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USGS2002

Forecasting bacteria levels at bathing beaches in Ohio

The U.S. Geological Survey developed models for predicting exceedance of the bathing-water standard for Escherichia coli ( E. coli ) at three Lake Erie beaches and one inland lake in Ohio. The statistical models were speciﬁc to each beach, and the best model for each beach was based on a unique combination of environmental and water-quality variables as explanatory factors. For the Lake Erie beaches, these factors included wave height, number of birds on the beach at the time of sampling, lake-current direction, rainfall, turbidity, and streamﬂow of a nearby river. For the inland lake, these factors included date, wind direction and speed, number of birds, and rainfall. The prediction error in the models was too large to accurately estimate concentrations of E. coli ; however, the models can be used like weather forecasts to predict the probability, given a set of input variables, that the Ohio bathing-water standard used to judge swimming safety will be exceeded.

Ohio↗

USGS2018

Concentrations of nutrients at the water table beneath forage fields receiving seasonal applications of manure, Whatcom County, Washington, autumn 2011–spring 2015

The U.S. Geological Survey, in cooperation with the Whatcom Conservation District (WCD), collected groundwater-quality data for roughly 3 years (October 2011–May 2015) from near the water table beneath forage fields receiving regular seasonal applications of liquid dairy manure in Whatcom County, Washington. The work was done as part of an evaluation of WCD’s prototypical Application Risk Management (ARM) decision support system. The ARM system uses a combination of field-specific hydrology, stage of crop-growth, manure management practices, soil conditions, and precipitation forecast to evaluate the timing of manure application via a set of decision support tools (Manure Spreading Advisory, ARM Worksheet, manure application setback distances) in order to reduce the risk of contamination of surface water and groundwater. The ARM system’s effectiveness in reducing leaching of nitrate to groundwater was evaluated by monitoring nitrate concentrations in recently recharged groundwater beneath paired test plots receiving manure application scheduled using either conventional (CON) manure scheduling procedures, which utilize fixed start and end dates for manure application along with projected crop nutrient requirements or ARM manure scheduling procedures using an approach to manure application timing based on projected crop nutrient needs, field conditions, and weather forecast. Water-quality samples from the surface of the water table were collected synoptically from paired test plots (2–5 monitoring wells per test plot) at approximately monthly intervals at three different dairy field sites. Water-quality samples from near the water table were isolated from the underlying aquifer using a combination of an inflatable packer and a fine-grained sand pack encompassing the well-screen interval. Concentrations of nitrate and chloride measured at the water table beneath test plots were highly variable. Concentrations of nitrate ranged from non-detectable to 116 milligrams nitrogen per liter (mg-N/L), and chloride ranged from 1.15 to 153 mg/L. In each test plot, seasonal variations were much greater than spatial variations. Differences in nitrate concentrations in groundwater between the two treatments were inconclusive. Nitrate concentrations in groundwater at paired treatment plots (Mann Whitney, p<0.05) were significantly lower beneath the ARM treatment plot at site B, yet significantly higher beneath the ARM treatment plot at site C. Nitrate concentrations in ground water varied significantly among individual wells at each site (Kruskal-Wallis, p<0.05), indicating that leaching of nitrates from soil following manure application is spatially variable at the field scale tested regardless of manure application strategy. At all three paired test plots, average concentrations of nitrate and chloride at the water table were lowest near the end of the growing season (September) and increased rapidly with the onset of autumn rains (October–December). Under both the conventional (calendar-based) and treatment (ARM-based) manure application scheduling systems, high soil nitrate concentrations in autumn were coincident with rising groundwater levels, suggesting that nitrate and chloride were flushed from soil to groundwater by recharge from the seasonal rains. Under both treatments, concentrations of nitrate in shallow (10–25 feet) groundwater beneath forage fields receiving manure applications were greater than the nitrate drinking water standard of 10 mg-N/L in approximately 85 percent of samples. Yearly mass loading of nitrogen to the groundwater system calculated from nitrate concentrations at the water table and estimates of recharge volume ranged from 86 to 196 pounds-N per acre, which was equivalent to approximately 16–37 percent of the recommended manure application rate for projected forage production yield of 7 dry tons per acre per year. Manure nitrogen applied in the autumn, when crop nutrient needs decrease due to reduced sunlight and cooler temperatures and commensurate with ongoing mineralization of soil organic-nitrogen and increased seasonal precipitation, are more likely to exceed the immediate plant nutritional requirements and hence be flushed to groundwater than manure applications occurring near the peak of the growing season.

