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At least 91 records · Page 5Linked to original sources
USGS2019

Event detection performance of the PLUM earthquake early warning algorithm in southern California

We test the Japanese ground‐motion‐based earthquake early warning (EEW) algorithm, propagation of local undamped motion (PLUM), in southern California with application to the U.S. ShakeAlert system. In late 2018, ShakeAlert began limited public alerting in Los Angeles to areas of expected modified Mercalli intensity (⁠IMMI⁠) 4.0+ for magnitude 5.0+ earthquakes. Most EEW systems, including ShakeAlert, use source‐based methods: they estimate the location, magnitude, and origin time of an earthquake from P waves and use a ground‐motion prediction equation to identify regions of expected strong shaking. The PLUM algorithm uses observed ground motions directly to define alert areas and was developed to address deficiencies in the Japan Meteorological Agency source‐based EEW system during the 2011 Mw 9.0 Tohoku earthquake sequence. We assess PLUM using (a) a dataset of 193 magnitude 3.5+ earthquakes that occurred in southern California between 2012 and 2017 and (b) the ShakeAlert testing and certification suite of 49 earthquakes and other seismic signals. The latter suite includes events that challenge the current ShakeAlert algorithms. We provide a first‐order performance assessment using event‐based metrics similar to those used by ShakeAlert. We find that PLUM can be configured to successfully issue alerts using IMMI trigger thresholds that are lower than those implemented in Japan. Using two stations, a trigger threshold of IMMI 4.0 for the first station and a threshold of IMMI 2.5 for the second station PLUM successfully detect 12 of 13 magnitude 5.0+ earthquakes and issue no false alerts. PLUM alert latencies were similar to and in some cases faster than source‐based algorithms, reducing area that receives no warning near the source that generally have the highest ground motions. PLUM is a simple, independent seismic method that may complement existing source‐based algorithms in EEW systems, including the ShakeAlert system, even when alerting to light (⁠IMMI 4.0) or higher ground‐motion levels.

California↗
USGS2022

Working toward a National Coordinated Soil Moisture Monitoring Network: Vision, progress, and future directions

Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

Bulletin of the American Meteorological Society↗
USGS2006

Analysis of the surface hydrology in a regional climate model

This paper discusses the surface hydrology of a multi‐year simulation of present day climate over the United States (US) conducted with a regional climate model (RegCM) nested within a general circulation model (GCM). The RegCM, which is run with a 60 km gridpoint spacing is interactively coupled with a state‐of‐the‐art surface physics package that includes full surface hydrology calculations (the Biosphere‐Atmosphere Transfer Scheme or BATS). The hydrologic budgets of ten regional drainage basins in the US are analysed. Model results are compared with available observations and with results from previous modelling experiments to evaluate the feasibility of using nested RegCM/GCM models for hydrology studies. In our experiment, the model captures the basic seasonality of the basin hydrologic budgets, although the simulated precipitation amounts are too high over the western US and too low over the eastern US. As a result, runoff, snow cover and soil water content are underestimated over the eastern US basins, while evaporation and runoff are overestimated in some of the western US basins. Topographically induced characteristics of precipitation, snow cover and runoff are well simulated over the mountainous western regions. Also well captured is the inter‐basin variation of hydrologic budgets which occurs in response to different climatic settings. The springtime snowmelt and peak runoff season generally occurs in the model earlier in the year than is observed. Although our work indicates that the coupled regional modelling system can be useful in applications to hydrological studies, results from this experiment indicate that better accuracy in the simulation of regional climatic variables and more detailed representation of some hydrologic processes would be required before the coupled modelling system could be used to provide accurate assessments of hydrologic responses to climate change.

