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At least 1,297 records · Page 72Linked to original sources

Wildlife mortality at wind facilities: How we know what we know how we might mislead ourselves, and how we set our future course

To accurately estimate per turbine – or per megawatt – annual wildlife mortality at wind facilities, the raw counts of carcasses found must be adjusted for four major sources of imperfect detection: (1) fatalities that occur outside the monitoring period; (2) carcasses that land outside the monitored area; (3) carcasses that are removed by scavengers or deteriorate beyond recognition prior to detection; and (4) carcasses that remain undiscovered by searchers even when present. To accurately estimate regional or national annual wildlife mortality, data must come from a representative (or appropriately weighted) sample of facilities for which estimates of mortality account for all sources of imperfect detection. I argue that the currently available data in the United States and much of the world do not represent the impacts of wind power on wildlife because not all facilities conduct monitoring studies, not all study results are publicly available, and few studies adequately account for imperfect detection. I present examples illustrating the limitations of our current data and pitfalls of interpreting data without accurately adjusting for detection bias. I close by proposing a solution through a simplified monitoring process that can be applied at every facility as part of normal operations. Application of an unbiased estimator that accounts for all sources of imperfect detection would assure comparability of mortality estimates. Public access to reported estimates would achieve representation. With these data we could develop a clearer understanding of how wind power is affecting wildlife throughout the world and inform our efforts to address it.

Book chapter↗

Earthquakes, ShakeMap

ShakeMap® is an open-source software program employed to automatically produce a suite of maps and products that portray the geographical extent and severity of potentially damaging shaking following an earthquake. ShakeMap’s primary purpose is to provide post-earthquake situational awareness for emergency management and response as well as damage and loss estimation. The availability of ShakeMaps immediately after a significant earthquake is critical for the identification of areas likely to be most damaged. Principal users include first responders, utility companies, response and aid agencies, scientists and engineers, and the media. Maps are made publicly available via the Internet within several minutes of an earthquake’s occurrence. ShakeMap is widely deployed in seismically active, well-instrumented portions of the USA and internationally in numerous countries including Italy, Iceland, Greece, Costa Rica, and Switzerland, among others, and the US...

Book chapter↗

Earthquakes, PAGER

PAGER, short for Prompt Assessment of Global Earthquakes for Response, is an automated system developed and run by the US Geological Survey (USGS) that produces information concerning the impact of significant earthquakes around the world within approximately 20 min of any magnitude 5.5 or larger event. PAGER rapidly assesses earthquake impacts by combining populations exposed to estimates of shaking intensity with models of economic and fatality losses based on past earthquakes in each country or region of the world. The main purpose of the PAGER system is to inform emergency responders, government and aid agencies, and the media regarding the scope of the potential disaster. Earthquake alerts – which were formerly sent based on event magnitude and location or population exposure to shaking – are generated based on the estimated range of fatalities and/or economic losses.

Book chapter↗

Earthquakes, did you feel it?

The US Geological Survey (USGS) “Did You Feel It?” ® (DYFI) system is an automated system for rapidly collecting macroseismic intensity data from Internet users’ shaking and damage reports and generating intensity maps immediately following earthquakes. Although the collection and assignment of DYFI-based Macroseismic Intensity (MI) data depart from traditional assignments, they are made more quickly, provide more complete coverage at higher spatial resolution, offer citizen input and interaction, and allow data collection at rates and quantities that were not previously possible. These aspects of Internet-based data collection, in turn, allow for data analyses, graphics, and ways to communicate with the public, opportunities that were not feasible with traditional data-collection approaches.

Book chapter↗

Earthquakes, ShakeCast

ShakeCast® – short for ShakeMap Broadcast – is a fully automated software system for delivering specific ShakeMap products to critical users and for triggering established post-earthquake response protocols. ShakeCast is a freely available, postearthquake situational awareness software application that automatically retrieves earthquake shaking data from ShakeMap to compare ground shaking intensity measures against users’ facilities (Lin and Wald 2008). ShakeCast then generates potential damage assessment and inspection priority notifications, maps, and web-based products for critical users, emergency managers, and those on a need-to-know basis.

