James R. Wallis

Extreme environmental events, such as floods, droughts, rainstorms and high winds, have severe consequences for human society. Regional frequency analysis helps to solve the problem of estimating the frequency of these rare events at one site, by using data from several sites. This book is the first complete account of the L-moment approach to regional frequency analysis. Regional Frequency Analysis comprehensively describes the theoretical background to the subject, is rich in practical advice for users, and contains detailed examples that illustrate the approach. This book will be of great value to hydrologists, atmospheric scientists and civil engineers, concerned with environmental extremes.

The complexity of a forest ecosystem makes difficult any attempt to synthesize knowledge about forest dynamics or to perceive the implications of information and assumptions regarding forest growth. Although digital computer simulation seems to offer a potential for creating a complete model of forest growth, little progress has been reported. Computer simulation has been carried out for the growth of trees in even-aged stands of a single species (Mitchell 1969), and for meteorological energy exchange in a forest canopy (Waggoner & Reifsnyder 1968). A specific simulation built directly from Hubbard Brook data has been reported (Siccama et al. 1969). Successional change in northern hardwood forests has been predicted from observed birth and death rates (Leak 1970). A conceptual model has been created for the growth of individual tree seedlings from rates of photosynthesis and the distribution of photosynthates (Ledig 1969). Computer simulation has been carried out for some aspects in a few other terrestrial ecosystems, such as productivity in a corn crop (Duncan et al. 1967); but apparently no one has successfully reproduced the major characteristics of a mixed-species, mixed-aged forest from a conceptual basis. A computer simulation of forest growth is now developed that successfully reproduces the population dynamics of the trees in a mixed-species forest of north-east North America. The simulator is designed to be used in the Hubbard Brook Ecosystem Study and to provide output in the same form as the original vegetation survey of that study (Bormann et al. 1970). However, the underlying concepts of the simulation are general. The properties of each species are derived from its entire geographic range and in theory any non-hydrophytic species whose relevant characteristics are known can be entered into the simulation. In the present version of the program, the description of the environment is restricted to those features that have been recorded for the Hubbard Brook Forest, but the relative importance attached to each environmental factor has been influenced by the environmental characteristics of the north-eastern United States. It is hoped that a wide dissemination of this simulator will encourage others to test this version with their data and hence lead to later versions of wider usefulness and applicability. The basic goal was to produce a dynamic model of forest growth, a model in which changes in the state of the forest are a function of the present state and random components. This approach has two advantages over the curve-fitting approach to forest growth: first, the simulator can be regarded as a repository for an integrated knowledge of the ecosystem; second, additional hypotheses can be formulated and tested using Monte Carlo samples of simulator runs and comparing the results with observed data. For

We use the method of probability-weighted moments to derive estimators of the parameters and quantiles of the generalized extreme-value distribution. We investigate the properties of these estimators in large samples, via asymptotic theory, and in small and moderate samples, via computer simulation. Probability-weighted moment estimators have low variance and no severe bias, and they compare favorably with estimators obtained by the methods of maximum likelihood or sextiles. The method of probability-weighted moments also yields a convenient and powerful test of whether an extreme-value distribution is of Fisher-Tippett Type I, II, or III.

Distributions whose inverse forms are explicitly defined, such as Tukey's lambda, may present problems in deriving their parameters by more conventional means. Probability weighted moments are introduced and shown to be potentially useful in expressing the parameters of these distributions.

By ‘Noah Effect’ we designate the observation that extreme precipitation can be very extreme indeed, and by ‘Joseph Effect’ the finding that a long period of unusual (high or low) precipitation can be extremely long. Current models of statistical hydrology cannot account for either effect and must be superseded. As a replacement, ‘self‐similar’ models appear very promising. They account particularly well for the remarkable empirical observations of Harold Edwin Hurst. The present paper introduces and summarizes a series of investigations on self‐similar operational hydrology.