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At least 1,315 records · Page 73Linked to original sources
USGS2020

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↗
USGS2019

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↗
USGS2019

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↗
USGS2020

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↗
USGS2021

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↗
USGS2021

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.

Book chapter↗
USGS2021

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↗
USGS2021

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.

Book chapter↗
USGS2021

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 .

Book chapter↗
USGS2021

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↗
USGS2020

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↗
USGS2019

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↗
USGS2019

Sulfur contamination in the Everglades, a major control on mercury methylation

In this chapter sulfur contamination of the Everglades and its role as a major control on methylmercury (MeHg) production is examined. Sulfate concentrations over large portions of the Everglades (60% of the ecosystem) are elevated or greatly elevated compared to background conditions of <1 mg/L. Land and water management practices in south Florida are the primary reason for the high levels of sulfate loading to the Everglades. Marshes in the northern Everglades that are highly enriched in sulfate have average concentrations of 60 mg/L, but water in canals in the Everglades Agricultural Area (EAA) contain the highest concentrations of sulfate averaging 60–70 mg/L. Studies that examined the mass balance of sulfur to the Everglades have determined that the primary sources of sulfate include: sulfur currently used in agriculture, and natural and legacy agricultural sulfur released by oxidation of organic soil within the EAA. The extensive loading of sulfate to the ecosystem increases microbial sulfate reduction, the dominant microbial process driving mercury methylation and MeHg production. The biogeochemical processes linking sulfate loading and MeHg production, however, are complex. MeHg production increases as sulfate levels rise from levels <1 mg/L up to about 20 mg/L. However, production of sulfide (a byproduct of microbial sulfate reduction) starts to inhibit MeHg production above 20 mg/L. Sulfate loading to canals in the EAA has impacted the northern Everglades the most, but the Everglades canal system can transport sulfate as far as Everglades National Park (ENP), 80 km further south. Plans to deliver more water to ENP as part of restoration may increase overall sulfate loads to the southern Everglades. Reduction of sulfate loading should be a major goal of Everglades restoration because of the many negative effects of sulfate on the ecosystem. The ecosystem has been shown to respond quickly to reductions in sulfate loading, and strategies for reducing sulfate loading may produce positive outcomes for the Everglades in the near-term. Strategies for reducing sulfate loading will need to include: best management practices for agricultural use of sulfate, approaches to minimize soil oxidation in the EAA, and modifications to stormwater treatment areas to improve sulfate retention.

Florida↗
USGS2020

Quaternary eolian dunes and sand sheets in inland locations of the Atlantic Coastal Plain Province, USA

Quaternary eolian dunes and sand sheets that are stabilized by vegetation are present throughout many inland locations of the Atlantic Coastal Plain province (USA). These locations include river valleys, the Carolina Sandhills region, adjacent to Carolina Bays, and upland areas of the northern coastal plain. The eolian dunes are primarily parabolic in river valleys and in upland areas of the northern coastal plain, linear in the Carolina Sandhills region, and arcuate adjacent to Carolina Bays. Optically stimulated luminescence (OSL) ages from the eolian sands range from circa (ca.) 92–5 ka, revealing that they are relict features that are not active today. These sands have been degraded by vegetation and pedogenic processes, and are stabilized under modern environmental conditions. Most of the OSL ages are approximately coincident with the last glacial maximum (LGM), when conditions were generally colder, drier, and windier. Various features associated with these eolian dunes and sand sheets suggest that the winds that mobilized the sand blew from the northwest in the coastal plain region of Maryland and Delaware, and from the west in the coastal plain region of North Carolina, South Carolina, and Georgia. Most of the eolian dunes and sand sheets are composed of fine to medium sand, although a substantial silt component is present in the northern coastal plain, and a substantial coarse sand component is present in the Carolina Sandhills region. Eolian sand mobilization would have been facilitated by conditions of stronger wind velocity (at least 4–6 m/s), lower air temperature, lower air humidity, and (or) reduced vegetation cover. Eolian sediment mobilization appears to have occurred episodically at any given site, although sites that are farther south have preserved a greater proportion of eolian sands yielding pre-LGM ages (indicating that the southern landscapes farther from the ice sheet have experienced less reworking).

Atlantic Coastal Plain Province↗
USGS2020

Sand dunes, modern and ancient, on southern Colorado Plateau tribal lands, southwestern USA

A mantle of both active and stable aeolian sand covers approximately 34,000 km 2 of northern Arizona, western New Mexico and southern Utah on the southern Colorado Plateau. From west to east, these deposits can be subdivided into the Kaibab-Moenkopi dunes, Chinle Valley dunes, and Chaco dunes, all of which include relict, partly stable and mobile aeolian sand. Locally, these deposits have distinct compositional characteristics. An examination of previous studies into disparate aspects of Colorado Plateau dunes, taken in the context of local geology, Quaternary landscape history and geomorphic processes, provides new insights into interpretation of this regional aeolian sedimentary record. Additional new data about the characteristics of the deposits, and an assessment of present-day climatic conditions enhances our ability to interpret the relative influences of ecosystem and geomorphologic processes with climate variability that continue to influence both new dune formation and reactivation of older deposits. Taken as a whole, the data emphasizes the role that local landscape conditions and history play in providing the context for correctly interpreting aeolian activity and depositional environments, and whether sediment supply or climate play a dominant role in sand dune formation. This is particularly true in the Little Colorado River Valley of northeastern Arizona, where Quaternary volcanic activity has significantly influenced the local landscape processes, deposit characteristics, and dune paleohistory.

Book chapter↗
USGS2020

“Good” and “bad”: Human perceptions of and interactions with urban wildlife

Urban environments offer habitat for many species of animals. Although some of those are ubiquitous and/or undesirable, others are native and in some cases, of conservation value. In many cases, urban wildlife populations are a source of enjoyment for human residents, who sometimes invest considerable amounts in attracting them to yards and public spaces. Their presence there can serve an important educational role that helps protect non-urban habitats and species. Nonetheless, urban wildlife must survive what has been termed a “landscape of fear.” Although some of the urban wildlife that do well in this environment are benign, other populations – sometimes of a species that, in other locations, is iconic and desirable – can become problematic. Some species can serve as vectors that carry important zoonosis, such as the plague or diseases that affect other wildlife. Others can create noise or olfactory nuisances and degrade structures or usability of public spaces. Some pose hazards at busy airports, whereas still others may present an envenomation or predation risk on unwary humans. Here, we review the role that reptiles, birds, and mammals play in urban environments and discuss how urban wildlife rehabilitation centers help address some related issues. We close by looking ahead and trying to predict how global patterns such as increased urbanization and population growth may affect urban wildlife and its value for conservation.

Book chapter↗