Washington↗

USGS2012

Solar thematic maps for space weather operations

Thematic maps are arrays of labels, or "themes", associated with discrete locations in space and time. Borrowing heavily from the terrestrial remote sensing discipline, a numerical technique based on Bayes' theorem captures operational expertise in the form of trained theme statistics, then uses this to automatically assign labels to solar image pixels. Ultimately, regular thematic maps of the solar corona will be generated from high-cadence, high-resolution SUVI images, the solar ultraviolet imager slated to fly on NOAA's next-generation GOES-R series of satellites starting ~2016. These thematic maps will not only provide quicker, more consistent synoptic views of the sun for space weather forecasters, but digital thematic pixel masks (e.g., coronal hole, active region, flare, etc.), necessary for a new generation of operational solar data products, will be generated. This paper presents the mathematical underpinnings of our thematic mapper, as well as some practical algorithmic considerations. Then, using images from the Solar Dynamics Observatory (SDO) Advanced Imaging Array (AIA) as test data, it presents results from validation experiments designed to ascertain the robustness of the technique with respect to differing expert opinions and changing solar conditions.

Space Weather↗

USGS2005

The seasonal cycle of diabatic heat storage in the Pacific Ocean

This study quantifies uncertainties in closing the seasonal cycle of diabatic heat storage (DHS) over the Pacific Ocean from 20??S to 60??N through the synthesis of World Ocean Circulation Experiment (WOCE) reanalysis products from 1993 to 1999. These products are DHS from Scripps Institution of Oceanography (SIO); near-surface geostrophic and Ekman currents from Earth and Space Research (ESR); and air-sea heat fluxes from Comprehensive Ocean-Atmosphere Data Set (COADS), National Centers for Environmental Prediction (NCEP), and European Center for Mid-Range Weather Forecasts (ECMWF). With these products, we compute residual heat budget components by differencing long-term monthly means from the long-term annual mean. This allows the seasonal cycle of the DHS tendency to be modeled. Everywhere latent heat flux residuals dominate sensible heat flux residuals, shortwave heat flux residuals dominate longwave heat flux residuals, and residual Ekman heat advection dominates residual geostrophic heat advection, with residual dissipation significant only in the Kuroshio-Oyashio current extension. The root-mean-square (RMS) of the differences between observed and model residual DHS tendencies (averaged over 10??latitude-by-20??longitude boxes) is <20 W m-2 in the interior ocean and <100 W m-2 in the Kuroshio-Oyashio current extension. This reveals that the residual DHS tendency is driven everywhere by some mix of residual latent heat flux, shortwave heat flux, and Ekman heat advection. Suppressing bias errors in residual air-sea turbulent heat fluxes and Ekman heat advection through minimization of the RMS differences reduces the latter to <10 W m-2 over the interior ocean and <25 W m -2 in the Kuroshio-Oyashio current extension. This reveals air-sea temperature and specific humidity differences from in situ surface marine weather observations to be a principal source of bias error, overestimated over most of ocean but underestimated near the Intertropical Convergence Zone. ?? 2005 Elsevier Ltd. All rights reserved.

Progress in Oceanography↗

USGS1995

Two-year simulation of the Great Lakes region with a coupled modeling system

In this paper, we report on an experiment aimed at evaluating the feasibility of the application of our coupled regional climate modeling system to long-term climate simulations over the Great Lakes region. The simulation analyzed covers a continuous 24-month period beginning 1 September 1990 and extending to 1 September 1992. Many aspects of this simulation agreed well with observations. Compared with European Centre for Medium-Range Weather Forecasts (ECMWF) analyses, area-averaged atmospheric temperature and moisture biases were generally small. The largest temperature biases were found in the simulated boundary layer, up to 1–1.5 K colder than observed in most months. Atmospheric moisture biases were of both signs and small in magnitude, almost universally less than 0.5 g kg −1 . Comparison of simulated surface air temperatures with station observations also indicated that model simulated temperatures generally display a cold bias. Simulated precipitation values agreed well with observations during the cold portions of the year while during warm months precipitation was overpredicted by 10%–50%. Spatial patterns of precipitation over the model domain agreed well with observations during the winter months but were not as well simulated during the other seasons. A one-dimensional lake model was coupled to the atmospheric component of the model to capture the effects of the Great Lakes on regional climate. Lake surface temperatures were generally well simulated by the lake model in the summer and fall seasons, and lake ice extent agreed well with the analysis over at least three of the five lakes. The greatest shortcomings in lake temperature simulation were the earlier-than-observed warm-up in the spring and warmer than observed peak temperatures in the summer over the northern portions of the lakes. Also, lake ice extent was generally overpredicted over Lake Superior and underpredicted over Lake Erie. In summary, the coupled modeling system described in this paper shows promise for use in climate simulators over regions where lakes are important such as the Great Lakes. It has been shown that many aspects of the simulation are in good agreement with available observations. Areas in which the results point to the need for further work are the model's convective parameterization, the eddy diffusivities in the lake model, and the treatment of clouds in the radiation package.

