularity for calibrating the nonlinear relationships inherent in the rainfall-runoff process. In this study, the advantages of artificial neural networks and neuro-fuzzy system in continuous modeling of the daily and hourly behaviour of runoff were examined. Three different adaptive techniques were constructed and examined namely, Levenberg-Marquardt feed forward neural network, Bayesian regularization feed forward neural network, and neuro-fuzzy. In addition, the effects of data transformation on model performance were also investigated. This was done by examining the performance of the three network architectures and training algorithms using both raw and transformed data. Through inspection of the results it was found that although the model built on transformed data outperforms the model built on raw data, no significant differences were found between the forecast accuracies of the three examined models. A detailed comparison of the overall performance indicated that the neuro-fuzzy model performed better than both the Levenberg-Marquardt-FFNN and the Bayesian regularization-FFNN. In order to enable users to process the data easily, a graphic user interface (GUI) was developed. This program allows users to process the rainfall-runoff data, to train/test the model using various input options and to visualize results.

  14. A simple daily soil water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas

    NASA Astrophysics Data System (ADS)

    Dripps, W. R.; Bradbury, K. R.

    2007-05-01

    Quantifying the spatial and temporal distribution of natural groundwater recharge is usually a prerequisite for effective groundwater modeling and management. As flow models become increasingly utilized for management decisions, there is an increased need for simple, practical methods to delineate recharge zones and quantify recharge rates. Existing models for estimating recharge distributions are data intensive, require extensive parameterization, and take a significant investment of time in order to establish. The Wisconsin Geological and Natural History Survey (WGNHS) has developed a simple daily soil water balance (SWB) model that uses readily available soil, land cover, topographic, and climatic data in conjunction with a geographic information system (GIS) to estimate the temporal and spatial distribution of groundwater recharge at the watershed scale for temperate humid areas. To demonstrate the methodology and the applicability and performance of the model, two case studies are presented: one for the forested Trout Lake watershed of north central Wisconsin, USA and the other for the urban-agricultural Pheasant Branch Creek watershed of south central Wisconsin, USA. Overall, the SWB model performs well and presents modelers and planners with a practical tool for providing recharge estimates for modeling and water resource planning purposes in humid areas.

  15. A simple daily soil-water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas

    USGS Publications Warehouse

    Dripps, W.R.; Bradbury, K.R.

    2007-01-01

    Quantifying the spatial and temporal distribution of natural groundwater recharge is usually a prerequisite for effective groundwater modeling and management. As flow models become increasingly utilized for management decisions, there is an increased need for simple, practical methods to delineate recharge zones and quantify recharge rates. Existing models for estimating recharge distributions are data intensive, require extensive parameterization, and take a significant investment of time in order to establish. The Wisconsin Geological and Natural History Survey (WGNHS) has developed a simple daily soil-water balance (SWB) model that uses readily available soil, land cover, topographic, and climatic data in conjunction with a geographic information system (GIS) to estimate the temporal and spatial distribution of groundwater recharge at the watershed scale for temperate humid areas. To demonstrate the methodology and the applicability and performance of the model, two case studies are presented: one for the forested Trout Lake watershed of north central Wisconsin, USA and the other for the urban-agricultural Pheasant Branch Creek watershed of south central Wisconsin, USA. Overall, the SWB model performs well and presents modelers and planners with a practical tool for providing recharge estimates for modeling and water resource planning purposes in humid areas. ?? Springer-Verlag 2007.

  16. Utility of NASA's daily solar and meteorological data for regional level modeling of wheat phenology and yield potential

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Data products from the NASA Science Mission Directorate's Applied Science Energy Managed Program provide estimates of long-term meteorological conditions from assimilation models and surface solar energy fluxes derived from satellite observations. NASA's Prediction Of Worldwide Energy Resource (POWE...

  17. Managing Daily Life

    MedlinePlus

    ... To Cure MD PPMD Merchandise Host an Event Create a Personal Page My Donor Portfolio™ Sponsor Programs Other Ways to Help About Us Mission Financials History Staff & Board Media Awards Partners Contact Us Home / Care for Duchenne / Managing Daily Life Print Email Managing Daily Life Environmental ...

