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At least 127 records · Page 7Linked to original sources

USGS2005

Calibration of numerical models for small debris flows in Yosemite Valley, California, USA

This study compares documented debris flow runout distances with numerical simulations in the Yosemite Valley of California, USA, where about 15% of historical events of slope instability can be classified as debris flows and debris slides (Wieczorek and Snyder, 2004). To model debris flows in the Yosemite Valley, we selected six streams with evidence of historical debris flows; three of the debris flow deposits have single channels, and the other three split their pattern in the fan area into two or more channels. From field observations all of the debris flows involved coarse material, with only very small clay content. We applied the one dimensional DAN (Dynamic ANalysis) model (Hungr, 1995) and the two-dimensional FLO2D model (O'Brien et al., 1993) to predict and compare the runout distance and the velocity of the debris flows observed in the study area. As a first step, we calibrated the parameters for the two softwares through the back analysis of three debris- flows channels using a trial-and-error procedure starting with values suggested in the literature. In the second step we applied the selected values to the other channels, in order to evaluate their predictive capabilities. After parameter calibration using three debris flows we obtained results similar to field observations We also obtained a good agreement between the two models for velocities. Both models are strongly influenced by topography: we used the 30 m cell size DTM available for the study area, that is probably not accurate enough for a highly detailed analysis, but it can be sufficient for a first screening. European Geosciences Union ?? 2005 Author(s). This work is licensed under a Creative Commons License.

Natural Hazards and Earth System Sciences↗

USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗

USGS2004

User's Guide for the MapImage Reprojection Software Package, Version 1.01

Scientists routinely accomplish small-scale geospatial modeling in the raster domain, using high-resolution datasets (such as 30-m data) for large parts of continents and low-resolution to high-resolution datasets for the entire globe. Recently, Usery and others (2003a) expanded on the previously limited empirical work with real geographic data by compiling and tabulating the accuracy of categorical areas in projected raster datasets of global extent. Geographers and applications programmers at the U.S. Geological Survey's (USGS) Mid-Continent Mapping Center (MCMC) undertook an effort to expand and evolve an internal USGS software package, MapImage, or mapimg, for raster map projection transformation (Usery and others, 2003a). Daniel R. Steinwand of Science Applications International Corporation, Earth Resources Observation Systems Data Center in Sioux Falls, S. Dak., originally developed mapimg for the USGS, basing it on the USGS's General Cartographic Transformation Package (GCTP). It operated as a command line program on the Unix operating system. Through efforts at MCMC, and in coordination with Mr. Steinwand, this program has been transformed from an application based on a command line into a software package based on a graphic user interface for Windows, Linux, and Unix machines. Usery and others (2003b) pointed out that many commercial software packages do not use exact projection equations and that even when exact projection equations are used, the software often results in error and sometimes does not complete the transformation for specific projections, at specific resampling resolutions, and for specific singularities. Direct implementation of point-to-point transformation with appropriate functions yields the variety of projections available in these software packages, but implementation with data other than points requires specific adaptation of the equations or prior preparation of the data to allow the transformation to succeed. Additional constraints apply to global raster data. It appears that some packages use the USGS's GCTP or similar point transformations without adaptation to the specific characteristics of raster data (Usery and others, 2003b). It is most common for programs to compute transformations of raster data in an inverse fashion. Such mapping can result in an erroneous position and replicate data or create pixels not in the original space. As Usery and others (2003a) indicated, mapimg performs a corresponding forward transformation to ensure the same location results from both methods. The primary benefit of this function is to mask cells outside the domain. MapImage 1.01 is now on the Web. You can download the User's Guide, source, and binaries from the following site: http://mcmcweb.er.usgs.gov/carto_research/projection/acc_proj_data.html

Open-File Report↗

USGS2011

Quality assurance and quality control of geochemical data—A primer for the research scientist