Illinois, Indiana, Michigan, Minnesota, New York, ↗

USGS1993

Toward the simulation of the effects of the Great Lakes on regional climate

This paper describes a set of numerical experiments aimed at evaluating the feasibility of applying a version of the National Center for Atmospheric Research-Pennsylvania State University regional model (MM4) to regional climate simulation over the Great Lakes Basin. The objectives of this initial modeling investigation are 1) to examine whether the MM4 can capture the primary forcing exerted by the Great Lakes on the regional climate and 2) to evaluate what model resolution and configuration are needed to simulate such forcing. Simulations over the Great Lakes region are conducted with and without representation of the lakes at four model gridpoint resolutions ranging from 15 to 90 km. One experiment at 60-km resolution is discussed in which a one-dimensional thermal eddy diffusion model is interactively coupled to the MM4 to represent the lakes. Initial and lateral boundary conditions necessary to drive these simulations are provided by European Centre for Medium-Range Weather Forecasts (ECMWF) analyses of observations. All simulations conducted are 10 days in length, from 22 December 1985 to 1 January 1986. When driven with data from ECMWF analyses of observations, the climate version of the MM4 reproduces the basic characteristics of the distribution of lake-effect precipitation over the Great Lakes Basin. Differences between simulations with and without the lakes represented indicate that the lakes accounted for approximately 25% of the precipitation over the basin during the 10-day period simulated. Over localized areas, identified as the major snowbelts downwind from the lakes, lake effects were responsible for 50%–70% of the precipitation. Basinwide precipitation did not vary greatly among the simulations with resolutions of 60, 30, and 15 km, although biases between model results and station observations did decrease slightly with increasing model resolution. Basinwide maximum and minimum temperature biases decreased more markedly with finer resolution. In the snowbelt regions downwind from the lakes, precipitation was underforecast at all four model resolutions, but precipitation generally increased with finer resolution. Differences between the results from the simulations at the three finest resolutions were greater over snowbelt regions than over the basin as a whole. A simulation was conducted with the MM4 coupled to a lake model in an interactive two-way nested configuration. The implementation of this coupling was accomplished in a straightforward manner, with no model tuning required, and added very little to the computation time needed for the MM4 system. This coupled modeling system was found to produce realistic distributions of lake surface temperatures, evaporation rates, and ice thicknesses across the lakes. In climate simulations where the MM4 is nested in a general circulation model (GCM), we believe that the use of this coupled modeling system is preferable to specifying lake parameters by interpolation from GCM output. The next step in this work is to conduct a simulation of at least one annual cycle over the region to more fully test the coupled MM4-take model system.

Great Lakes region↗

USGS2019

Temporally adaptive acoustic sampling to maximize detection across a suite of focal wildlife species

Acoustic recordings of the environment can produce species presence–absence data for characterizing populations of sound-producing wildlife over multiple spatial scales. If a species is present at a site but does not vocalize during a scheduled audio recording survey, researchers may incorrectly conclude that the species is absent (“false negative”). The risk of false negatives is compounded when audio devices have sampling constraints, do not record continuously, and must be manually scheduled to operate at pre-selected times of day, particularly when research programs target multiple species with acoustic availability that varies across temporal conditions. We developed a temporally adaptive acoustic sampling algorithm to maximize detection probabilities for a suite of focal species amid sampling constraints. The algorithm combines user-supplied species vocalization models with site-specific weather forecasts to set an optimized sampling schedule for the following day. To test our algorithm, we simulated hourly vocalization probabilities for a suite of focal species in a hypothetical monitoring area for the year 2016. We conducted a factorial experiment that sampled from the 2016 acoustic environment to compare the probability of acoustic detection by a fixed (stationary) schedule versus a temporally adaptive optimized schedule under several sampling efforts and monitoring durations. We found that over the course of a study season, the probability of acoustically capturing a focal species (given presence) at least once via automated acoustic monitoring was greater (and acoustic capture occurred earlier in the season) when using the temporally adaptive optimized schedule as compared to a fixed schedule. The advantages of a temporally adaptive optimized acoustic sampling schedule are magnified when a study duration is short, sampling effort is low, and/or species acoustic availability is minimal. This methodology presents the opportunity to maximize acoustic monitoring sampling efforts amid constraints.