  18. The acute effects of daily nicotine intake on heart rate--a toxicokinetic and toxicodynamic modelling study.

    PubMed

    Gajewska, M; Worth, A; Urani, C; Briesen, H; Schramm, K-W

    2014-10-01

    Joint physiologically-based toxicokinetic and toxicodynamic (PBTK/TD) modelling was applied to simulate concentration-time profiles of nicotine, a well-known stimulant, in the human body following single and repeated dosing. Both kinetic and dynamic models were first calibrated by using in vivo literature data for the Caucasian population. The models were then used to estimate the blood and liver concentrations of nicotine in terms of the Area Under Curve (AUC) and the peak concentration (Cmax) for selected exposure scenarios based on inhalation (cigarette smoking), oral intake (nicotine lozenges) and dermal absorption (nicotine patches). The model simulations indicated that whereas frequent cigarette smoking gives rise to high AUC and Cmax in blood, the use of nicotine-rich dermal patches leads to high AUC and Cmax in the liver. Venous blood concentrations were used to estimate one of the most common acute effects, mean heart rate, both at rest and during exercise. These estimations showed that cigarette smoking causes a high peak heart rate, whereas dermal absorption causes a high mean heart rate over 48h. This study illustrates the potential of using PBTK/TD modelling in the safety assessment of nicotine-containing products. PMID:25066669

  19. Chronic daily headaches

    PubMed Central

    Ahmed, Fayyaz; Parthasarathy, Rajsrinivas; Khalil, Modar

    2012-01-01

    Chronic Daily Headache is a descriptive term that includes disorders with headaches on more days than not and affects 4% of the general population. The condition has a debilitating effect on individuals and society through direct cost to healthcare and indirectly to the economy in general. To successfully manage chronic daily headache syndromes it is important to exclude secondary causes with comprehensive history and relevant investigations; identify risk factors that predict its development and recognise its sub-types to appropriately manage the condition. Chronic migraine, chronic tension-type headache, new daily persistent headache and medication overuse headache accounts for the vast majority of chronic daily headaches. The scope of this article is to review the primary headache disorders. Secondary headaches are not discussed except medication overuse headache that often accompanies primary headache disorders. The article critically reviews the literature on the current understanding of daily headache disorders focusing in particular on recent developments in the treatment of frequent headaches. PMID:23024563

  20. Assessment and ground-based correction of the Level-3 MODIS daily aerosol optical depth: Implications in the context of surface solar radiation prediction and numerical weather modeling

    NASA Astrophysics Data System (ADS)

    Ruiz-Arias, J. A.; Dudhia, J.; Pozo-Vazquez, D.

    2012-12-01

    The Level-3 MODIS (L3M) aerosol optical depth (AOD) product offers interesting features for surface solar radiation and numerical weather modeling applications. However, most of the validation efforts so far have been focused on Level-2 (L2M) products and only rarely on L3M. We compare the Collection 5.1 L3M AOD (Terra dataset) available since 2000 against observed daily AOD values at 550 nm from more than 500 AERONET ground stations. The aim is to check the advisability of this dataset for surface solar radiation calculations using numerical weather models. Overall, the mean error (ME) is 0.03 (17%, relative to the mean observed AOD), with a root mean square error (RMSE) of 0.14 (73%), albeit these values are found highly dependent on geographical region. For AOD values above about 0.3 the expected error (EE) is found higher than that of the L2M product. We propose specific parameterizations for the EE of the L3M AOD, as well as for both its ME and its standard deviation. We also found that, roughly, half of the uncertainty of the L3M AOD dataset might be attributable to its sub-pixel variability. Finally, we used a radiative transfer model to investigate how the L3M AOD uncertainty propagates into the direct normal (DNI) and global horizontal (GHI) irradiances evaluation. Overall, for AODs smaller than 0.5, the induced uncertainty in DNI due to AOD alone is below 15% on average, and below 5% for GHI (for a solar zenith angle of 30 degrees). But the uncertainty in AOD is highly spatially variable, so is that in irradiance. These results suggest the necessity of a correction method to reduce the bias of the L3M AOD. Ground-based AOD measurements can be also used in a data fusion procedure. We present the results of a preliminary study using optimal interpolation of L3M daily AOD data based on daily AERONET AOD measurements in the US in the period since June to August 2009. The method removes the data gaps in the original dataset, assesses the spatial distribution

  1. Long-term prediction of solar and geomagnetic activity daily time series using singular spectrum analysis and fuzzy descriptor models

    NASA Astrophysics Data System (ADS)

    Mirmomeni, M.; Kamaliha, E.; Shafiee, M.; Lucas, C.