Geochemistry is a constantly expanding science. More and more, scientists are employing geochemical tools to help answer questions about the Earth and earth system processes. Scientists may assume that the responsibility of examining and assessing the quality of the geochemical data they generate is not theirs but rather that of the analytical laboratories to which their samples have been submitted. This assumption may be partially based on knowledge about internal and external quality assurance and quality control (QA/QC) programs in which analytical laboratories typically participate. Or there may be a perceived lack of time or resources to adequately examine data quality. Regardless of the reason, the lack of QA/QC protocols can lead to the generation and publication of erroneous data. Because the interpretations drawn from the data are primary products to U.S. Geological Survey (USGS) stakeholders, the consequences of publishing erroneous results can be significant. The principal investigator of a scientific study ultimately is responsible for the quality and interpretation of the project's findings, and thus must also play a role in the understanding, implementation, and presentation of QA/QC information about the data. Although occasionally ignored, QA/QC protocols apply not only to procedures in the laboratory but also in the initial planning of a research study and throughout the life of the project. Many of the tenets of developing a sound QA/QC program or protocols also parallel the core concepts of developing a good study: What is the main objective of the study? Will the methods selected provide data of enough resolution to answer the hypothesis? How should samples be collected? Are there known or unknown artifacts or contamination sources in the sampling and analysis methods? Assessing data quality requires communication between the scientists responsible for designing the study and those collecting samples, analyzing samples, treating data, and interpreting results. This primer has been developed to provide basic information and guidance about developing QA/QC protocols for geochemical studies. It is not intended to be a comprehensive guide but rather an introduction to key concepts tied to a list of relevant references for further reading. The guidelines are presented in stepwise order beginning with presampling considerations and continuing through final data interpretation. The goal of this primer is to outline basic QA/QC practices that scientists can use before, during, and after chemical analysis to ensure the validity of the data they collect with the goal of providing defendable results and conclusions.

Open-File Report↗

USGS2023

Modeling post-wildfire hydrologic response: Review and future directions for applications of physically based distributed simulation

Wildfire is a growing concern as climate shifts. The hydrologic effects of wildfire, which include elevated hazards and changes in water quantity and quality, are increasingly assessed using numerical models. Post-wildfire application of physically based distributed models provides unique insight into the underlying processes that affect water resources after wildfire. This work reviews and synthesizes post-wildfire applications of physically based distributed models by examining the scales and geographic/ecohydrologic distribution of model applications, hydrologic response process representation, model parameterization, and model performance metrics. Highlighted gaps and opportunities for advancing physically based distributed hydrologic response modeling after wildfire include the following: (a) applying models in under-represented geographic (S. America, Africa, Asia) and ecohydrologic regions (arid or dry subhumid climates), (b) incorporating all four major streamflow generation mechanisms (infiltration excess, saturation excess, subsurface storm flow, and groundwater flow), (c) representing integrated vadose zone and saturated zone processes to better capture subsurface streamflow generation, (d) building new remotely sensed model parameterization methods for precipitation interception, infiltration, and overland flow that account for burn severity and recovery, (e) incorporating distributed state variables (e.g., soil moisture, groundwater levels) in model performance assessment, (f) designing model intercomparison studies, including field datasets specifically for post-wildfire model development and validation, (g) linking mechanistic vegetation regrowth models with hydrologic models to improve simulation of process shifts as ecosystems recover, and (h) creating a new community modeling framework to integrate modeling advances across the wildfire science community.

Earth's Future↗

USGS2014

Advances in a distributed approach for ocean model data interoperability

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

Journal of Marine Science and Engineering↗

USGS2024

A Bayesian age from dispersed plagioclase and zircon dates in the Los Chocoyos ash, Central America