Quarterly Journal of the Royal Meteorological Soci↗
USGS2013

A volcanic activity alert-level system for aviation: Review of its development and application in Alaska

An alert-level system for communicating volcano hazard information to the aviation industry was devised by the Alaska Volcano Observatory (AVO) during the 1989–1990 eruption of Redoubt Volcano. The system uses a simple, color-coded ranking that focuses on volcanic ash emissions: Green—normal background; Yellow—signs of unrest; Orange—precursory unrest or minor ash eruption; Red—major ash eruption imminent or underway. The color code has been successfully applied on a regional scale in Alaska for a sustained period. During 2002–2011, elevated color codes were assigned by AVO to 13 volcanoes, eight of which erupted; for that decade, one or more Alaskan volcanoes were at Yellow on 67 % of days and at Orange or Red on 12 % of days. As evidence of its utility, the color code system is integrated into procedures of agencies responsible for air-traffic management and aviation meteorology in Alaska. Furthermore, it is endorsed as a key part of globally coordinated protocols established by the International Civil Aviation Organization to provide warnings of ash hazards to aviation worldwide. The color code and accompanying structured message (called a Volcano Observatory Notice for Aviation) comprise an effective early-warning message system according to the United Nations International Strategy for Disaster Reduction. The aviation color code system currently is used in the United States, Russia, New Zealand, Iceland, and partially in the Philippines, Papua New Guinea, and Indonesia. Although there are some barriers to implementation, with continued education and outreach to Volcano Observatories worldwide, greater use of the aviation color code system is achievable.

Alaska↗
USGS2026

Predicting streamflow drought in the conterminous United States using machine learning and a donor-gage approach, 1982–2020

Drought is a highly consequential natural disaster that may likely increase in both severity and extent across the conterminous United States (CONUS). The mechanisms affecting the propagation of drought from the atmosphere to streamflow are complex and interactive, making the prediction of streamflow drought difficult with current modeling approaches. Machine learning is an emerging tool in the field of hydrology that may be well-suited to prediction of streamflow drought across large and topographically diverse areas. Here, we train and analyze 3198 random forest models at U.S. Geological Survey streamgages to understand common meteorological drivers of streamflow drought and to define physiographic characteristics of basins sensitive to these drivers. We also develop a novel dynamic regionalization approach using donor gages to predict daily streamflow drought at pseudo-ungaged locations. Our results show that teleconnections, temperature, evaporative demand, and snow–water equivalent are important drivers of streamflow drought in the West, Southwest, and Northern Rocky Mountains (Northern Rockies) regions of the United States, and precipitation and soil moisture are primary drivers of streamflow drought in the Northeast, Southeast, and the Northwest regions. Prediction using dynamic regionalization shows comparable performance to at-site models. This method may be applicable to other hydrologic prediction problems requiring transferability to ungaged locations.

conterminous United States↗
USGS1998

Regional characterization of land cover using multiple sources of data

Many organizations require accurate intermediate-scale land-cover information for many applications, including modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land-use planning, and policy development. While many techniques have been successfully used to classify land cover in relatively small regions, there are substantial obstacles in applying these methods to large, multiscene regions. The purpose of this study was to generate and evaluate a large region land-cover classification product using a multiple-layer land-characteristics database approach. To derive land-cover information, mosaicked Landsat thematic mapper (TM) scenes were analyzed in conjunction with digital elevation data (and derived slope, aspect, and shaded relief), population census information, Defense Meteorological Satellite Program city lights data, prior land-use and land-cover data, digital line graph data, and National Wetlands Inventory data. Both leaf-on and leaf-off TM data sets were analyzed. The study area was U.S. Federal Region III, which includes the states of Pennsylvania, Virginia, Maryland, Delaware, and West Virginia. The general procedure involved (1) generating mosaics of multiple scenes of leaves-on TM data using histogram equalization methods; (2) clustering mosaics into 100 spectral classes using unsupervised classification; (3) interpreting and labeling spectral classes into approximately 15 land-cover categories (analogous to Anderson Level 1 and 2 classes) using aerial photographs; (4) developing decision-making rules and models using from one to several ancillary data layers to resolve confusion in spectral classes that represented two or more targeted land-cover categories; and (5) incorporating data from other sources (for example, leaf-off TM data and National Wetlands Inventory data) to yield a final land-cover product. Although standard accuracy assessments were not done, a series of consistency checks using available sources of land-cover information were conducted to evaluate the effectiveness of this approach for generating accurate land-cover information for large regions.

Photogrammetric Engineering and Remote Sensing↗
USGS2022

A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan

From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56 ∘ N, 30–100 ∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).

Earth System Science Data↗
USGS1996

Comparison of alternative spatial resolutions in the application of a spatially distributed biogeochemical model over complex terrain

Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the study of the potential impacts of climate change.