    2009-09-01

    Of the various conditions that affect space weather, Sun-driven phenomena are the most dominant. Cyclic solar activity has a significant effect on the Earth, its climate, satellites, and space missions. In recent years, space weather hazards have become a major area of investigation, especially due to the advent of satellite technology. As such, the design of reliable alerting and warning systems is of utmost importance, and international collaboration is needed to develop accurate short-term and long-term prediction methodologies. Several methods have been proposed and implemented for the prediction of solar and geomagnetic activity indices, but problems in predicting the exact time and magnitude of such catastrophic events still remain. There are, however, descriptor systems that describe a wider class of systems, including physical models and non-dynamic constraints. It is well known that the descriptor system is much tighter than the state-space expression for representing real independent parametric perturbations. In addition, the fuzzy descriptor models as a generalization of the locally linear neurofuzzy models are general forms that can be trained by constructive intuitive learning algorithms. Here, we propose a combined model based on fuzzy descriptor models and singular spectrum analysis (SSA) (FD/SSA) to forecast a number of geomagnetic activity indices in a manner that optimizes a fuzzy descriptor model for each of the principal components obtained from singular spectrum analysis and recombines the predicted values so as to transform the geomagnetic activity time series into natural chaotic phenomena. The method has been applied to predict two solar and geomagnetic activity indices: geomagnetic aa and solar wind speed (SWS) of the solar wind index. The results demonstrate the higher power of the proposed method-- compared to other methods -- for predicting solar activity.

  2. A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

  3. Daily temperature changes and variability in ENSEMBLES regional models predictions: Evaluation and intercomparison for the Ebro Valley (NE Iberia)

    NASA Astrophysics Data System (ADS)

    El Kenawy, A.; López-Moreno, J. I.; McCabe, M. F.; Brunsell, N. A.; Vicente-Serrano, S. M.

    2015-03-01

    We employ a suite of regional climate models (RCMs) to assess future changes in summer (JJA) maximum temperature (Tmax) over the Ebro basin, the largest hydrological division in the Iberian Peninsula. Under the A1B emission scenario, future changes in both mean values and their corresponding time varying percentiles were examined by comparing the control period (1971-2000) with two future time slices: 2021-2050 and 2071-2100. Here, the rationale is to assess how lower/upper tails of temperature distributions will change in the future and whether these changes will be consistent with those of the mean. The model validation results demonstrate significant differences among the models in terms of their capability to representing the statistical characteristics (e.g., mean, skewness and asymmetry) of the observed climate. The results also indicate that the current substantial warming observed in the Ebro basin is expected to continue during the 21st century, with more intense warming occurring at higher altitudes and in areas with greater distance from coastlines. All models suggest that the region will experience significant positive changes in both the cold and warm tails of temperature distributions. However, the results emphasize that future changes in the lower and upper tails of the summer Tmax distribution may not follow the same warming rate as the mean condition. In particular, the projected changes in the warm tail of the summer Tmax are shown to be significantly larger than changes in both mean values and the cold tail, especially at the end of the 21st century. The finding suggests that much of the changes in the summer Tmax percentiles will be driven by a shift in the entire distribution of temperature rather than only changes in the central tendency. Better understanding of the possible implications of future climate systems provides information useful for vulnerability assessments and the development of local adaptation strategies for multi

  4. Subseasonal to multidecadal variability of northeast monsoon daily rainfall over Peninsular Malaysia using a hidden Markov model

    NASA Astrophysics Data System (ADS)

    Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli

    2016-04-01

    This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.