The eruption that produced the modern Atitlán caldera in Guatemala has a Volcanic Explosivity Index (VEI) greater than 8, making it the largest of the Quaternary in the Central American Volcanic Arc (CAVA). It generated ∼1220 km 3 (730 km 3 dense rock equivalent) of dispersed rhyolitic ash and unwelded pyroclastic flow deposits known as the Los Chocoyos ash. Despite its scale, the age of the Los Chocoyos ash has been difficult to constrain. Its inferred position in marine sediment off the coast of Ecuador suggests an oxygen isotope-based age of 84 ± 5 thousand years (ka) that has been used for decades. A recent effort to determine the eruption age of the Los Chocoyos ash used U-Th/He and U/Th disequilibrium methods that both yield overdispersed sets of dates from zircon. Using U/Th disequilibrium corrected U-Th/He dates, which include some that are up to 19 ± 8 kyr younger than the youngest U/Th disequilibrium dates, an eruption age of 75 ± 4 ka has been proposed, which overlaps the eruption age of the Youngest Toba Tuff, 40 Ar/ 39 Ar dated at 73.9 ± 0.6 ka. Critically, the above findings have led to the hypothesis that this pair of sulfate-rich supereruptions may be responsible for a global cooling event ∼74 ka. To further test this supereruption doublet hypothesis we present new 40 Ar/ 39 Ar and U-Th disequilibrium data from plagioclase and zircon rim surfaces, respectively, to quantify the timing of the Los Chocoyos eruption. We apply Bayesian estimation to the overdispersed sets of 40 Ar/ 39 Ar plagioclase and U-Th disequilibrium zircon dates. This approach accounts for retention of radiogenic 40 Ar in plagioclase in a subset of crystals and for crystallization of zircon that may precede the time of eruption and yields remarkably consistent ages from two independent chronometers. The Bayesian estimate of 98 ± 6 ka calculated from 308 individual dates identifies the population nearest to the time of the Los Chocoyos eruption. Interpreted as an eruption age, this finding precludes a link between the Los Chocoyos eruption and global cooling ∼74 ka and rules out the supereruption doublet trigger.

Earth and Planetary Science Letters↗

USGS2022

System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)

Executive Summary This report addresses system characterization of the Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (band to band and image to image), radiometric, and spatial performances. Results of these analyses indicate that PRISMA has a band-to-band geometric performance in the range of −0.046 to 0.040 pixel; an image-to-image geometric performance (relative to the Landsat 8 Operational Land Imager) in the range of −60.791 meters (m; −2.03 pixels) to 299.541 m (9.98 pixels); a radiometric performance in the range of −0.037 to −0.001 in offset and 1.026 to 1.274 in slope; and a spatial performance with a relative edge response in the range of 0.56 to 0.63, full width at half maximum in the range of 1.84 to 1.97 pixels, and a modulation transfer function at a Nyquist frequency in the range of 0.054 to 0.096. Regarding fairly large geometric accuracy, the following explanation is provided to help the reader. The geometric accuracy required for PRISMA is a 200-m circular error at 90 percent (CE90) without ground control points (GCPs), a 15-m CE90 using GCPs is documented in the PRISMA mission overview (Agenzia Spaziale Italiana, 2021). The PRISMA images used for the current system characterization were georeferenced without using any GCPs; thus, the 200-m geometric accuracy requirement is applied. Beginning in 2022, a worldwide GCP database will be used in the PRISMA product processing chain, which will improve georeferencing accuracy to meet the 15-m CE90 requirement.

Open-File Report↗

USGS2024

Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: The hotspot learning and identification network

An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.

Alaska↗

USGS2026

Mars sample return campaign: Biological risk and a proposed sample safety assessment protocol

Returning surface samples from Mars to Earth has been a major planetary science objective, with the potential for the detection of microbiological life and the possibility of improving our understanding of the origins of life. The National Aeronautics and Space Administration and the European Space Agency assembled a team to assess the level of risk that returned samples could contain potential biohazards. The team was chartered with optimizing previous sample safety assessment strategies, defining what constitutes a biological hazard, developing a protocol to test for biohazards, and establishing a statistical framework to determine if samples may be safe for release from a high-containment facility. This report presents the biological context for a proposed three-step protocol for testing returned samples, including how to determine if microorganisms are present, and if they could be (or were recently) alive.