Ecological Modelling↗
USGS2011

The Forecast Interpretation Tool—a Monte Carlo technique for blending climatic distributions with probabilistic forecasts

Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique.

International Journal of Climatology↗
USGS2008

Using climate information for fuels management

Climate has come to the forefront of wildfire discussions in recent years as research contributes to the general understanding of how climate influences fuels availability to burn, the occurrence of severe fire weather conditions and other wildfire parameters. This understanding has crossed over into wildfire management applications through the creation of tools like climate forecasts for wildfire and drought indices, which are now widely used in wildfire suppression and mitigation planning. The overall question is how can climate information help fire managers meet management objectives? Climate underlies weather. For example, a number of days could be generally wet, but that may occur in the context of a two-year overall drought. Knowing the baseline climate is not only critical to preventing escaped prescribed fires, but also how it may affect fire behavior, fire effects and whether or not fire managers will meet their fuels management objectives. Thus, for fire managers to use prescribed and WFU fire safely and effectively, and to minimize the number of escaped fires and conversions to suppression, they need to understand how current climate conditions will impact the use of fire. One example is the need to use prescribed fire under set “burn windows”. Since meteorological conditions vary considerably from year to year for a given day, fire managers will be more successful in utilizing burn windows effectively if they understand those climate thresholds conducive to an increased number of safe burn windows, and are able to predict and take advantage of those burn windows. While climate and wildfire has been studied extensively, climate and fire use has not. The initial goal of this project was to assess how climate impacts prescribed fire use in a more general sense. After a preliminary informal survey in the spring of 2003, we determined that 1) there is insufficient data (less than 10 years) to conduct empirical correlative studies similar to those of the relationships between climate and wildfire (e.g., Swetnam and Betancourt 1990), and 2) prescribed fire policy has many regulations that potentially inhibited the use of climate information for decision-making. It was also determined that because fire use is a human decision, it would be more informative to ask fire managers themselves how climate impacts fire use through their decision-making processes, and whether or not they use climate information for prescribed fire. The first task for this project was to complete a regional survey of prescribed fire managers in California and Nevada. During the second phase of the project, additional prescribed fire managers were surveyed across the country. During the third year a second survey of WFU managers was completed. The goals of these inquiries were to determine: 1) If fire managers use climate information for fuels management; 2) The perspective fire managers have towards climate affecting fuels management; 3) Determine any obstacles that make it difficult to use climate information for fuels management; and 4) Determine climate information managers need to help them make better decisions for fire use.

Climate Ecosystem Fire Applications↗
USGS2021

HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.

Water↗
USGS2009

Investigating the impact of surface wave breaking on modeling the trajectories of drifters in the northern Adriatic Sea during a wind-storm event

An accurate numerical prediction of the oceanic upper layer velocity is a demanding requirement for many applications at sea and is a function of several near-surface processes that need to be incorporated in a numerical model. Among them, we assess the effects of vertical resolution, different vertical mixing parameterization (the so-called Generic Length Scale -GLS- set of k-??, k-??, gen, and the Mellor-Yamada), and surface roughness values on turbulent kinetic energy (k) injection from breaking waves. First, we modified the GLS turbulence closure formulation in the Regional Ocean Modeling System (ROMS) to incorporate the surface flux of turbulent kinetic energy due to wave breaking. Then, we applied the model to idealized test cases, exploring the sensitivity to the above mentioned factors. Last, the model was applied to a realistic situation in the Adriatic Sea driven by numerical meteorological forcings and river discharges. In this case, numerical drifters were released during an intense episode of Bora winds that occurred in mid-February 2003, and their trajectories compared to the displacement of satellite-tracked drifters deployed during the ADRIA02-03 sea-truth campaign. Results indicted that the inclusion of the wave breaking process helps improve the accuracy of the numerical simulations, subject to an increase in the typical value of the surface roughness z 0 . Specifically, the best performance was obtained using ?? CH = 56,000 in the Charnok formula, the wave breaking parameterization activated, k-?? as the turbulence closure model. With these options, the relative error with respect to the average distance of the drifter was about 25% (5.5 km/day). The most sensitive factors in the model were found to be the value of ?? CH enhanced with respect to a standard value, followed by the adoption of wave breaking parameterization and the particular turbulence closure model selected. ?? 2009 Elsevier Ltd.

