bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sediment Transport
Authors: Rodrigues Stéphane , Moatar Florentina , Claude Nicolas 
Published: January 2013

AbstractSediment transport is a geological process involved in the morphological evolution of the surface of the earth. Fundamental and applied aspects concerning this process are of interest for human societies. Sediment transport can be triggered by a moving fluid on cohesive or noncohesive sediments produced by the weathering of landmasses. The transport of sediments is influenced by complex interactions between physical features of particles and properties of flow. For instance, the mechanics of flow close to the bed of a river or a sea is implied in the threshold of motion of sediments and in their migration downstream. Processes involved in sediment transport and the estimation of transport rates in rivers and coastal environments constitute major scientific issues. The development of new analytical devices in the recent years offers new perspectives for the evaluation of sediment transport rates by combining direct or indirect measurements performed on the field and semiempirical equations. These tools are promising for understanding of the physical processes involved in sediment transport.
Risk Assessment, Seismological
Authors: Özel Gamze 
Published: January 2013

AbstractSeismic risk is usually defined as the probability of exceeding a specified level of loss due to earthquakes for a set of goods within a reference period of time. Usually it is measured by considering the convolution of three main factors: hazard (probability of earthquakes), vulnerability (probability of damage, given the earthquake), and exposure (probability of loss given the damage). Thus, the seismic risk in a geographical region depends on many uncertain variables pertaining to the three main factors listed above. Some of the uncertainties are reducible through deeper investigation, while others reflect intrinsic randomness and are therefore unavoidable. Seismic risk assessment (SRA) is very important for public safety and hazards mitigation. It is also important for the correct determination of earthquake insurance premiums, and for understanding the social and psychological effects of earthquakes. This study represents an extensive review and evaluation of SRA methodology. SRA strategy, source parameters, attenuation, and local site effects are explained in detail. Global and national seismic hazard maps relating to seismic risk are presented. A variety of references concerned with the problem SRA are given.
Seismological Modeling
Authors: Rathbun Stephen L. 
Published: January 2013

AbstractTemporal point process models for seismological applications are described including Poisson point process, Hawkes' self‐exciting point process for clustered events, and the stress‐release point process for regularly‐spaced events. Earthquake magnitudes may be included in marked versions of the point process models. In seismology, the self‐exciting point process model is often referred to as the epidemic‐type aftershock sequences (ETAS) model and is suitable for modeling aftershock sequences. Dependencies among seismological events may be described using Laguerre polynomials or the modified Omori function and versions of the model can be constructed under which high‐magnitude shocks produce a higher mean number of aftershocks. The ETAS model can also accommodate long‐term trends and periodic behavior. Under the stress‐release model describes the release of tectonic stress following each earthquake. Maximum likelihood estimation of model parameters and its statistical inference are briefly described. Methods are applied to a sequence of 432 California earthquakes.
Spatial Analysis in Ecology
Authors: Fortin Marie‐josée , Dale Mark R.T. , Ver Hoef Jay M. 
Published: January 2013

AbstractThe first step in understanding ecological processes is to identify patterns. Ecological data are usually characterized by spatial structures due to spatial autocorrelation.Spatial autocorrelationrefers to the pattern in which observations from nearby locations are more likely to have similar magnitude than by chance alone. The magnitude, intensity, as well as extent of spatial autocorrelation can be quantified using spatial statistics. Most ecological data exhibit some degree of spatial autocorrelation, depending on the scale at which the data were recorded and then analyzed. Ecological phenomena are also characterized by the multiple ecological processes that act upon them; these processes often operate at more than one spatial scale. Ecological data are a composite of several spatial scales: trends at macroscales; patches, gradients and patterns at meso‐ and local scales; and random patterns at local and microscales. The different processes and patterns at different scales are not necessarily linear or additive, and this contributes to the degree of spatial dependence in the data.
Species Diversity
Authors: Latimer Andrew M. 
Published: January 2013

Abstract
Species diversity is the number of species present in an area or sample, together with the characteristics of their relative abundance distribution, especially its evenness. Species diversity is only one dimension of biodiversity, but because species are fundamental units of ecology and evolution, and because most diversity data consist of species presence/absence, species diversity is often central to assessing and conserving biodiversity. After noting major conceptual and practical issues with species diversity and species richness, we discuss how best to estimate them from samples both for estimating total species number and for comparing diversity across samples and areas. Despite errors associated with extrapolation, recent research has demonstrated that in many cases, given adequate sampling, both nonparametric and parametric methods can estimate total species richness and effectively standardize across samples. Much research has focused on developing and interpreting compound diversity measures that incorporate patterns of both richness and abundance, some with weightings for additional factors such as rarity, phylogenetic distinctness, and ecological function. For a well‐known set of these indices, including the Shannon and Simpson indices, lognormal mean and variance, log‐series α and related neutral theory parameters, we discuss their consistency in ordering samples and their sensitivity and dependence on sample size. Finally, we touch upon major global patterns of diversity including estimates of the number of species on earth (5 million to 50 million) and large‐scale diversity gradients.
Statistical Computing in Environmental Science
Authors: Stein Alfred 
Published: January 2013