  5. Impact of stratospheric volcanic aerosols on the daily temperature range (DTR) in Europe over the past 200 years: observations vs. model simulations

    NASA Astrophysics Data System (ADS)

    Auchmann, Renate; Wegmann, Martin; Arfeuille, Florian; Franke, Jörg; Barriendos, Mariano; Prohom, Marc; Sanchez-Lorenzo, Arturo; Bhend, Jonas; Wild, Martin; Folini, Doris; Stepanek, Petr; van der Schrier, Gerard; Brönnimann, Stefan

    2013-04-01

    Explosive tropical volcanic eruptions can affect climate and weather on many timescales and over large areas and are one of the major causes of natural climate variability. The dominant and best understood mechanism through which volcanic eruptions influence climate is the direct radiative perturbation through secondary sulfate aerosols in the stratosphere, enhancing the reflectance of solar radiation and as a consequence leading to a loss of energy at the Earth's surface. The decrease of shortwave radiation on the ground affects the energy balance during daytime only. During the night (and also during the day), even a slight increase in surface net radiation is expected due to the increase in downwelling longwave radiation. Overall, these changes in the energy balance may lead to an overall reduction of the daily temperature range (DTR). Hence, the DTR can be utilized as a quantitative measure of the radiative forcing impact through stratospheric volcanic aerosols. We analyze this impact over Europe using long-term daily and sub-daily station records. Eight stratospheric volcanic eruptions from the instrumental period (ca. 200 years) are investigated. Seasonal all-sky DTR anomalies after volcanic eruptions are compared to contemporary (ca. 20 year) reference periods. We further use clear-sky DTR anomalies to eliminate cloud effects and better estimate the signal from the direct radiative forcing of the volcanic aerosols. We find a stronger negative signal in the clear-sky DTR anomalies compared to the all-sky case. Although the all-sky and clear-sky anomalies for different stations, volcanic eruptions, and seasons show heterogenic signals in terms of magnitude and sign, the significantly negative DTR anomalies (e.g., for Tambora) are qualitatively consistent with other studies. We quantify the impact on clear-sky DTR through stratospheric volcanic forcing, by applying a weighted linear regression model to clear-sky DTR anomalies and radiative forcing. Our estimate

  6. Repeat topography surveys of geomorphic changes using digital surface models deriving from Formosat-2 daily revisit stereo pair with very narrow baseline

    NASA Astrophysics Data System (ADS)

    Liu, C.; Wen, H.; Liu, J.; Ko, M.; Yan, H.; Chang, L.

    2012-12-01

    translative offset between two similar images to be rapidly estimated. To meet the requirements in remote sensing and biomedical imaging, the technology of phase correlation has been extended to the sub-pixel level. Liu and Yan (2008) developed a robust phase correlation model using the based feature matching for image co-registration and DEM generation. Considering the fact that the Formosat-2 consecutive images are intrinsically stereo pairs with very narrow baselines, this innovative stereo-matching algorithm based on SPPC technique is employed to process Formosat-2 daily revisit stereo pairs with very narrow baselines. The detailed accuracy and efficiency analysis is investigated for the study area, Namasha, Kaohsiung, using the 50cm resolution aerial photo and the 2m resolution DEM derived from airborne LiDAR data. The archive of Formosat-2 images in Taiwan area collected from 2005 to 2012 was screened out, with the intention to select the consecutive pairs of those areas where major slope disasters occurred in the past eight years. This research encourages the repeated topography surveys of geomorphic changes using digital surface models deriving from Formosat-2 daily revisit stereo pair with very narrow baseline.

  7. Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality

    PubMed Central

    2012-01-01

    Background Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Methods Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton’s method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. Results In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. Conclusions GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies. PMID:23110601

  8. Two-stage model for time-varying effects of discrete longitudinal covariates with applications in analysis of daily process data.

    PubMed

    Yang, Hanyu; Cranford, James A; Li, Runze; Buu, Anne

    2015-02-20

    This study proposes a generalized time-varying effect model that can be used to characterize a discrete longitudinal covariate process and its time-varying effect on a later outcome that may be discrete. The proposed method can be applied to examine two important research questions for daily process data: measurement reactivity and predictive validity. We demonstrate these applications using health risk behavior data collected from alcoholic couples through an interactive voice response system. The statistical analysis results show that the effect of measurement reactivity may only be evident in the first week of interactive voice response assessment. Moreover, the level of urge to drink before measurement reactivity takes effect may be more predictive of a later depression outcome. Our simulation study shows that the performance of the proposed method improves with larger sample sizes, more time points, and smaller proportions of zeros in the binary longitudinal covariate. PMID:25395042

