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At least 703 records · Page 39Linked to original sources
USGS2021

Understanding collaborative governance from a communication network perspective: A case study of the Atlantic Salmon recovery framework

Atlantic salmon populations in Maine remain critically low despite extensive hatchery supplementation and habitat improvement efforts. In 2000, the Gulf of Maine Distinct Population Segment was listed as Endangered under the ESA with joint listing authority shared by the National Oceanic and Atmospheric Administration (NOAA) and the United States Fish and Wildlife Service (USFWS). Because, regulators, and tribal, federal and state managers operate with independent authorities, recovery decisions depend upon effective communication and coordination among groups. Using a mixed-methods approach, we surveyed (n = 41) and interviewed (n = 28) members of the Atlantic Salmon Recovery Framework (ASRF), the joint governance structure responsible for Atlantic salmon management in Maine. We used survey results to examine the communication between members of ASRF through communication network analysis. While there is a relatively high network density for individual communication (56 %), connections are decentralized, a characteristic that can be incompatible with some organizational structures. Challenges reported by members fit into three general categories; i. slow and ineffective decision-making, ii. confusion surrounding leadership and accountability, and iii. low adaptive capacity. Despite these challenges, participants reported a commitment to maintaining a collaborative governance structure and a long history of inter-organizational relationships. This introspective effort has led to a reorganization effort to optimize communication pathways.

Maine↗
USGS2012

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

Environmental Modelling and Software↗
USGS2020

A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.

Environmental Modelling and Software↗
USGS2022

Complex magmatic-tectonic interactions during the 2020 Makushin Volcano, Alaska, earthquake swarm

On June 15, 2020, at 21:16 UTC, a locally-felt earthquake of magnitude 4.2 struck Unalaska Island, Alaska, ∼15 km west of the town of Unalaska and the large fishing port of Dutch Harbor. The event was followed by a M4.1 earthquake at 00:34 UTC and several M3+ aftershocks, initiating a prolific sequence with hundreds of earthquakes recorded into late December. The earthquakes all locate about 12 km southeast of the summit of Makushin Volcano at 7 to 10 km depth. To date, no eruptive activity or other surface changes have been observed at the volcano in webcam images, GPS or InSAR. Seismic bursts close to volcanoes are often associated with the onset of unrest that can lead to eruption. However, determining whether seismicity reflects magmatic rather than tectonic stresses is often challenging, although critical for hazard assessments and risk management strategies. To investigate the triggering mechanisms of the recent Makushin seismicity, we integrate information from space-time patterns of the earthquake hypocenters with their fault-plane solutions. We relocate the swarm events using double-difference relocation techniques and a 3D velocity model and find that the earthquakes, although they seem to follow two predominant orientations (NW-SE and SW-NE), do not show clear clustering into preferred alignments. Similarly, we do not observe pronounced migration in time and space. Fault-plane solutions (FPS) for all but one M2.5+ earthquakes have P-axis orientations consistent with subhorizontal NW-SE oriented regional maximum compression, whereas many of the lower-magnitude earthquakes have P-axes perpendicular to regional maximum compression. This provides evidence for the presence of a local stress field likely induced by magma intrusion. Results from Coulomb stress modeling are also consistent with dike inflation modulated by stresses induced by the M4+ earthquakes. The seismic swarm is thus likely linked to a superposition of driving stresses from both magmatic and tectonic processes on pre-existing faults. The case of the 2020 Makushin swarm, with its unusual characteristics, challenges traditional swarm classification schemes and suggests that a reconsideration of the definition of seismic swarms as having the maximum magnitude event in the middle of the swarm is warranted.