Book chapter↗

Understanding the central Great Plains as a coupled climatic-hydrological-human system: Lessons learned in operationalizing interdisciplinary collaboration

This chapter discusses an interdisciplinary and transdisciplinary project to understand the interactions of agriculture, climate, and water resources in the Central Great Plains as a coupled natural-human system. We focus on the Smoky Hills Watershed in Kansas, where we gathered socioeconomic, hydrological, and climatic data, along with ecological data on fish species. The project involved substantial stakeholder engagement, which was complicated by post-truth attitudes about climate science and environmental regulation by some groups. We discuss the challenges of team management, stakeholder engagement, and data integration for modeling, notably the incorporation of stakeholder support for environmental policy in the context of extreme climatic events. We conclude by offering a framework for good collaborative practice to manage the complications of crossing boundaries in transdisciplinary research and outreach.

Kansas↗

S2HM of buildings in USA

The evolution of seismic structural-health monitoring (S 2 HM) of buildings in the USA is described in this chapter, emphasizing real-time monitoring. Rapid and accurate assessment of post-earthquake building damage is of paramount importance to stakeholders (including owners, occupants, city officials, and rescue teams). Relying merely on rapid visual inspection could result in serious damage being missed because it is hidden by building finishes and fireproofing. Absent visible damage to a building’s frame, most steel or reinforced-concrete moment-frame buildings will be green-tagged based on limited visual indications of deformation, such as damage to partitions or glazing. Contrary, uncertainty in judging extent of structural damage may lead an inspector toward a relatively conservative tag, such as a red tag. In such cases, expensive, intrusive, and time-consuming inspections may be recommended to building owners (e.g., following the M w 6.7 1994 Northridge, Calif., earthquake, approximately 300 buildings were subjected to costly inspection of connections (FEMA 352)). Using real-time data-driven computation of drift ratios as the parametric indicator of structural deformation and damage to a structure could be of great value to minimize potential judgmental errors in such assessments. Recorded sensor data are an indication of performance, and performance-based design standards stipulate that the amplitude of relative displacement of a building’s roof (with respect to its base) indicates performance. Establishing sound criteria for performance is the most important issue for S 2 HM process, and since 2000 (in the USA), using real-time computed drift ratios and acceptable threshold criteria form the basis for almost all applications in S 2 HM.

Book chapter↗

Algorithm and data improvements for version 2.1 of the Climate Hazards center’s InfraRed Precipitation with Stations Data Set

To support global drought early warning, the Climate Hazards Center (CHC) at the University of California, Santa Barbara developed the Climate Hazards center InfraRed Precipitation with Stations (CHIRPS) dataset, in collaboration with the US Geological Survey and NASA SERVIR. Specifically designed to support early warning applications, CHIRPS has high a spatial resolution (0.05°), a long period of record (1981 to the near present), and relatively low latencies. Here we will describe a brief formal analysis of distributional bias in CHIRPS2.0. This analysis reveals, as expected, that CHIRPS2.0 means are very similar to observed station data. However, a closer look suggests that low precipitation values are underestimated and high values are over-estimated in the CHIRPS2.0. We describe a potential correction for this below.

Book chapter↗

Frequency distribution

Given a numerical dataset, a frequency distribution is a summary displaying fluctuations of an attribute within the range of values. In contrast to an analytical probability distribution, a frequency distribution always deals with empirically observed values (Everitt and Skondall 2010 ). In general, the larger the number of values, the more useful is the frequency distribution relative to listing all values. Today, multiple software packages allow easy display of a frequency distribution.

Book chapter↗

Random forest

This entry defines and discusses the random forest machine learning algorithm. The algorithm is used to predict class or quantities for target variables using values of a set of predictor variables. It uses decision trees that are generated from bootstrap sampling of the training data set to create a "forest". The entry discusses the algorithm steps, the interpretative tools of the resulting model, current areas of research, and its limitations. Applications to the quantitative geosciences are reviewed as well as availability of software to implement the algorithm.