Ocean Modelling↗
USGS2023

A global long-term daily reanalysis of reference evapotranspiration for drought and food-security monitoring

NOAA has developed a global reference evapotranspiration (ET 0 ) reanalysis using the UN Food and Agriculture Organization formulation (FAO-56) of the Penman-Monteith equation forced by MERRA phase 2 (MERRA2) meteorological and radiative drivers. The NOAA ET 0 reanalysis is provided daily from January 1, 1980 to the near-present at a resolution of 0.5° latitude × 0.625° longitude. The reanalysis is verified against station data across southern Africa, a region presenting both significant challenges regarding hydroclimatic variability and observational quantity and quality and significant potential benefits to food-insecure populations. These data are generated from observations from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) network. We further verified globally against spatially distributed ET 0 derived from two reanalyses–the Global Data Assimilation System (GDAS) and Princeton Global Forcing (PGF)–and these verifications produced similar results, yet demonstrated wide regional and seasonal differences. We also present cases that verify the operational applicability of the reanalysis in long-established drought, famine, crop- and pastoral-stress metrics, and in predictability assessments of drought forecasts.

Nature Scientific Data↗
USGS2024

A spatiotemporal optimization engine for prescribed burning in the Southeast US

Many ecosystems in the Southeast US are dependent upon frequent low-intensity surface fires to sustain native biodiversity, ecosystem services, and endangered species populations. Today, landscape-scale prescribed fire is required to manage these systems for conservation objectives and to mitigate wildland fire risk. Successful application of prescribed fire in this region requires careful planning and assessment of the risks and tradeoffs involved when deciding whether or not to conduct a burn. Many of these risks are closely tied to ambient environmental conditions and are reflected in sets of ‘prescription’ parameters that define safe and effective operating conditions to meet objectives or regulatory requirements. To facilitate effective decision making and acknowledge growing uncertainties related to climate change effects on wildland fire operations, we developed a spatiotemporal optimization engine to identify near-term optimal burning opportunities for prescribed fire implementation. By mining historical 3-day numerical weather forecasts and observation-based weather data for 2015–2021, we have developed a Bayesian hierarchical model for forecast verification that provides calibrated daily weather forecasts and joint uncertainty estimates on meteorological variables of interest, with the latter serving as a measure of risk associated with prescribed fire activities. Burn allocation decisions are then optimized by considering this risk jointly with the utility of burning a particular habitat parcel. The initial iteration of the optimization engine is demonstrated through a case study of short-term meteorological conditions for the Eglin Air Force Base, located in Florida, USA. Results indicate agreement between the optimization engine and the observed past decision-making, with the largest divergences likely arising primarily from differences between utility functions presumed important and used to develop the optimization engine versus the true utility functions driving management behavior in practice.

Florida↗
USGS2014

Quantifying the residence time and flushing characteristics of a shallow, back-barrier estuary: Application of hydrodynamic and particle tracking models

Estuarine residence time is a major driver of eutrophication and water quality. Barnegat Bay-Little Egg Harbor (BB-LEH), New Jersey, is a lagoonal back-barrier estuary that is subject to anthropogenic pressures including nutrient loading, eutrophication, and subsequent declines in water quality. A combination of hydrodynamic and particle tracking modeling was used to identify the mechanisms controlling flushing, residence time, and spatial variability of particle retention. The models demonstrated a pronounced northward subtidal flow from Little Egg Inlet in the south to Pt. Pleasant Canal in the north due to frictional effects in the inlets, leading to better flushing of the southern half of the estuary and particle retention in the northern estuary. Mean residence time for BB-LEH was 13 days but spatial variability was between ∼0 and 30 days depending on the initial particle location. Mean residence time with tidal forcing alone was 24 days (spatial variability between ∼0 and 50 days); the tides were relatively inefficient in flushing the northern end of the Bay. Scenarios with successive exclusion of physical processes from the models revealed that meteorological and remote offshore forcing were stronger drivers of exchange than riverine inflow. Investigations of water quality and eutrophication should take into account spatial variability in hydrodynamics and residence time in order to better quantify the roles of nutrient loading, production, and flushing.