Colorado↗

USGS2009

The November 15, 2006 Kuril Islands-generated tsunami in Crescent City, California

On November 15, 2006, Crescent City in Del Norte County, California was hit by a tsunami generated by a M w 8.3 earthquake in the central Kuril Islands. Strong currents that persisted over an eight-hour period damaged floating docks and several boats and caused an estimated $9.2 million in losses. Initial tsunami alert bulletins issued by the West Coast Alaska Tsunami Warning Center (WCATWC) in Palmer, Alaska were cancelled about three and a half hours after the earthquake, nearly five hours before the first surges reached Crescent City. The largest amplitude wave, 1.76-meter peak to trough, was the sixth cycle and arrived over two hours after the first wave. Strong currents estimated at over 10 knots, damaged or destroyed three docks and caused cracks in most of the remaining docks. As a result of the November 15 event, WCATWC changed the definition of Advisory from a region-wide alert bulletin meaning that a potential tsunami is 6 hours or further away to a localized alert that tsunami water heights may approach warning- level thresholds in specific, vulnerable locations like Crescent City. On January 13, 2007 a similar Kuril event occurred and hourly conferences between the warning center and regional weather forecasts were held with a considerable improvement in the flow of information to local coastal jurisdictions. The event highlighted the vulnerability of harbors from a relatively modest tsunami and underscored the need to improve public education regarding the duration of the tsunami hazards, improve dialog between tsunami warning centers and local jurisdictions, and better understand the currents produced by tsunamis in harbors.

California↗

USGS2019

Reducing wet ammonium deposition in Rocky Mountain National Park: The development and evaluation of a pilot early warning system for agricultural operations in eastern Colorado

Agricultural emissions are the primary source of ammonia (NH3) deposition in Rocky Mountain National Park (RMNP), a Class I area, that is granted special air quality protections under the Clean Air Act. Between 2014 and 2016, the pilot phase of the Colorado agricultural nitrogen early warning system (CANEWS) was developed for agricultural producers to voluntarily and temporarily minimize emissions of NH3 during periods of upslope winds. The CANEWS was created using trajectory analyses driven by outputs from an ensemble of numerical weather forecasts together with the climatological expertise of human forecasters. Here, we discuss the methods for the CANEWS and offer preliminary analyses of 33 months of the CANEWS based on atmospheric deposition data from two sites in RMNP as well as responses from agricultural producers after warnings were issued. Results showed that the CANEWS accurately predicted 6 of 9 high N deposition weeks at a lower-elevation observation site, but only 4 of 11 high N deposition weeks at a higher-elevation site. Sixty agricultural producers from 39 of Colorado’s agricultural operations volunteered for the CANEWS, and a two-way line of communication between agricultural producers and scientists was formed. For each warning issued, an average of 23 producers responded to a post-warning survey. Over 75% of responding CANEWS participants altered their practices after an alert. While the current effort was insufficient to reduce atmospheric deposition, we were encouraged by the collaborative spirit between agricultural, scientific, and resource management communities. Solving a broad and complex social-ecological problem requires both a technological approach, such as the CANEWS, and collaboration and trust from all participants, including agricultural producers, land managers, university researchers, and environmental agencies.