Abstract
This article describes statistical computing for the environment. A distinction is made in computing for modeling, statistical analyses, use of large packages, and use of the Internet. Modeling requires the use of basic programming routines; a standard statistical package is recommended for a statistical analysis; typically, geographical information system (GIS) packages and integrating programming environments are interesting large packages; and the Internet offers great opportunities for data and information sharing.
Superfund site, US
Authors: Bowers Teresa Suter 
Published: January 2013

Abstract
The Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) provides authority for the cleanup of abandoned landfills, warehouses, and other properties sufficiently contaminated by hazardous waste such that they were deemed to represent threats to human health and the environment. Superfund sites become identified as such when they are added to the US National Priorities List (NPL), a list prioritizing sites most in need of remedial response due to contamination.
Surveillance, Environmental
Authors: Järpe Eric 
Published: January 2013

Abstract
In many environmental random processes it is relevant to consider a sudden shift in a parameter of the probability distribution of the random variables. This parameter may be a mean level of the process or the natural variation of the variables. Suddenly the parameter can shift to some other, commonly higher, value or to another, commonly increasing, function in time and/or space. To prevent or mitigate the fatal consequences such shifts could imply, it is often of utter importance to detect the change as accurately and quickly as possible. Therefore, stopping rules which are functions of the random process data observed are evaluated, on‐line or retrospectively, in order to indicate a systematic change. Different shift processes are mentioned. An example with data from the Swedish Radiation Safety Authority is given.
Time Series, Periodic
Authors: Lund Robert B. 
Published: January 2013

Abstract
This entry overviews time series with periodic autocovariance structures. Such series often arise in the environmental sciences. Here, extensions to the class of covariance stationary time series are introduced and used to model and describe the autocovariance periodicities. Detection of autocovariance periodicities is considered. A series of monthly averaged temperatures from Griffin, Georgia (United States) is used to illustrate aspects of the discourse.
Aquatic Toxicity Tests
Authors: Fox John F. , Denton Debra L. , Diamond Jerry 
Published: January 2013

Abstract

Aquatic toxicity tests are used in a variety of regulatory and nonregulatory applications to characterize toxicity of known chemicals and environmental water samples, for effluent compliance testing, and to monitor the ambient environment (watersheds), using a variety of aquatic organisms and biological endpoints. This article covers statistical approaches routinely applied to such tests. Concentration‐response models and point estimates (e.g., EC
25
) based on these models are used to characterize concentration‐response relations for toxic chemicals using toxicity data, and also to characterize single effluent or ambient samples for watershed monitoring. Hypothesis tests can also be used to make yes‐no decisions for compliance and watershed determinations. Test experimental designs and statistical methods have been standardized for regulatory use. New approaches or refinements published in the past 10 years are cited here; these can improve interpretation and application of aquatic toxicity tests.
Epigenetics
Authors: Saunders Ian William 
Published: January 2013

Abstract
Epigenetics is the study of heritable variations within cells that affect gene activity without changing the DNA sequence. Epigenetic responses to environmental changes are common and have potential for early biomarkers of contamination and other environmental effects. This article outlines the basic concepts of epigenetics, the technologies involved in measuring epigenetic changes, the genome‐wide study of such changes, or epigenomics, and the implications for environmetrics.
Groundwater Monitoring, Detection, and Compliance
Authors: Gibbons Robert D. , Bhaumik Dulal K. , Aryal Subhash 
Published: January 2013

Abstract

An overview of statistical issues and methods is provided relevant to the analysis of groundwater detection and compliance‐monitoring problems. The rich methodology that has been used in this area goes well beyond groundwater monitoring applications to many other areas of interest in environmental monitoring and statistics. First, we provide an overview of statistical prediction limits for normal, lognormal, and gamma random variables, as well as nonparametric alternatives. Issues related to multiple comparisons, censored data, nonnormality, and verification sampling (i.e., sequential testing) are considered in the statistical developments. Second, we provide an overview of statistical methods that are relevant for compliance monitoring, where interest is often focused on whether on‐site average concentrations exceed a health‐based regulatory standard. This leads to discussion of a variety of statistical approaches based on confidence limits for normal and nonnormal distributions and nonparametric alternatives. A distinction is made between assessment monitoring where the null hypothesis is that contamination
is not
present and corrective action monitoring where the null hypothesis is that contamination
is
present.
Risk Governance
Authors: Shi Peijun , Jaeger Carlo , Ye Qian , Wang Ming , Henning Elke 
Published: January 2013