Applied and Environmental Microbiology↗

USGS2023

Rapid pre-explosion increase in dome extrusion rate at La Soufrière, St. Vincent quantified from synthetic aperture radar backscatter

The extrusion rate of a lava dome is a critical parameter for monitoring silicic eruptions and forecasting their development. Satellite radar backscatter can provide unique information about dome growth during a volcanic eruption when other datasets (e.g., optical, thermal, ground-based measurements, etc.) may be limited. Here, we present an approach for estimating volcanic topography from individual backscatter images. Using data from multiple SAR sensors we apply the method to the dome growth during the 2021 eruption at La Soufrière, St. Vincent. We measure an average extrusion rate of 1.8 m 3 s −1 between December 2020 and March 2021 before an acceleration in extrusion rate to 17.5 m 3 s −1 in the 2 days prior to the explosive eruption on 9 April 2021. We estimate a final dome volume of 19.4 million m 3 , extrapolated from the SAR sensors, with approximately 15% of the total extruded volume emplaced in the last 2 days. A possible explanation for the acceleration in extrusion rate could be the combined emptying of a conduit and reservoir of older material before the ascent of gas-rich magma in April 2021.

La Soufrière, Saint Vincent↗

USGS2017

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.

International Journal of Applied Earth Observation↗

USGS2019

Demonstrating the value of Earth observations—methods, practical applications, and solutions—group on Earth observations side event proceedings

Executive Summary The U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the European Association for Remote Sensing Companies, and the European Space Agency in coordination with the GEOValue Community hosted a side event to the Group on Earth Observations Plenary on October 23–24, 2017, in Washington, D.C. The workshop, entitled “Demonstrating the Value of Earth Observations: Methods, Practical Applications and Solutions,” brought together more than 60 international experts including economists, scientists, and engineers to consider the state of the science and applications of valuing Earth observations (EO). This 2-day workshop built upon previous activities developed under the GEOValue initiative. This workshop brought together expert analysts from multiple disciplines and backgrounds who are developing methods to identify and measure the value of information generated from the use of satellite and in-situ data. The mix of government agencies, international financial institutions, and independent consultants who participated in the workshop blended to develop a rich mix of views, approaches, and outcomes. During the first part of the workshop, the focus was on the latest science in valuing EO. A number of methodologies were described. Approaches generally assess the societal benefits of specific actions (for example, investments in EO). Some methods focus on broad measures of economic activity (for example, gross domestic product) or methods to assess total economic value such as contingent valuation surveys. Alternatively, use-case approaches (a use case is defined as an evaluation in which one or more decisions, applications, or other uses of data, information, and information products are specifically considered) start with the specific actions and how information is used to support decision making and affect outcomes. The second part of the meeting was focused on the use and development of value chains and decision trees. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. In terms of EO, the value chain approach can be applied to consider societal benefits of the data and assess the value of data and data features. The EO value chain considers the geospatial data sources and the processing of the data into value added information to be incorporated into decision-support systems, leading to decision makers’ actions. To understand the value of EO, one would also need to recognize the demand side of the equation or how EO benefits users. Extending the value chain concept and incorporating tenets of Bayesian decision making, a decision tree would include one or more use cases. The value provided by the marginal increase in information could flow from one or several parts of the supply side of the value chain. The decision tree is based on the premise that information has no value if it is not used in at least one decision. By connecting the value chain and the decision tree, a framework is created that allows for conceptualizing the value of EO in its many uses. One can then apply economic techniques to monetize the marginal benefit of an outcome with information versus one without. A third part of the meeting applied the value chain and decision-tree frameworks to five specific thematic areas, each with the focus of using information for a decision point: Effect of increasing temperatures on human health; Flooding—Mitigating, managing, and avoiding impacts to safety and property damage; Harmful algal blooms—Effects on human health, recreation, and tourism; Energy and mineral supply—Mitigating, managing, and avoiding impacts of shortfalls on the economy; and Effects of natural hazards on transportation systems—Effects on mobility, safety, and the economy. During the working session, five separate groups worked to define and delineate the value chains and decision trees associated with each topic, discussing the related challenges and data needs. The outcomes were reported back to the full group. Because of the complexity of the topics, most groups first identified a network of value chains and then narrowed the scope to develop a single value chain to address their group’s topic. Although they worked separately and on different topics, the groups came to similar conclusions, concurring that the value chain and decision-tree frameworks are very effective for informing quantitative impact assessments and developing a relatable narrative to assist the public in understanding the link between EO and citizens.