Colorado↗

USGS2013

Indo-Pacific sea surface temperature influences on failed consecutive rainy seasons over eastern Africa

Rainfall over eastern Africa (10°S–10°N; 35°E–50°E) is bimodal, with seasonal maxima during the "long rains" of March–April–May (MAM) and the "short rains" of October–November–December (OND). Below average precipitation during consecutive long and short rains seasons over eastern Africa can have devastating long-term impacts on water availability and agriculture. Here, we examine the forcing of drought during consecutive long and short rains seasons over eastern Africa by Indo-Pacific sea surface temperatures (SSTs). The forcing of eastern Africa precipitation and circulation by SSTs is tested using ten ensemble simulations of a global weather forecast model forced by 1950–2010 observed global SSTs. Since the 1980s, Indo-Pacific SSTs have forced more frequent droughts spanning consecutive long and short rains seasons over eastern Africa. The increased frequency of dry conditions is linked to warming SSTs over the Indo-west Pacific and to a lesser degree to Pacific Decadal Variability. During MAM, long-term warming of tropical west Pacific SSTs from 1950–2010 has forced statistically significant precipitation reductions over eastern Africa. The warming west Pacific SSTs have forced changes in the regional lower tropospheric circulation by weakening the Somali Jet, which has reduced moisture and rainfall over the Horn of Africa. During OND, reductions in precipitation over recent decades are oftentimes overshadowed by strong year-to-year precipitation variability forced by the Indian Ocean Dipole and the El Niño–Southern Oscillation.

Climate Dynamics↗

USGS2020

Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set

We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.

Scientific Data↗

USGS2013

Overview of intercalibration of satellite instruments

Inter-calibration of satellite instruments is critical for detection and quantification of changes in the Earth’s environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be inter-operable, the instruments must be cross-calibrated. To meet the stringent needs of such applications requires that instruments provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités (SI) traceable Calibration and Validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Inter-calibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Inter-calibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated inter-calibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth’s climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of post-launch radiometric calibration of remote sensing satellite instruments, through inter-calibration.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2023

Toward probabilistic post-fire debris-flow hazard decision support

Post-wildfire debris flows (PFDF) threaten life and property in western North America. They are triggered by short-duration, high-intensity rainfall. Following a wildfire, rainfall thresholds are developed that, if exceeded, indicate high likelihood of a PFDF. Existing weather forecast products allow forecasters to identify favorable atmospheric conditions for rainfall intensities that may exceed established thresholds at lead times needed for decision-making (e.g., ≥24 h). However, at these lead times, considerable uncertainty exists regarding rainfall intensity and whether the high-intensity rainfall will intersect the burn area. The approach of messaging on potential hazards given favorable conditions is generally effective in avoiding unanticipated PFDF impacts, but may lead to “messaging fatigue” if favorable triggering conditions are forecast numerous times, yet no PFDF occurs (i.e., false alarm). Forecasters and emergency managers need additional tools that increase their confidence regarding occurrence of short-duration, high-intensity rainfall as well as tools that tie rainfall forecasts to potential PFDF outcomes. We present a concept for probabilistic tools that evaluate PFDF hazards by coupling a high-resolution (1-km), large (100-member) ensemble 24-h precipitation forecast at 5-min resolution with PFDF likelihood and volume models. The observed 15-min maximum rainfall intensities are captured within the ensemble spread, though in highest ∼10% of members. We visualize the model output in several ways to demonstrate most likely and most extreme outcomes and to characterize uncertainty. Our experiment highlights the benefits and limitations of this approach, and provides an initial step toward further developing situational awareness and impact-based decision-support tools for forecasting PFDF hazards.

California↗

USGS2019

Regional Global Navigation Satellite System networks for crustal deformation monitoring

Regional networks of Global Navigation Satellite System (GNSS) stations cover seismically and volcanically active areas throughout the United States. Data from these networks have been used to produce high‐precision, three‐component velocity fields covering broad geographic regions as well as position time series that track time‐varying crustal deformation. This information has contributed to assessing interseismic strain accumulation and related seismic hazard, revealed previously unknown occurrences of aseismic fault slip, constrained coseismic slip estimates, and enabled monitoring of volcanic unrest and postseismic deformation. In addition, real‐time GNSS data are now widely available. Such observations proved invaluable for tracking the rapidly evolving eruption of Kīlauea in 2018. Real‐time earthquake source modeling using GNSS data is being incorporated into tsunami warning systems, and a vigorous research effort is focused on quantifying the contribution that real‐time GNSS can make to improve earthquake early warnings as part of the Advanced National Seismic System ShakeAlert system. Real‐time GNSS data can also aid in the tracking of ionospheric disturbances and precipitable water vapor for weather forecasting. Although regional GNSS and seismic networks generally have been established independently, their spatial footprints often overlap, and in some cases the same institution operates both types of networks. Further integration of GNSS and seismic networks would promote joint use of the two data types to better characterize earthquake sources and ground motion as well as offer opportunities for more efficient network operations. Looking ahead, upgrading network stations to leverage new GNSS technology could enable more precise positioning and robust real‐time operations. New computational approaches such as machine learning have the potential to enable full utilization of the large amounts of data generated by continuous GNSS networks. Development of seafloor Global Positioning System‐acoustic networks would provide unique information for fundamental and applied research on subduction zone seismic hazard and, potentially, monitoring.

