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This book is a collection of 12 papers describing the role of hydrology in water resources management. The papers can be divided s according to their area of focus as 1) modeling of hydrological processes, 2) use of modern techniques in hydrological analysis, 3) impact of human pressure and climate change on water resources, and 4) hydrometeorological extremes. Belonging to the first area is the presentation of a new Muskingum flood routing model, a new tool to perform frequency analysis of maximum precipitation of a specified duration via the so-named PMAXΤP model (Precipitation MAXimum Time (duration) Probability), modeling of interception processes, and using a rainfall-runoff GR2M model to calculate monthly runoff. For the second area, the groundwater potential was evaluated using a model of multi-influencing factors in which the parameters were optimized by using geoprocessing tools in geographical information system (GIS) in combination with satellite altimeter data and the reanalysis of hydrological data to simulate overflow transport using the Nordic Sea as an example. Presented for the third area are a water balance model for the comparison of water resources with the needs of water users, the idea of adaptive water management, impacts of climate change, and anthropogenic activities on the runoff in catchment located in the western Himalayas of Pakistan. The last area includes spatiotemporal analysis of rainfall variability with regard to drought hazard and use of the copula function to meteorologically analyze drought.

Research & information: general --- GR2M --- inverse distance weighting --- rainfall-runoff model --- sensitivity analysis --- multi-influencing factors (MIF) --- vertical electrical sounding (VES) --- electrical resistivity tomography (ERT) --- groundwater resource management (GRM) --- hydro-stratigraphy --- well logs --- precipitation --- climate change --- Sen’s estimator --- Mann-Kendall --- Wadi Cheliff basin --- upper Minjiang River --- marginal distribution --- copula --- bivariate joint distribution --- return period --- rainfall partitioning --- dry tropical forest --- gash model --- interception modelling --- Nordic Sea --- overflow flux --- barotropic pressure --- baroclinic pressure --- annual maximum precipitation --- peaks-over-threshold methods --- statistical analysis --- maximum precipitation frequency analysis --- gamma --- Weibull --- log-gamma --- log-normal --- Gumbel distributions --- nonparametric tests --- drought --- trends --- SPI --- mina basin --- Algeria --- Kunhar River Basin --- streamflow --- trend analysis --- Soil and Water Assessment Tool (SWAT) --- anthropogenic impacts --- hydrologic flood routing --- Muskingum flood routing model --- meta-heuristic optimization --- self-adaptive vision correction algorithm --- Adaptive Water Management --- stakeholder engagement --- legislation --- survey --- uncertainty in water management --- water requirements of aquatic and water dependent ecosystems --- water resources allocation --- water balance model

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This book is a collection of 12 papers describing the role of hydrology in water resources management. The papers can be divided s according to their area of focus as 1) modeling of hydrological processes, 2) use of modern techniques in hydrological analysis, 3) impact of human pressure and climate change on water resources, and 4) hydrometeorological extremes. Belonging to the first area is the presentation of a new Muskingum flood routing model, a new tool to perform frequency analysis of maximum precipitation of a specified duration via the so-named PMAXΤP model (Precipitation MAXimum Time (duration) Probability), modeling of interception processes, and using a rainfall-runoff GR2M model to calculate monthly runoff. For the second area, the groundwater potential was evaluated using a model of multi-influencing factors in which the parameters were optimized by using geoprocessing tools in geographical information system (GIS) in combination with satellite altimeter data and the reanalysis of hydrological data to simulate overflow transport using the Nordic Sea as an example. Presented for the third area are a water balance model for the comparison of water resources with the needs of water users, the idea of adaptive water management, impacts of climate change, and anthropogenic activities on the runoff in catchment located in the western Himalayas of Pakistan. The last area includes spatiotemporal analysis of rainfall variability with regard to drought hazard and use of the copula function to meteorologically analyze drought.

