Current practice using probabilistic methods applied for designing hydraulic structures generally assume that extreme events are stationary. However, many studies in the past decades have shown that hydrological records exhibit some type of nonstationarity such as trends and shifts. Human intervention in river basins (e.g., urbanization), the effect of low-frequency climatic variability (e.g., Pacific Decadal Oscillation), and climate change due to increased greenhouse gasses in the atmosphere have been suggested to be the leading causes of changes in the hydrologic cycle of river basins in addition to changes in the magnitude and frequency of extreme floods and extreme sea levels. To tackle nonstationarity in hydrologic extremes, several approaches have been proposed in the literature such as frequency analysis, in which the parameters of a given model vary in accordance with time. The aim of this paper is to show that some basic concepts and methods used in designing flood-related hydraulic structures assuming a stationary world can be extended into a nonstationary framework. In particular, the concepts of return period and risk are formulated by extending the geometric distribution to allow for changing exceeding probabilities over time. Building on previous developments suggested in the statistical and climate change literature, the writers present a simple and unified framework to estimate the return period and risk for nonstationary hydrologic events along with examples and applications so that it can be accessible to a broad audience in the field. The applications demonstrate that the return period and risk estimates for nonstationary situations can be quite different than those corresponding to stationary conditions. They also suggest that the nonstationary analysis can be helpful in making an appropriate assessment of the risk of a hydraulic structure during the planned project-life.

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One of the problems which often arises in engineering hydrology is to estimate data at a given site because either the data are missing or the site is ungaged. Such estimates can be made by spatial interpolation of data available at other sites. A number of spatial interpolation techniques are available today with varying degrees of complexity. It is the intent of this paper to compare the applicability of various proposed interpolation techniques for estimating annual precipitation at selected sites. The interpolation techniques analyzed include the commonly used Thiessen polygon, the classical polynomial interpolation by least‐squares or Lagrange approach, the inverse distance technique, the multiquadric interpolation, the optimal interpolation and the Kriging technique. Thirty years of annual precipitation data at 29 stations located in the Region II of the North Central continental United States have been used for this study. The comparison is based on the error of estimates obtained at five selected sites. Results indicate that the Kriging and optimal interpolation techniques are superior to the other techniques. However, the multiquadric technique is almost as good as those two. The inverse distance interpolation and the Thiessen polygon gave fairly satisfactory results while the polynomial interpolation did not produce good results.

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This paper presents a multi-member evolution strategy) (ES    to forecast future value of observed time series) 1 , 1 (ARMA model. The proposed method is simple and straight forward and doesn't required any problem specific parameter tuning of the problem. The experiments designed based on simulate) (ES    for different values of sample size (n=25,50,100),model parameters  set (75 , 3. 0 , 05. 0   ) and  set to (9. , 4. 0 , 1. 0   ) and use lead time for forecasting future value equal to (l=1,2,3).The value of  , take equal to (15,100) beside this, there is anther experiment designed for simulating one of method which is known as Box-Jenkins with same values of sample size, model parameters and leads time(l) for number of replicate (RR=1000). Results of this study has cleared by numbers of figures and tables, which are made to clear compression between ES-algorithm and B.J method based on computing values of FMSE (Forecasting Mean Square Error) & Thiels' (U-statistic) ,statistics used as tools to measures reliability of ES-algorithm and also used to clear accuracy of ES algorithm results. Table(1), tables (2-7) and figures (4-9) results of statistics show the reliability of) (ES    algorithm to producing individuals which give reasonably predictions of future values of time series for different values of sample size and lead time values of model parameters.

Abstract. The Earth's climate system is highly nonlinear: inputs and outputs are not proportional, change is often episodic and abrupt, rather than slow and gradual, and multiple equilibria are the norm. While this is widely accepted, there is a relatively poor understanding of the different types of nonlinearities, how they manifest under various conditions, and whether they reflect a climate system driven by astronomical forcings, by internal feedbacks, or by a combination of both. In this paper, after a brief tutorial on the basics of climate nonlinearity, we provide a number of illustrative examples and highlight key mechanisms that give rise to nonlinear behavior, address scale and methodological issues, suggest a robust alternative to prediction that is based on using integrated assessments within the framework of vulnerability studies and, lastly, recommend a number of research priorities and the establishment of education programs in Earth Systems Science. It is imperative that the Earth's climate system research community embraces this nonlinear paradigm if we are to move forward in the assessment of the human influence on climate.

Statistical and physically-based methods have been used for designing and assessing water infrastructure such as spillways and stormwater drainage systems. Traditional approaches assume that hydrological processes evolve in an environment where the hydrological cycle is stationary over time. However, in recent years, it has become increasingly evident that in many areas of the world the foregoing assumption may no longer apply, due to the effect of anthropogenic and climatic induced stressors that cause nonstationary conditions. This has attracted the attention of national and international agencies, research institutions, academia, and practicing water specialists, which has led to developing new techniques that may be useful in those cases where there is good evidence and attribution of nonstationarity. We review the various techniques proposed in the field and point out some of the challenges ahead in future developments and applications. Our review emphasizes hydrological design to protect against extreme events such as floods and low flows.

Historical and paleoflood data have become an important source of information for flood frequency analysis. A number of studies have been proposed in the literature regarding the value of historical and paleoflood information for estimating flood quarttiles. These studies have been generally based on computer simulation experiments. In this paper the value of using systematic and historical/paleoflood data relative to using systematic records alone is examined analytically by comparing the asymptotic variances of flood quantiles assuming a two-parameter general extreme value marginal distribution, type 1 and type 2 censored data, and maximum likelihood estimation method. The results of this study indicate that the value of historical and paleoflood data for estimating flood quarttiles can be small or large depending on only three factors: the relative magnitudes of the length of the systematic record (N) and the length of the historical period (M); the return period (T) of the flood quanti!e of interest; and the return period (H) of the threshold level of perception. For instance, for N --50, M = 50 and T = 500, the statistical gain for type 2 censoring becomes significantly larger than for type 1 censoring as H becomes greater than 100 years. In addition, computer experiments have shown that the results regarding the statistical gain based on asymptotic considerations are valid for the usual sample sizes. 1Now at books, damage reports (for instance, records of bridge repairs), unpublished written records, and verbal communication from the general public. Paleoflood data are generally obtained from the botanical evidence left by past floods through corrosion scars, adventitious sprouts, ring anomalies, and vegetation age distribution [Hupp, 1986, 1988] and from paleostage indicators such as silt lines [O'Connor et al., 1986], scour lines [Jarrett and Malde, 1987], and slackwater deposits [Kochel and Baker, 1988]. Both historical and paleoflood information can provide data in various forms such as the magnitude and date of one or more large floods and the occurrence of one or more floo.ds greater than a certain threshold value. The combination of historical and paleoflood data can provide the most accurate information of the magnitude and frequency of extreme floods occurring prior to the systematic period [Fuertsch, 1992]. Frequency analysis of flood data arising from systematic, historical, and paleoflood records has been proposed by several investigators. A review of the literature on this subject has been made by Stedinger and Baker [1987]. Empirical and nonparametric methods for determining flood quantiles have been suggested by many [e.g., Benson, 1950; USWRC, 1982; Hirsch and Stedinger, 1987; Hirsch, 1987; Adamowski and Feluch, 1990; Guo and Cunnane, 1991; also J. D. Salas and B. Fernandez, Plotting position formulas based on systematic, historical and paleoflood data, submitted to Journal of Hydrology, 1993]. Likewise, parametric methods based on the method of moments estimation and the log Pearson type 3 ...