Alaska↗
USGS2026

Strength of depensation not influenced by fish population productivity

A long-held assumption in the management of exploited fisheries is that fish populations will compensate with increased recruit survival to replenish the population when adult stock size is reduced through harvest. Observations of depensatory recruitment (reduced recruit survival at low adult stock size) and critical depensatory thresholds have challenged the compensation assumption. Post et al. (2002) postulated that critical depensatory thresholds were related to fish population productivity. Walleye Sander vitreus are a culturally, economically, and recreationally important sportfish whose persistence is being challenged by natural recruitment declines throughout much of its native range. Depensation, among other abiotic and biotic stressors, has been implicated in walleye natural recruitment declines. If walleye population productivity is related to critical depensatory thresholds, then population productivity benchmarks could be established to reduce the probability of crossing them. We used empirically-derived and model predicted depensation values ( q ) and empirical estimates of walleye population productivity to test for relationships between these variables in northern Wisconsin lakes. We found little evidence for a relationship between q and walleye population productivity across all lakes examined. Our finding failed to support the theoretical postulation of a relationship between these variables by Post et al. (2002) for walleye. Little evidence for a relationship between q and population productivity suggests that depensatory thresholds may differ among individual walleye populations and that walleye populations may transition abruptly between compensatory and depensatory states. Given our findings, conservation efforts for walleye that solely focus on low productivity populations may miss other trends because population productivity may not be considered a broad predictor of crossing a critical depensatory threshold.

Fisheries Research↗
USGS2009

Wildfire risk in the wildland-urban interface: A simulation study in northwestern Wisconsin

The rapid growth of housing in and near the wildland–urban interface (WUI) increases wildfire risk to lives and structures. To reduce fire risk, it is necessary to identify WUI housing areas that are more susceptible to wildfire. This is challenging, because wildfire patterns depend on fire behavior and spread, which in turn depend on ignition locations, weather conditions, the spatial arrangement of fuels, and topography. The goal of our study was to assess wildfire risk to a 60,000 ha WUI area in northwestern Wisconsin while accounting for all of these factors. We conducted 6000 simulations with two dynamic fire models: Fire Area Simulator (FARSITE) and Minimum Travel Time (MTT) in order to map the spatial pattern of burn probabilities. Simulations were run under normal and extreme weather conditions to assess the effect of weather on fire spread, burn probability, and risk to structures. The resulting burn probability maps were intersected with maps of structure locations and land cover types. The simulations revealed clear hotspots of wildfire activity and a large range of wildfire risk to structures in the study area. As expected, the extreme weather conditions yielded higher burn probabilities over the entire landscape, as well as to different land cover classes and individual structures. Moreover, the spatial pattern of risk was significantly different between extreme and normal weather conditions. The results highlight the fact that extreme weather conditions not only produce higher fire risk than normal weather conditions, but also change the fine-scale locations of high risk areas in the landscape, which is of great importance for fire management in WUI areas. In addition, the choice of weather data may limit the potential for comparisons of risk maps for different areas and for extrapolating risk maps to future scenarios where weather conditions are unknown. Our approach to modeling wildfire risk to structures can aid fire risk reduction management activities by identifying areas with elevated wildfire risk and those most vulnerable under extreme weather conditions.

Wisconsin↗
USGS2025

Practical application of time-lapse camera imagery to develop water-level data for three hydrologic monitoring sites in Wisconsin during water year 2020

Using camera imagery to measure water level (camera-stage) is a well-researched area of study. Previous camera-stage studies have shown promising results when implementing this technology with tight constraints on test conditions. However, there is a need for a more comprehensive evaluation of the extensibility of camera-stage to practical applications. Therefore, the aim of this study was to test a camera-stage method under a wide variety of test conditions to better understand the successes and challenges of using this technology in real-world scenarios. In this study, this approach was tested during Water Year 2020 at three existing U.S. Geological Study (USGS) stream gaging stations in south central Wisconsin that had existing USGS water-level instrumentation. The specific reference objects tested were white pipes and a concrete wall. Since successful application of camera-stage relies on use of suitable images, all captured images in this study were visually inspected to determine suitability for application of camera-stage. Camera-stage measurements were then computed only on images deemed suitable and the results were compared with ground-truth stage values to determine the accuracy. For the purposes of this study, camera-stage values within ±0.10 ft of the actual stage were considered acceptable. One major challenge highlighted was the potential difficulty in obtaining suitable imagery, with the proportion of suitable images varying greatly between the four trials from 38 % to 92 %. The results from applying camera-stage to suitable images were encouraging though, with 79 % to 99 % of evaluated camera-stage values qualifying as acceptable among the four test trials.