Open-File Report↗

USGS2001

HAZUS 99 estimated annualized earthquake losses for the United States

Recent earthquakes around the world show a pattern of steadily increasing damages and losses. The increases are due primarily to two factors: 1) significant growth in urban areas that are prone to earthquakes; and 2) the vulnerability of the older building stock, even buildings that were constructed within the past twenty years. In the United States, earthquake risk has grown substantially with development, while the earthquake hazard has remained relatively constant. To understand the hazard, we study earthquake characteristics and locales in which they occur. To understand risk, we must include characteristics of the built environment in the locales where earthquakes occur, and we must assess potential damages to the built environment and the people who use it. That is a complex undertaking and one this study intends to advance. It is important to estimate the varying degrees of seismic risk throughout the United States because we need that understanding in order to make informed decisions on mitigation policies, priorities, strategies and funding levels—in both the public and private sectors. We can reduce earthquake losses to older buildings by rehabilitating them, and we can reduce earthquake losses in new buildings by applying seismic codes to their design and construction. However, decisions to spend money on either of those solutions require evidence of risk. In the absence of a nationally accepted criterion and methodology for comparing seismic risk across regions, a consensus on optimal mitigation approaches has been difficult to reach. We are all aware of regions with high hazard and high risk, such as Los Angeles, but there is growing recognition that some regions with low seismic hazard actually have high seismic risk, as is the case in New York City and Boston. This risk results, in part, from concentrations of buildings and infrastructure built without use of seismic codes or provisions. Additionally, mitigation policies and practices in the public and private sectors may not have been adopted because a community’s earthquake risk was not clearly demonstrated, and neither was the value of mitigation measures in reducing that risk. The low hazard/high risk problem exists in a number of areas in the U.S., where the infrequency of damaging earthquakes has been interpreted, wrongly, as lack of risk. While earth scientists work to increase knowledge about the hazard throughout the United States, structural engineers and other professionals work to enumerate the many factors that comprise risk. This study is one result of that endeavor. It is based on loss estimates generated by Hazards U.S. (HAZUS), a Geographic Information System (GIS)- based earthquake loss estimation tool, developed by the Federal Emergency Management Agency (FEMA) in cooperation with the National Institute of Building Sciences (NIBS). The HAZUS tool provides an approach to quantifying future earthquake losses that is national in scope, uniform in application, and comprehensive in its coverage of the built environment.

Report↗

USGS1949

Fourth special report of the Hawaiian Volcano Observatory of the U.S. Geological Survey and the Hawaiian Volcano Research Association: Steam blast volcanic eruptions: A study of Mount Pelée in Martinique as type volcano