Seismological Research Letters↗

USGS2007

Analysis of continuous GPS measurements from southern Victoria Land, Antarctica

Several years of continuous data have been collected at remote bedrock Global Positioning System (GPS) sites in southern Victoria Land, Antarctica. Annual to sub-annual variations are observed in the position time-series. An atmospheric pressure loading (APL) effect is calculated from pressure field anomalies supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) model loading an elastic Earth model. The predicted APL signal has a moderate correlation with the vertical position time-series at McMurdo, Ross Island (International Global Navigation Satellite System Service (IGS) station MCM4), produced using a global solution. In contrast, a local solution in which MCM4 is the fiducial site generates a vertical time series for a remote site in Victoria Land (Cape Roberts, ROB4) which exhibits a low, inverse correlation with the predicted atmospheric pressure loading signal. If, in the future, known and well modeled geophysical loads can be separated from the time-series, then local hydrological loading, of interest for glaciological and climate applications, can potentially be extracted from the GPS time-series.

Open-File Report↗

USGS2008

Amount and Percentage of Current Societal Assets in Areas on Kaua'i, Hawai'i, within the 1992 Hurricane 'Iniki Storm-Surge Inundation Zone

The Pacific Risk Management 'Ohana (PRiMO) is a network of partners and stakeholders involved in the development, delivery, and communication of risk management-related information, products, and services across the Pacific Ocean (National Oceanic and Atmospheric Administration Pacific Services Center, 2008). One PRiMO-related project is the NOAA National Climatic Data Center's Integrated Data and Environmental Applications (IDEA) Center's Pacific Region Integrated Climatology Information Products (PRICIP) initiative, which seeks to improve the understanding of patterns and trends of storm frequency and intensity ('storminess') within the Pacific region and to develop a suite of integrated information products that can be used by emergency managers, mitigation planners, government agencies, and other decision-makers (National Oceanic and Atmospheric Administration Integrated Data and Environmental Applications Center, 2008a). One of the PRICIP information products is a historical storm 'event anatomy', which includes a summary of sector-specific socioeconomic impacts associated with a particular event, as well as information about the event and its climatological context. The intent of an event anatomy is to convey the causes of an extreme storm event and the associated impacts in a format that users can understand. The event anatomies also are intended to familiarize users with the in-place and remotely sensed products typically employed to track and forecast weather and climate. The first event anatomy developed as a prototype and hosted on the PRICIP portal is for Hurricane 'Iniki (National Oceanic and Atmospheric Administration Integrated Data and Environmental Applications Center, 2008b), a Category 3-4 hurricane that made landfall on the south coast of Kaua'i Island on September 11, 1992, with estimated maximum sustained winds of more than 140 mph and gusts as high as 175 mph. Storm-surge inundation occurred on the southern and northeastern coast of Kaua'i Island. In an effort to increase the amount of available information on the Hurricane 'Iniki event anatomy of the PRICIP portal, representatives from the NOAA NCDC IDEA Center contacted the U.S. Geological Survey (USGS) in 2007 for assistance in determining what current societal assets are in areas that were inundated by storm surge during Hurricane 'Iniki in 1992. This report contains data summarizing the amount and percentage of current societal assets on Kaua'i Island, Hawai'i, that exist in the historic Hurricane 'Iniki storm-surge inundation zone. Coupled with an array of information on the socioeconomic impacts of Hurricane 'Iniki and distributed through the PRICIP portal, the results of this effort will help managers and the general public to understand the current risks posed by extreme storms in the Pacific Basin.