Wisconsin↗
USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2011

Adaptive management for a turbulent future

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management. ?? 2010 .

Journal of Environmental Management↗
USGS2021

Is there enough water? How bearish and bullish outlooks are linked to decision-maker perspectives on environmental flows

Policies that mandate environmental flows (e-flows) can be powerful tools for freshwater conservation, but implementation of these policies faces many hurdles. To better understand these challenges, we explored two key questions: (1) What additional data are needed to implement e-flows? and (2) What are the major socio-political barriers to implementing e-flows? We surveyed water and natural resource decision makers in the semi-arid Red River basin, Texas-Oklahoma, USA, and used social network analysis to analyze their communication patterns. Most respondents agreed that e-flows can provide important benefits and identified the same data needs. However, respondents sharply in their beliefs on other issues, and a clustering analysis revealed two distinct groups of decision makers. One cluster of decision makers tended to be bearish, or pessimistic, and believed that: current flow conditions are not adequate, there are many serious socio-political barriers to implementation, water conflicts will likely increase in the future, and climate change is likely to exacerbate these issues. The other cluster of respondents was bullish, or optimistic: they foresaw fewer future water conflicts and fewer socio-political barriers to implementation. Despite these differences, both clusters largely identified the same data needs and barriers to e-flows implementation. Our social network analysis revealed that the frequency of communication between clusters was not significantly different than the frequency of communication within clusters. Overall, our results suggest that the different perspectives of decision-makers could complicate efforts to implement e-flows and proactively plan for climate change. However, there are opportunities for collaboration on addressing common data needs and barriers to implementation. Overall, our study provides a key socio-environmental perspective on e-flows implementation from a semi-arid and socio-politically complex river basin and contextualizes the many challenges facing e-flows implementation in river basins globally.

Journal of Environmental Management↗
USGS2022

Data synthesis for environmental management: A case study of Chesapeake Bay

Synthesizing large, complex data sets to inform resource managers towards effective environmental stewardship is a universal challenge. In Chesapeake Bay, a well-studied and intensively monitored estuary in North America, the challenge of synthesizing data on water quality and land use as factors related to a key habitat, submerged aquatic vegetation, was tackled by a team of scientists and resource managers operating at multiple levels of governance (state, federal). The synthesis effort took place over a two-year period (2016–2018), and the results were communicated widely to a) scientists via peer review publications and conference presentations; b) resource managers via web materials and workshop presentations; and c) the public through newspaper articles, radio interviews, and podcasts. The synthesis effort was initiated by resource managers at the United States Environmental Protection Agencys’ Chesapeake Bay Program and 16 scientist participants were recruited from a diversity of organizations. Multiple short, immersive workshops were conducted regularly to conceptualize the problem, followed by data analysis and interpretation that supported the preparation of the synthetic products that were communicated widely. Reflections on the process indicate that there are a variety of structural and functional requirements, as well as enabling conditions, that need to be considered to achieve successful outcomes from synthesis efforts.

Chesapeake Bay↗
USGS2021

Enhancing Great Lakes coastal ecosystems research by initiating engagement between scientists and decision-makers

A disconnect between scientific research and environmental management communities can be a detriment to both. In the case of Great Lakes coastal ecosystems, which are inherently complex and subject to uncertain effects of future climatic, environmental, and anthropogenic drivers, greater collaboration could be beneficial to their sustainability. We capture the challenges and opportunities identified by a scientist/decision-maker co-production workshop focused on the future environmental quality of Great Lakes coastal wetlands. We explain our path through the stakeholder workshop process, our challenges in translating meeting outcomes into actionable items, and lessons learned to bridge gaps between scientists and decision-makers. Additionally, we determine topics and directions identified by decision-makers that can be modeled with existing technologies and others that require further research. These topics may be incorporated into future research efforts and could serve as a shortlist of research priorities that were identified by decision-makers working with coastal wetland issues. Based on lessons learned during and after the workshop, we provide suggestions for bridging the gap between researchers and decision-makers, including sustained engagement between these groups and improved interaction through the beginning, duration, and end of research and/or management efforts.