The investigation is concerned with the author's expedition to Martinique and St. Vincent in 1902 and comparison of the experience of investigators and sufferers with that of others in so-called "explosive" eruptions. The Hawaiian mechanism is reviewed with special reference to rifts, underground water, intrusion furnace, wedge rupture, and lowering of magma. These features of structure are applied to Martinique, St. Vincent, Kilauea, Tarawera, Sakurajima, Katmai, Taal and Tomboro as a series of steam blasts old and new. The comparison is found to be applicable and the analogy with Hawaii considered as fundamentally magmatic for gas and basaltic slag, brings out the contrast that lies in steam eruptions. For all volcanoes they are believed features of ground water and of collapse. Ground water stimulates lava eruptions. The Pelée disaster at St. Pierre May 8, 1902, followed by a dacite dome with spines, which renewed activity in 1929, is examined for paroxysms of downblast. These are distinguished sharply from the Carib migratory upblasts along valley fissures which are not uncommon elsewhere. The valleys are on rifts recognized as deep fumaroles. The Ghyben-Herzberg laws of ground water are applicable. Geyser rhythm was followed by Pelée, Soufriére of St. Vincent, and Kilauea in their sequence of paroxysms. Structure sections are drawn to scale, and the structural reactions of intrusion, rifts, boiler, gas effervescence, heat, and timing are thus outlined. The bearing of this machinery on volcanism in general, on world ignisepta and on reaction of magma is suggested. It is contended that steamblast is a climax of eruption in the water zone and should be sharply delimited from the rising and intrusion of fundamental earth magma, and from the high pressure water reactions of ocean bottoms. Rising magma is considered an age-long elevatory force along volcanic lines, modified by cyclical yielding. Compared with oceanic volcanism continental irruption in sediments is a separate science in experimental field geophysics. Every locality supramarine or submarine of warm ground and steep thermal gradient is a subject for volcanology, if pulsating ground water is critically, thermally and chemically measured. Authors are referred to herein by names and dates in parentheses, as listed in the appendices.

Martinique↗

USGS2022

An integrated approach for physical, economic, and demographic evaluation of coastal flood hazard adaptation in Santa Monica Bay, California

The increased risk of coastal flooding associated with climate-change driven sea level rise threatens to displace communities and cause substantial damage to infrastructure. Site-specific adaptation planning is necessary to mitigate the negative impacts of flooding on coastal residents and the built environment. Cost-benefit analyses used to evaluate coastal adaption strategies have traditionally focused on economic considerations, often overlooking potential demographic impacts that can directly influence vulnerability in coastal communities. Here, we present a transferable framework that couples hydrodynamic modeling of flooding driven by sea level rise and storm scenarios with site-specific building stock and census block-level demographic data. We assess the efficacy of multiple coastal adaptation strategies at reducing flooding, economic damages, and impacts to the local population. We apply this framework to evaluate a range of engineered, nature-based, and hybrid adaptation strategies for a portion of Santa Monica Bay, California. Overall, we find that dual approaches that provide protection along beaches using dunes or seawalls and along inlets using sluice gates perform best at reducing or eliminating flooding, damages, and population impacts. Adaptation strategies that include a sluice gate and partial or no protection along the beach are effective at reducing flooding around inlets but can exacerbate flooding elsewhere, leading to unintended impacts on residents. Our results also indicate trade-offs between economic and social risk-reduction priorities. The proposed framework allows for a comprehensive evaluation of coastal protection strategies across multiple objectives. Understanding how coastal adaptation strategies affect hydrodynamic, economic, and social factors at a local scale can enable more effective and equitable planning approaches.

California↗

USGS2013

Prediction, time variance, and classification of hydraulic response to recharge in two karst aquifers

Many karst aquifers are rapidly filled and depleted and therefore are likely to be susceptible to changes in short-term climate variability. Here we explore methods that could be applied to model site-specific hydraulic responses, with the intent of simulating these responses to different climate scenarios from high-resolution climate models. We compare hydraulic responses (spring flow, groundwater level, stream base flow, and cave drip) at several sites in two karst aquifers: the Edwards aquifer (Texas, USA) and the Madison aquifer (South Dakota, USA). A lumped-parameter model simulates nonlinear soil moisture changes for estimation of recharge, and a time-variant convolution model simulates the aquifer response to this recharge. Model fit to data is 2.4% better for calibration periods than for validation periods according to the Nash–Sutcliffe coefficient of efficiency, which ranges from 0.53 to 0.94 for validation periods. We use metrics that describe the shapes of the impulse-response functions (IRFs) obtained from convolution modeling to make comparisons in the distribution of response times among sites and between aquifers. Time-variant IRFs were applied to 62% of the sites. Principal component analysis (PCA) of metrics describing the shapes of the IRFs indicates three principal components that together account for 84% of the variability in IRF shape: the first is related to IRF skewness and temporal spread and accounts for 51% of the variability; the second and third largely are related to time-variant properties and together account for 33% of the variability. Sites with IRFs that dominantly comprise exponential curves are separated geographically from those dominantly comprising lognormal curves in both aquifers as a result of spatial heterogeneity. The use of multiple IRF metrics in PCA is a novel method to characterize, compare, and classify the way in which different sites and aquifers respond to recharge. As convolution models are developed for additional aquifers, they could contribute to an IRF database and a general classification system for karst aquifers.