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Realizations

In statistics, a realization is an observed value of a random variable (Gubner 2006 ). In mathematical geology, the most important realizations are those in the form of maps of spatially correlated regionalized variables. Spatial description of random variables within complex domains and making certain decisions about those require complete knowledge of the attribute of interest at each point in space. However, it is virtually impossible to sample from every location within the domain to gain a complete spatial understanding of the random variables with certainty at different scales. Therefore, limited sampling leaves us with incomplete information, which is the source of uncertainty. Understanding the uncertainty and quantifying it are essential to minimize the risks of decision making. Geostatistical simulation techniques aim to quantify spatial uncertainty of random variables by numerically reproducing the reality, which we have limited knowledge of, in a discretized...

Book chapter↗

Total alkali-silica diagram

The total alkali-silica (TAS) diagram is a scatterplot of the chemical concentrations of silica oxide (SiO 2 ) versus total alkali-sodium oxide (Na 2 O) plus potassium oxide (K 2 O) – in volcanic rocks.

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Random variable

A random variable is a function that assigns a value in a sample space to an element of an arbitrary set (James 1992 ; Pawlowsky-Glahn et al. 2015 ). It is a model for a random experiment: the arbitrary set is an abstraction of the experimental conditions, the values taken by the random variable are in the sample space, and the function itself models the assignment of outcomes, thus also describing its frequency of appearance. In simpler terms, for the purpose of this presentation, a random variable is a function that assigns to each of the outcomes of a random experiment a value with a certain probability. A random variable also goes by stochastic variable and aleatory variable. Random variables are usually annotated as Roman capital letters, such as X or Y .

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Multilayer perceptrons (MLPs)

Artificial neural networks (ANNs) are adaptable systems that can solve problems that are difficult to describe with a mathematical relationship. They seek relationships between different types of datasets with their abilities to learn either with supervision or without. ANNs recognize patterns between input and output space and generalize solutions, in a way simulating the human brain’s learning experience with many relatively simple individual processing elements, called neurons. Neurons are networked (network topology) in a number of ways depending on the problem type and complexity. One of the most widely used ANN learning techniques is supervised learning coupled with a multilayer perceptron (MLP) topology due to its flexible applicability to a wide range of modeling problems involving both general classification and regression. ANNs, due to this flexibility, have been applied to many fields since the 1990s and their theory, types (such as radial basis functions, random...

Book chapter↗

Applications of knowledge and predictions of atmospheric rivers

This chapter reviews how AR research is being applied in real-world situations to address issues of flood planning and emergency intervention. It includes water supply management case studies. Examples comprise five distinct sections that show how AR research is being directly applied to the challenges that water managers, dam operators, crisis-management engineers such as USACE, National Weather Service (NWS) personnel, the media, and others face. These topics include how decision-makers on the ground must iteratively alternate between forecasts and their own field observations, especially in unfolding emergency-response conditions, and the trade-offs necessitated between acting on competing priorities such as flood-risk management and water supply management. Ultimately, almost all AR studies have the potential to directly benefit the public’s need for ongoing water supply as well as for accurate weather forecasts and deployable emergency protocols for natural hazards that necessitate municipal, state, and federal government personnel to collaborate.

Book chapter↗

Aquatic cycling of mercury

This chapter examines crucial processes in the aquatic cycling of mercury (Hg) that may lead to microbial production of neurotoxic and bioaccumulative methylmercury (MeHg), and highlights environmental conditions in the Everglades that make it ideal for MeHg production and bioaccumulation. The role of complexation of Hg 2+ in surface water, especially by dissolved organic matter (DOM), in the transport of mercury to sites of microbial methylation are discussed. Photochemical reactions important in Hg cycling in surface water are also discussed. A principal focus of the chapter is on the environmental conditions that promote MeHg production, especially the role of sulfide and DOM in transport of inorganic Hg into bacteria for methylation, and the types of bacteria that have the ability to methylate Hg. Finally, perturbations to the ecosystem (e.g., fire and drought) that have important effects on Hg cycling are discussed.

Book chapter↗