Research & information: general --- GR2M --- inverse distance weighting --- rainfall-runoff model --- sensitivity analysis --- multi-influencing factors (MIF) --- vertical electrical sounding (VES) --- electrical resistivity tomography (ERT) --- groundwater resource management (GRM) --- hydro-stratigraphy --- well logs --- precipitation --- climate change --- Sen’s estimator --- Mann-Kendall --- Wadi Cheliff basin --- upper Minjiang River --- marginal distribution --- copula --- bivariate joint distribution --- return period --- rainfall partitioning --- dry tropical forest --- gash model --- interception modelling --- Nordic Sea --- overflow flux --- barotropic pressure --- baroclinic pressure --- annual maximum precipitation --- peaks-over-threshold methods --- statistical analysis --- maximum precipitation frequency analysis --- gamma --- Weibull --- log-gamma --- log-normal --- Gumbel distributions --- nonparametric tests --- drought --- trends --- SPI --- mina basin --- Algeria --- Kunhar River Basin --- streamflow --- trend analysis --- Soil and Water Assessment Tool (SWAT) --- anthropogenic impacts --- hydrologic flood routing --- Muskingum flood routing model --- meta-heuristic optimization --- self-adaptive vision correction algorithm --- Adaptive Water Management --- stakeholder engagement --- legislation --- survey --- uncertainty in water management --- water requirements of aquatic and water dependent ecosystems --- water resources allocation --- water balance model --- GR2M --- inverse distance weighting --- rainfall-runoff model --- sensitivity analysis --- multi-influencing factors (MIF) --- vertical electrical sounding (VES) --- electrical resistivity tomography (ERT) --- groundwater resource management (GRM) --- hydro-stratigraphy --- well logs --- precipitation --- climate change --- Sen’s estimator --- Mann-Kendall --- Wadi Cheliff basin --- upper Minjiang River --- marginal distribution --- copula --- bivariate joint distribution --- return period --- rainfall partitioning --- dry tropical forest --- gash model --- interception modelling --- Nordic Sea --- overflow flux --- barotropic pressure --- baroclinic pressure --- annual maximum precipitation --- peaks-over-threshold methods --- statistical analysis --- maximum precipitation frequency analysis --- gamma --- Weibull --- log-gamma --- log-normal --- Gumbel distributions --- nonparametric tests --- drought --- trends --- SPI --- mina basin --- Algeria --- Kunhar River Basin --- streamflow --- trend analysis --- Soil and Water Assessment Tool (SWAT) --- anthropogenic impacts --- hydrologic flood routing --- Muskingum flood routing model --- meta-heuristic optimization --- self-adaptive vision correction algorithm --- Adaptive Water Management --- stakeholder engagement --- legislation --- survey --- uncertainty in water management --- water requirements of aquatic and water dependent ecosystems --- water resources allocation --- water balance model

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In this classic of statistical mathematical theory, Harald Cramér joins the two major lines of development in the field: while British and American statisticians were developing the science of statistical inference, French and Russian probabilitists transformed the classical calculus of probability into a rigorous and pure mathematical theory. The result of Cramér's work is a masterly exposition of the mathematical methods of modern statistics that set the standard that others have since sought to follow. For anyone with a working knowledge of undergraduate mathematics the book is self contained. The first part is an introduction to the fundamental concept of a distribution and of integration with respect to a distribution. The second part contains the general theory of random variables and probability distributions while the third is devoted to the theory of sampling, statistical estimation, and tests of significance.