South Dakota, Texas↗

USGS2022

National strategy for landslide loss reduction

Executive Summary Landslide hazards are present in all 50 States and most U.S. territories, and they affect lives, property, infrastructure, and the environment. Landslides are the downslope movement of earth materials under the force of gravity. They can occur without any obvious trigger. Widespread or severe landslide events are often driven by such hazards as hurricanes, earthquakes, volcanic eruptions, heavy rain events, flooding, and wildfires. Landslides can also cause their own cascading consequences, such as the spread of hazardous materials or the creation of devastating local tsunamis. This strategy document describes goals and strategic actions of a comprehensive strategy to meet key challenges to reducing the Nation’s risk from landslide hazards equitably and effectively. The document follows the direction of the National Landslide Preparedness Act (Public Law 116–323) by presenting a strategy for addressing landslide hazards, including risk reduction and response. The act directs the Department of the Interior to establish a program that will work with State, Tribal, and local governments as well as with academia, the private sector, community-based groups, and nonprofit organizations to identify landslide hazards and risk and improve communication, coordination, and emergency preparedness, with the objective of reducing landslide losses. As the only Federal program dedicated to landslide hazard science, the U.S. Geological Survey’s Landslide Hazards Program will lead and coordinate many of the efforts described in this strategy document. Landslide hazard risk reduction must be undertaken collectively and collaboratively across the Federal Government. This strategy document will provide a framework for the creation of an interagency management plan that describes the programs, projects, workforce, and budgets required to carry out the national strategy. The strategy outlined in this document presents a vision of how to equitably produce, communicate, and apply landslide data and science to support a broad range of land management, infrastructure, planning, and emergency response decisions. These decisions are made by a variety of actors, including private and nonprofit landholders; State, Tribal, territorial, city, and county planners; emergency managers; engineers; infrastructure managers; Federal agencies and their partners; and community leaders and individuals. Supporting those decisions and reducing the Nation’s vulnerability to landslides requires overcoming three main challenges: (1) gaps in basic information needed to describe and understand landslide occurrence and societal risk, (2) difficulty in accurately mapping and forecasting landslide hazards, and (3) communication and coordination among the many jurisdictions and sectors that have responsibility for and interest in reducing landslide losses. To address those challenges, this strategy document puts forward a series of strategic actions to achieve four goals: Assess: Decision makers have access to detailed, nationwide, and contextually relevant information on landslide hazard and risk. Coordinate: Landslide hazard mitigation, preparedness, response, and recovery efforts are coordinated across Federal, State, Tribal, territorial, and local levels. Plan: Communities and land managers are prepared and able to plan for landslide hazards. Respond: Landslide surveillance, warnings, and responses to events are effective, efficient, equitable, cooperative, and data-driven to protect lives, property, infrastructure, and the environment. These strategic actions focus on expanding the knowledge of societal risk posed by landslides as well as better understanding of where, when, and why they occur. They focus on applying that knowledge to support landslide risk reduction efforts and decisions, including the establishment of new advisory, coordination, and working groups focused on landslide hazard and risk. They take into account that supporting landslide loss reduction decisions also requires new guidance, tools, and training codeveloped with the entities, organizations, and individuals faced with making those decisions. Finally, they address actions needed to support and expand landslide warning information and improve the technical response to landslide emergencies.

Open-File Report↗