Open-File Report↗

USGS2020

A probabilistic assessment of tephra-fall hazards at Hanford, Washington, from a future eruption of Mount St. Helens

Hanford, Washington (USA) is the construction site of a multi-billion-dollar high-level nuclear waste treatment facility. This site lies 200 kilometers (km) east of Mount St. Helens (MSH), the most active volcano in the contiguous United States. Tephra from a future MSH eruption could pose a hazard to the air intake and filtration systems at this plant. In this report, we present a probabilistic estimate of the amount of tephra that could fall, and the concentrations of airborne ash that could occur at the Hanford Site during a future eruption. Mount St. Helens has produced four large explosive eruptions in approximately the past 500 years, suggesting that its annual probability of eruption ( P 1 ) is roughly 4/500=0.008. Assuming that a large eruption occurs, we calculate the probability ( P 3|1 ) of a given fall deposit thickness or airborne concentration at Hanford by running about 10,000 simulations of ash-producing eruptions using the atmospheric transport model Ash3d. In each simulation, we calculate the pattern of tephra dispersal, deposit thickness at Hanford, and airborne ash concentration at ground level. As input for each simulation, we choose meteorological conditions from a randomly chosen time in the historical record between 1980 and 2010, using data from the European Centre for Medium-Range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim model. The volume (dense-rock equivalent) of each simulated eruption is randomly chosen from a uniform probability distribution on a log scale from the range of magma volumes (0.008–2.3 cubic kilometers [km 3 ]) estimated for late Holocene eruptions at MSH. Plume heights and durations of each eruption are chosen using empirical correlations between volume, height, and eruption rate, which account for the fact that larger eruptions have higher plumes and last longer. We construct summary tables of final deposit thickness ( T ), maximum ground-level airborne concentration ( C max ), and average ground-level airborne concentration ( C avg ) during tephra-fall for each run. Each table is sorted and ranked by decreasing value of T , C max , or C avg . Conditional probabilities ( P 3|1 ) are derived by dividing rank by n+1, where n is the total number of successful runs. For example, a deposit thickness of 5.10 centimeters (cm) from run 446 is ranked 123 of 9,785 successful runs, yielding P 3|1 =123/9,786=0.01257. Its annual probability is P = P 1 · P 3|1 =0.008×0.01257=0.000101. By interpolation, the deposit thickness ( T 10k ) having an annual probability of 1 in 10,000 ( P = 0.0001) is 5.11 cm. Analogous concentration values are C max,10k =3,819 and C avg,10k =1,513 milligrams per cubic meter (mg/m 3 ), respectively. Independent calculations using the known mass accumulation rate of the deposit (=0.001–0.006 kilograms per square meter per second [kg/m 2 /s]), aggregate fall velocities ( u =0.3–0.8 meters per second [m/s]), and the simple formula , yield similar results, although highly variable fall velocities add significant uncertainty. This formula implies that deposit accumulation rates of millimeters (mm) to greater than 1 cm per hour, which are not uncommon during heavy ash fall, are associated with airborne concentrations of 10 2 –10 3 milligrams per cubic meter (mg/m 3 ). These concentrations are much higher than published measurements (10 -3 –10 1 mg/m 3 ), which record only suspended particles sampled in sheltered areas. During heavy ashfall, most fine ash falls as aggregates. Whether such aggregates will be ingested into air ducts will depend on the aggregate size and fall rate, the fragility of the aggregates, the air duct geometry, intake velocity, and other factors.

Washington↗

USGS2024

Database and time series of nearshore waves along the Alaskan coast from the United States-Canada border to the Bering Sea

Alaska’s Arctic coast has some of the highest coastal erosion rates in the world, primarily driven by permafrost thaw and increasing wave energy. In the Arctic, a warming climate is driving sea ice cover to decrease in space and time. A lack of long-term observational wave data along Alaska’s coast challenges the ability of engineers, scientists, and planners to study and address threats and effects from wave-driven erosion and flooding. To overcome the lack of available observational wave data in the nearshore in this study by the U.S. Geological Survey, waves were downscaled with the Simulating WAves Nearshore numerical wave model (SWAN) for the hindcast period of 1979 to 2019 from the United States-Canada border to the Bering Sea utilizing nine model domains. For each domain, the model was forced at the open boundary with 2,500 representative “sea states,” which are likely combinations of significant wave heights, mean wave periods, mean wave directions, and wind speeds and directions. The sea states were obtained from the European Centre for Medium-Range Weather Forecasts “ERA5” dataset for reanalysis of winds and waves using a multivariant maximum-dissimilarity algorithm. The SWAN runs created a downscaled wave database at each grid point, which was used to reconstruct the 40-year time series in the nearshore along the 5- and 10-meter isobaths at locations approximately 400 m apart and corresponding to transects spaced approximately 50 m alongshore, as developed for USGS shoreline-change assessments. Reconstructed time series were compared to observations to validate the numerical model and the downscaled wave database method and showed overall good agreements.

Alaska↗