Abstract
Examples of recent large‐scale disasters show that the traditional risk management process faces difficulties in dealing with emerging unknowable, unmeasurable, and uncontrollable risks in face of the globalization processes. This article starts with the clarification of the difference between risk management and risk governance, and elaborates the importance of risk governance in terms of addressing the institutional design, the development of legislation system, and the establishment and promotion of paradigms. Following the definition of risk governance, this article emphasizes the large‐scale and emerging risks that risk governance deals with and raises four key questions that need to be answered in the practice. A more recent concept, integrated risk governance, is also discussed from the perspectives of risk governance process, risk‐holders, and outcomes. Although people may still possess different opinions and understanding on many aspects of risk governance, the international scientific risk community and intergovernmental organizations have taken serious and solid steps to tackle the issues of risk governance.
E
xtremes for
P
rocesses in
R
andom
E
nvironment
Authors: Piterbarg Vladimir I. 
Published: January 2013

Abstract
We give definitions for random processes in random environment, in particular, Gaussian random processes in random environment, or, in other words, conditionally Gaussian processes. With some constrains on a random environment, we formulate the large deviation principle for Gaussian processes in the random environment. Further, we consider a class of Gaussian processes in random environment that allows exact calculations for probabilities of large extremes. This is a class of Gaussian stationary processes with smooth bounded random stationary variance. We show how the random environment affects on such probabilities.
Max‐Stable Processes
Authors: Padoan Simone A. 
Published: January 2013

AbstractEnvironmental problems such as floods require statistical analysis that takes into account the complex nature of the data, namely, the observations that are sampled at different spatial points in a given region for a certain time. Thus the spatial dependence structure cannot be ignored. Extreme statistics for the design of structures for flood protection, for the study of the structural failures such as those in bridges, dams, and so on, for the prediction of heat waves, and other spatial extremes should be based on a solid theoretical framework. Max‐stable processes provide such a theory and in the last decade have emerged as fertile ground for research and have become a common tool for the statistical modeling of spatial extremes. This article provides a summary of max‐stable processes.
Sampling Agricultural Resources
Authors: Nusser Sarah M. , Zimmer Stephanie A. 
Published: January 2013

Abstract
Agricultural surveys gather data about agricultural production, land management practices, and economic outcomes for farms. Because they involve a complex mix of estimation goals and reporting units, probability samples for agricultural surveys are challenging to design. Two types of sampling frames are used: list frames of agricultural producers and area frames that represent units of land. List frames are cheaper to maintain and easier to select samples from, but may be incomplete. Area frames, which involve selecting land areas or points on the land, are expensive to use, but have full coverage of the population. Some surveys use both types of frames to control costs and estimate coverage bias of the list frame. Regardless of the frame, stratification and multistage sampling are commonly used to balance operational constraints and precision goals for agricultural surveys.
Adaptive Management
Authors: Nichols James D. , Williams Byron K. 
Published: January 2013

Abstract
Adaptive management (AM) is a form of decision analysis developed to deal with recurrent problems that are characterized by uncertainty. This article introduces the concept of AM and provides a brief history of its development in natural resource management, focusing on the relevance of recurrence and uncertainty to decision‐making. The central components of AM include objectives, potential actions, models of system response to actions, monitoring, and a solution algorithm. These components are described, and their respective roles in the iterative AM process specified. “Active” and “passive” forms of AM are defined, with the distinction depending on the degree to which each decision anticipates learning and the resultant improvement in future management. AM has substantial potential for improving natural resource management, but its increased use will depend on our ability to produce many more managers and scientists capable of developing and implementing AM programs.
Environmental Hydrology
Authors: St‐hilaire André , Daigle Anik , Caissie Daniel 
Published: January 2013

AbstractEnvironmental hydrology is the component of water science that focuses on the hydrological cycle with an emphasis on its interface with humans and other organisms. Most studies in environmental hydrology deal with ecohydrology, environmental flows or instream flow requirements, and water quality/quantity analysis and modeling. Ecohydrology deals with interactions between ecosystems and the hydrological cycle at different spatiotemporal scales. Historically, most ecohydrological studies focused on the role of vegetation within the hydrologic cycle (e.g., forest hydrology) with the objective of better quantifying its impact (e.g., timber harvesting) on soil water content, evapotranspiration rates, or rainfall interception.Instream flow needsgenerally refer to the amount of water or flow that is required to maintain ecological functions within a river. Water quality variations in rivers depend on water quantity and movement as well as exchange processes within the river. As an example, water temperature is discussed in greater details. Both deterministic and statistical water temperature models are briefly described.
Massive Data, Models for
Authors: Zhang Hao 
Published: January 2013