Estuaries and Coasts↗
USGS1978

Time-dependence of sea-ice concentration and multiyear ice fraction in the Arctic Basin

The time variation of the sea-ice concentration and multiyear ice fraction within the pack ice in the Arctic Basin is examined, using microwave images of sea ice recently acquired by the Nimbus-5 spacecraft and the NASA CV-990 airborne laboratory. The images used for these studies were constructed from data acquired from the Electrically Scanned Microwave Radiometer (ESMR) which records radiation from earth and its atmosphere at a wavelength of 1.55 cm. Data are analyzed for four seasons during 1973-1975 to illustrate some basic differences in the properties of the sea ice during those times. Spacecraft data are compared with corresponding NASA CV-990 airborne laboratory data obtained over wide areas in the Arctic Basin during the Main Arctic Ice Dynamics Joint Experiment (1975) to illustrate the applicability of passive-microwave remote sensing for monitoring the time dependence of sea-ice concentration (divergence). These observations indicate significant variations in the sea-ice concentration in the spring, late fall and early winter. In addition, deep in the interior of the Arctic polar sea-ice pack, heretofore unobserved large areas, several hundred kilometers in extent, of sea-ice concentrations as low as 50% are indicated. ?? 1978 D. Reidel Publishing Company.

Boundary-Layer Meteorology↗
USGS2019

Coastal Salinity Index along the southeastern Atlantic coast and the Gulf of Mexico, 1983 to 2018

Coastal droughts have a different dynamic than upland droughts, which are typically characterized by agricultural, hydrologic, meteorological, and (or) socioeconomic effects. Drought uniquely affects coastal ecosystems because of changes in the salinity conditions of estuarine creeks and rivers. The location of the freshwater-saltwater interface in surface-water bodies is an important factor in the ecological and socioeconomic dynamics of coastal communities. To address the data and information gap for characterizing coastal drought, the Coastal Salinity Index (CSI) was developed by using salinity data. The CSI uses a computational approach similar to the Standardized Precipitation Index. The CSI can be computed for unique time intervals (for example 1-, 6-, 12-, and 24-month intervals) to characterize short- and long-term drought (saline) conditions, as well as wet (high freshwater inflow) conditions. To encourage the use of the CSI in current and future research endeavors, this investigation addressed three activities to enhance the use and application of the CSI. First, a software package was developed for the consistent computation of the CSI that includes preprocessing of salinity data, filling missing data, computing the CSI, post-processing, and generating the supporting metadata. This software package is available for download from the U.S. Geological Survey GitLab repository. Second, the CSI has been computed at sites along the southeastern Atlantic coast (Florida to North Carolina) and the Gulf of Mexico (Texas to Florida) to increase the opportunity for linking the CSI to ecological response data. Third, using telemetered salinity data, the real-time computation of the CSI has been prototyped and disseminated on the web.

Alabama, Florida, Georgia, Louisiana, Mississippi,↗
USGS2015

Application of an extreme winter storm scenario to identify vulnerabilities, mitigation options, and science needs in the Sierra Nevada mountains, USA

In the Sierra Nevada mountains (USA), and geographically similar areas across the globe where human development is expanding, extreme winter storm and flood risks are expected to increase with changing climate, heightening the need for communities to assess risks and better prepare for such events. In this case study, we demonstrate a novel approach to examining extreme winter storm and flood risks. We incorporated high-resolution atmospheric–hydrologic modeling of the ARkStorm extreme winter storm scenario with multiple modes of engagement with practitioners, including a series of facilitated discussions and a tabletop emergency management exercise, to develop a regional assessment of extreme storm vulnerabilities, mitigation options, and science needs in the greater Lake Tahoe region of Northern Nevada and California, USA. Through this process, practitioners discussed issues of concern across all phases of the emergency management life cycle, including preparation, response, recovery, and mitigation. Interruption of transportation, communications, and interagency coordination were among the most pressing concerns, and specific approaches for addressing these issues were identified, including prepositioning resources, diversifying communications systems, and improving coordination among state, tribal, and public utility practitioners. Science needs included expanding real-time monitoring capabilities to improve the precision of meteorological models and enhance situational awareness, assessing vulnerabilities of critical infrastructure, and conducting cost–benefit analyses to assess opportunities to improve both natural and human-made infrastructure to better withstand extreme storms. Our approach and results can be used to support both land use and emergency planning activities aimed toward increasing community resilience to extreme winter storm hazards in mountainous regions.

Natural Hazards↗