Great Lakes↗
USGS2022

Genetic population structure of cisco, Coregonus artedi, in the Laurentian Great Lakes

Management of a widely distributed species can be a challenge when management priorities, resource status, and assessment methods vary across jurisdictions. For example, restoration and preservation of coregonine species diversity is a goal of management agencies across the Laurentian Great Lakes. However, management goals and the amount of information available varies across management units, making the focus for management efforts challenging to determine. Genetic data provide a spatially consistent means to assess diversity. Therefore, we examined the genetic stock structure of cisco ( Coregonus artedi ) in the Great Lakes where the species is still extant. Using genotype data from 17 microsatellite DNA loci, we observed low levels of population structure among collections with most contributions to overall diversity occurring among lakes. Cisco from lakes Superior, Michigan, Ontario, and the St. Marys River could be considered single genetic populations while distinct genetic populations were observed among samples from northern Lake Huron. Significant within-lake diversity in Lake Huron is supported by populations found in embayments in northern Lake Huron. The Grand Traverse Bay population in Lake Michigan represents a distinct population with reduced levels of genetic variation when compared to other lakes. The different levels of within lake population structure we observed will be important to consider as future lake-specific management plans are developed.

Great Lakes↗
USGS2014

Projections of the Ganges-Brahmaputra precipitation: downscaled from GCM predictors

Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the downscaled precipitation indicated that the uncertainty in the downscaled precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is suggested for comprehensive impact studies.

Brahmaputra;Ganges↗
USGS2018

Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA

Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO 3 − ) input functions by characterizing unsaturated zone NO 3 − transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous “vertical flux method” (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO 3 − source concentration factor (which determines the local NO 3 − input concentration); unsaturated zone travel time; NO 3 − concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO 3 − “extinction depth”, the eventual steady state depth of the NO 3 − front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R 2 values of 0.52 – 0.86 and 0.22 – 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO 3 − at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO 3 − extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO 3 − transport processes in a statistical framework based on readily mappable GIS input variables.

Wisconsin↗
USGS2020

Assessment of leachable elements in volcanic ashfall: A review and evaluation of a standardized protocol for ash hazard characterization

Volcanic ash presents a widespread and common hazard during and after eruptions. Complex interactions between solid ash surfaces and volcanic gases lead to the formation of soluble salts that may be mobilized in aqueous environments. A variety of stakeholders may be concerned about the effects of ash on human and animal health, drinking water supplies, crops, soils and surface runoff. As part of the immediate emergency response, rapid dissemination of information regarding potentially hazardous concentrations of soluble species is critical. However, substantial variability in the methods used to characterize leachable elements makes it challenging to compare datasets and eruption impacts. To address these challenges, the International Volcanic Health Hazard Network ( www.ivhhn.org ) organized a two-day workshop to define appropriate methods for hazard assessment. The outcome of this workshop was a ‘consensus protocol’ for analysis of volcanic ash samples for rapid assessment of hazards from leachable elements, which was subsequently ratified by leading volcanological organizations. The purpose of this protocol is to recommend clear, standard and reliable methods applicable to a range of purposes during eruption response, such as assessing impacts on drinking-water supplies and ingestion hazards to livestock, and also applicable to research purposes. Where possible, it is intended that the methods make use of commonly available equipment and require little training. To evaluate method transferability, an interlaboratory comparison exercise was organized among six laboratories worldwide. Each laboratory received a split of pristine ash, and independently analyzed it according to the protocol for a wide range of elements. Collated results indicate good repeatability and reproducibility for most elements, thus indicating that the development of this protocol is a useful step towards providing standardized and reliable methods for ash hazard characterization. In this article, we review recent ash leachate studies, report the outcomes of the comparison exercise and present a revised and updated protocol based on the experiences and recommendations of the exercise participants. The adoption of standardized methods will improve and facilitate the comparability of results among studies and enable the ongoing development of a global database of leachate information relevant for informing volcanic health hazards assessment.