  9. Why Are Children Different in Their Daily Sedentariness? An Approach Based on the Mixed-Effects Location Scale Model.

    PubMed

    Gomes, Thayse Natacha; Hedeker, Donald; dos Santos, Fernanda Karina; Pereira, Sara; Katzmarzyk, Peter T; Maia, José A R

    2015-01-01

    This study aimed to investigate the between- and within-individual variability in sedentary time over seven days, using a mixed-effects location scale model. The sample comprised 686 Portuguese children (381 girls) aged 9-11 years, from 23 schools. Sedentary time was estimated by the Actigraph GT3X+ accelerometer, which was used 24 hours/day for 7 consecutive days; height, sitting height, and weight were measured, BMI was computed (WHO cut-points were used to classify subjects as normal weight or overweight/obese), and maturity offset was estimated. Information regarding the home environment was obtained by questionnaire. Results revealed that (i) children were more sedentary on Friday, but less so on Saturday and Sunday (compared to Monday), with significant variation between- and within-subjects (between-subject variance=0.800, within-subject variance=1.793, intra-subject correlation=0.308); (ii) there is a sex effect on sedentariness, with boys being less sedentary than girls (p<0.001), and the between-subject variance was 1.48 times larger for boys than girls; (iii) in terms of the within-subject variance, or erraticism, Tuesday, Wednesday and Friday have similar erraticism levels as Monday (Thursday has less, while Saturday and Sunday have more); in addition, girls (variance ratio=0.632, p<0.001), overweight/obese children (variance ratio=0.861, p=0.019), and those later mature (variance ratio=0.849, p=0.013) have less erraticism than their counterparts; (iv) the within-subject variance varied significantly across subjects (scale std dev=0.342±0.037, p<0.001); and (v) in the fixed part of the model, only biological maturation was positively related to sedentariness. This study demonstrated that there is significant between- and within-subject variability in sedentariness across a whole week. This implies that a focus on intra-individual variability, instead of only on mean values, would provide relevant information towards a more complete map of children

  10. Why Are Children Different in Their Daily Sedentariness? An Approach Based on the Mixed-Effects Location Scale Model

    PubMed Central

    Gomes, Thayse Natacha; Hedeker, Donald; dos Santos, Fernanda Karina; Pereira, Sara; Katzmarzyk, Peter T.; Maia, José A. R.

    2015-01-01

    This study aimed to investigate the between- and within-individual variability in sedentary time over seven days, using a mixed-effects location scale model. The sample comprised 686 Portuguese children (381 girls) aged 9–11 years, from 23 schools. Sedentary time was estimated by the Actigraph GT3X+ accelerometer, which was used 24 hours/day for 7 consecutive days; height, sitting height, and weight were measured, BMI was computed (WHO cut-points were used to classify subjects as normal weight or overweight/obese), and maturity offset was estimated. Information regarding the home environment was obtained by questionnaire. Results revealed that (i) children were more sedentary on Friday, but less so on Saturday and Sunday (compared to Monday), with significant variation between- and within-subjects (between-subject variance=0.800, within-subject variance=1.793, intra-subject correlation=0.308); (ii) there is a sex effect on sedentariness, with boys being less sedentary than girls (p<0.001), and the between-subject variance was 1.48 times larger for boys than girls; (iii) in terms of the within-subject variance, or erraticism, Tuesday, Wednesday and Friday have similar erraticism levels as Monday (Thursday has less, while Saturday and Sunday have more); in addition, girls (variance ratio=0.632, p<0.001), overweight/obese children (variance ratio=0.861, p=0.019), and those later mature (variance ratio=0.849, p=0.013) have less erraticism than their counterparts; (iv) the within-subject variance varied significantly across subjects (scale std dev=0.342±0.037, p<0.001); and (v) in the fixed part of the model, only biological maturation was positively related to sedentariness. This study demonstrated that there is significant between- and within-subject variability in sedentariness across a whole week. This implies that a focus on intra-individual variability, instead of only on mean values, would provide relevant information towards a more complete map of children