Abstract
Massive data present a challenge to the use and manipulation of well‐studied inferential strategies such as maximum likelihood estimation, Bayesian methods, and best linear prediction (kriging) since these methods require the inversion of a large covariance matrix. This entry introduces statistical models that circumvent computational issues, which include local models, covariance tapering, low‐rank models, and Markovian models.
Spectral Methods
Authors: Fuentes Montserrat 
Published: January 2013

Abstract

Spectral methods are a powerful tool for characterizing and modeling spatial dependence, while offering significant computational advantages. Using the spectral representation of a spatial process, we can easily construct valid (positive definite) covariance functions and introduce new models to characterize complex dependence structures of spatial processes. Likelihood approaches for very large spatial datasets are often very difficult, if not infeasible, to implement owing to computational limitations. Even when we can assume normality, exact calculations of the likelihood for a Gaussian spatial process observed at
n
locations requires O(
n
3
) operations. The spectral version of the Gaussian log‐likelihood for gridded data requires O(
n
log2
n
) operations and does not involve calculating determinants.
E
nsemble
M
odels
Authors: Fuentes Montserrat , Foley Kristen 
Published: January 2013

Abstract
An ensemble, or sample, of competing numerical models has been used in many applications to represent different predictions of the true state of a physical system. Ensembles of computer models (e.g. weather, climate, air quality, ocean models) are often used to forecast future states of a physical system and to quantify uncertainty in the numerical model predictions. Various statistical methods have been proposed to improve ensemble predictions from deterministic computer simulations based on actual measurements of the physical systems (e.g. data assimilation, Bayesian model averaging). Ensemble data mining methods have also been developed for a wide variety of applications to combine different versions of a statistical model (e.g. time series models, simple regression models, neural networks) to improve the predictive model performance. We present different statistical criteria that have been proposed to select or weight ensemble members for both numerical model‐based and statistical model‐based ensembles.
Brownian Motion
Authors: Riedi Rudolf H. 
Published: January 2013

Abstract
This article on Brownian motion starts with a short historical survey on its discovery and importance and continues with its most relevant properties. Mathematically detailed formulas are given and connections between the properties are indicated, however no derivations or proofs are provided.
The properties covered start with the mathematical modeling of Brownian motion via the Wiener process; equivalent definitions and constructions such as the Lévy characterization, the midpoint displacement, and the rescaled random walks are discussed. Basic properties given include uniqueness, symmetry, restarted Brownian motion, and time inversion. More advanced topics treated cover stopping times, the strong Markov property, the reflection principle, extreme values, escape times from a strip, and last visits. Path oscillations, stationarity, Gaussian white noise, and Karhunen‐Löwe expansions are also discussed. Brownian motion with drift, the Brownian bridge, as well as intersections, recurrence, and transience of Brownian motion in higher dimensions, are mentioned briefly.
Random Walk
Authors: Lasinio Giovanna Jona , Pollice Alessio 
Published: January 2013

AbstractRandom walks (RW's) appeared in the mathematical and statistical literature in 1905 when Karl Pearson, in a letter to the journalNature, introduced the name for the first time. They are a simple kind ofstochastic processesand describe the random movements of an object in a set of possible positions. RW's areMarkov processesas the conditional distribution of a future state given the present and the past depends only on the present state. As a consequence, the classification ofMarkov chainsasirreducible,recurrent, andperiodiccan be applied to characterize their limiting behavior. An important role in this regard is played by asymptotic results in probability theory concerning sums of iid random variables, such as the laws of large numbers and the central limit theorem. At present, a large number of papers in environmental sciences make explicit or implicit use of RW‐based models. Their application has mainly to do with studies of animal movements and microscopic motility and of particle diffusion in fluids. The implicit use of RW models arises when computational algorithms or complex, possibly hierarchical, statistical models are employed.
Equivariant Estimation
Authors: Hallin Marc , JureČková Jana 
Published: January 2013

Abstract
The concepts of invariance and equivariance of estimators with respect to various groups of transformations of the sample space are defined. The structure and properties of equivariant and minimum risk equivariant estimators are characterized. Emphasis is put on equivariance with respect to shift, scale, and affine transformations.