Journal of Volcanology and Geothermal Research↗
USGS2021

Growth of the wildland-urban interface within and around U.S. National Forests and Grasslands, 1990-2010

The wildland-urban interface (WUI), where housing is in close proximity to or intermingled with wildland vegetation, is widespread throughout the United States, but it is unclear how this type of housing development affects public lands. We used a national dataset to examine WUI distribution and growth (1990–2010) in proximity to National Forests and created a typology to characterize each National Forest’s combination of WUI area and housing growth. We found that National Forests are hotspots for WUI growth, with a 38% increase in WUI area and 46% growth in WUI houses from 1990 to 2010, in excess of WUI growth for the conterminous U.S. Growth within National Forests was higher than the surrounding area. Diffuse intermix WUI, where houses are intermingled with wildland vegetation, is common within National Forests, but WUI houses around National Forests were primarily in denser interface WUI areas, which lack substantial wildland vegetation. WUI was more prevalent within and around National Forests in the East, while National Forests in the West experienced higher rates of WUI growth. National Forests with the most challenging WUI issues—extensive WUI area and rapid growth in intermix and interface—were found primarily in the South and interior West. Given the diversity of WUI landscapes, effectively responding to current and future WUI challenges will require both engagement with individual homeowners dispersed throughout National Forests, as well as increased emphasis on mitigating denser interface development around National Forests. At a time when wildfire risks are expected to intensify due to climate change, and 75% of privately owned land within and around National Forests is not yet WUI, understanding WUI growth patterns in proximity to public lands is vital for land management and human well-being.

Landscape and Urban Planning↗
USGS2014

Assessment of tsunami hazard to the U.S. Atlantic margin

Tsunami hazard is a very low-probability, but potentially high-risk natural hazard, posing unique challenges to scientists and policy makers trying to mitigate its impacts. These challenges are illustrated in this assessment of tsunami hazard to the U.S. Atlantic margin. Seismic activity along the U.S. Atlantic margin in general is low, and confirmed paleo-tsunami deposits have not yet been found, suggesting a very low rate of hazard. However, the devastating 1929 Grand Banks tsunami along the Atlantic margin of Canada shows that these events continue to occur. Densely populated areas, extensive industrial and port facilities, and the presence of ten nuclear power plants along the coast, make this region highly vulnerable to flooding by tsunamis and therefore even low-probability events need to be evaluated. We can presently draw several tentative conclusions regarding tsunami hazard to the U.S. Atlantic coast. Landslide tsunamis likely constitute the biggest tsunami hazard to the coast. Only a small number of landslides have so far been dated and they are generally older than 10,000 years. The geographical distribution of landslides along the margin is expected to be uneven and to depend on the distribution of seismic activity along the margin and on the geographical distribution of Pleistocene sediment. We do not see evidence that gas hydrate dissociation contributes to the generation of landslides along the U.S. Atlantic margin. Analysis of landslide statistics along the fluvial and glacial portions of the margin indicate that most of the landslides are translational, were probably initiated by seismic acceleration, and failed as aggregate slope failures. How tsunamis are generated from aggregate landslides remains however, unclear. Estimates of the recurrence interval of earthquakes along the continental slope may provide maximum estimates for the recurrence interval of landslide along the margin. Tsunamis caused by atmospheric disturbances and by coastal earthquakes may be more frequent than those generated by landslides, but their amplitudes are probably smaller. Among the possible far-field earthquake sources, only earthquakes located within the Gulf of Cadiz or west of the Tore-Madeira Rise are likely to affect the U.S. coast. It is questionable whether earthquakes on the Puerto Rico Trench are capable of producing a large enough tsunami that will affect the U.S. Atlantic coast. More information is needed to evaluate the seismic potential of the northern Cuba fold-and-thrust belt. The hazard from a volcano flank collapse in the Canary Islands is likely smaller than originally stated, and there is not enough information to evaluate the magnitude and frequency of flank collapse from the Azores Islands. Both deterministic and probabilistic methods to evaluate the tsunami hazard from the margin are available for application to the Atlantic margin, but their implementation requires more information than is currently available.

Marine Geology↗