Mathematical statistics --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistique mathématique --- Mathematical statistics. --- Statistique mathématique --- Statistique mathématique. --- Distribution (théorie des probabilités) --- Distribution (Probability theory) --- A priori probability. --- Addition theorem. --- Additive function. --- Analysis of covariance. --- Arithmetic mean. --- Axiom. --- Bayes' theorem. --- Bias of an estimator. --- Binomial distribution. --- Binomial theorem. --- Bolzano–Weierstrass theorem. --- Borel set. --- Bounded set (topological vector space). --- Calculation. --- Cartesian product. --- Central moment. --- Characteristic function (probability theory). --- Characteristic polynomial. --- Coefficient. --- Commutative property. --- Confidence interval. --- Convergence of random variables. --- Correlation coefficient. --- Degeneracy (mathematics). --- Degrees of freedom (statistics). --- Diagram (category theory). --- Dimension. --- Distribution (mathematics). --- Distribution function. --- Empirical distribution function. --- Equation. --- Estimation theory. --- Estimation. --- Identity matrix. --- Independence (probability theory). --- Interval (mathematics). --- Inverse probability. --- Invertible matrix. --- Joint probability distribution. --- Laplace distribution. --- Lebesgue integration. --- Lebesgue measure. --- Lebesgue–Stieltjes integration. --- Likelihood function. --- Limit (mathematics). --- Linear regression. --- Logarithm. --- Logarithmic derivative. --- Logarithmic scale. --- Marginal distribution. --- Mathematical analysis. --- Mathematical induction. --- Mathematical theory. --- Mathematics. --- Matrix (mathematics). --- Maxima and minima. --- Measure (mathematics). --- Method of moments (statistics). --- Metric space. --- Minor (linear algebra). --- Moment (mathematics). --- Moment matrix. --- Normal distribution. --- Numerical analysis. --- Parameter. --- Parity (mathematics). --- Poisson distribution. --- Probability distribution. --- Probability theory. --- Probability. --- Proportionality (mathematics). --- Quantity. --- Random variable. --- Realization (probability). --- Riemann integral. --- Sample space. --- Sampling (statistics). --- Scientific notation. --- Series (mathematics). --- Set (mathematics). --- Set function. --- Sign (mathematics). --- Standard deviation. --- Statistic. --- Statistical Science. --- Statistical hypothesis testing. --- Statistical inference. --- Statistical regularity. --- Statistical theory. --- Subset. --- Summation. --- Theorem. --- Theory. --- Transfinite number. --- Uniform distribution (discrete). --- Variable (mathematics). --- Variance. --- Weighted arithmetic mean. --- Z-test. --- Distribution (théorie des probabilités)

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Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointe.

Econometric models. --- Econometrics. --- Accuracy and precision. --- Asymptotic distribution. --- Autocorrelation. --- Autoregressive conditional heteroskedasticity. --- Autoregressive model. --- Bayesian statistics. --- Bayesian. --- Bernoulli distribution. --- Bias of an estimator. --- Calculation. --- Central limit theorem. --- Chow test. --- Cointegration. --- Conditional expectation. --- Conditional probability distribution. --- Confidence interval. --- Confidence region. --- Correlation and dependence. --- Correlogram. --- Count data. --- Cross-sectional data. --- Cross-sectional regression. --- Distribution function. --- Dummy variable (statistics). --- Econometric model. --- Empirical distribution function. --- Equation. --- Error term. --- Estimation. --- Estimator. --- Exogeny. --- Exploratory data analysis. --- F-distribution. --- F-test. --- Fair coin. --- Forecast error. --- Forecasting. --- Granger causality. --- Heteroscedasticity. --- Inference. --- Instrumental variable. --- Joint probability distribution. --- Law of large numbers. --- Least absolute deviations. --- Least squares. --- Likelihood function. --- Likelihood-ratio test. --- Linear regression. --- Logistic regression. --- Lucas critique. --- Marginal distribution. --- Markov process. --- Mathematical optimization. --- Maximum likelihood estimation. --- Model selection. --- Monte Carlo method. --- Moving-average model. --- Multiple correlation. --- Multivariate normal distribution. --- Nonparametric regression. --- Normal distribution. --- Normality test. --- One-Tailed Test. --- Opportunity cost. --- Orthogonalization. --- P-value. --- Parameter. --- Partial correlation. --- Poisson regression. --- Probability. --- Probit model. --- Quantile. --- Quantity. --- Quasi-likelihood. --- Random variable. --- Regression analysis. --- Residual sum of squares. --- Round-off error. --- Seemingly unrelated regressions. --- Selection bias. --- Simple linear regression. --- Skewness. --- Standard deviation. --- Standard error. --- Stationary process. --- Statistic. --- Student's t-test. --- Sufficient statistic. --- Summary statistics. --- T-statistic. --- Test statistic. --- Time series. --- Type I and type II errors. --- Unit root test. --- Unit root. --- Utility. --- Variable (mathematics). --- Variance. --- Vector autoregression. --- White test.