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At least 253 records · Page 14Linked to original sources
USGS2022

Getting ahead of flash drought: From early warning to early action

Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.

Bulletin of American Meteorological Society (BAMS)↗
USGS2016

Case study: 2016 Natural glide and wet slab avalanche cycle, Going-to-the-Sun Road, Glacier National Park, Montana, USA

The Going-to-the-Sun Road (GTSR) is the premier tourist attraction in Glacier National Park, Montana. The GTSR also traverses through and under 40 avalanche paths which pose a hazard to National Park Service (NPS) road crews during the annual spring snow plowing operation. Through a joint collaboration between the NPS and the U.S. Geological Survey (USGS), a forecasting program primarily dealing with wet snow avalanche problems serves to aid worker safety. The objective of this case study is to examine the meteorological metrics and snowpack characteristics leading up to a noteworthy wet slab and glide avalanche cycle that occurred 16-22 April, 2016 during a period of unseasonably warm and sunny weather. Continuous above freezing temperatures at upper elevations with daily maximum values reaching 10-15° C persisted for four days. The nearby Flattop Mountain SNOTEL station reported a steady loss of SWE of approximately 1.25 cm/day. River height and discharge on the Middle Fork of the Flathead River (app. 20-35 km away from starting zones) increased from 1.26 m and 139.60 m3/s (4930 cfs), respectively, on April 18 to 1.69 m and 267.59 m3/s (9450 cfs) on April 22. The ensuing avalanche cycle began with three small glide avalanches on 17 April and culminated in three large wet slab avalanches that released on wet, basal facets. These wet slabs were triggered by glide avalanches releasing above and cascading over cliffs. Four of these avalanches crossed plowed sections of the road, resulting in a three day delay in plowing operations. Finally, this specific case was compared to previous statistical models for wet snow avalanches in this transportation corridor. Out of 12 avalanche days, the model correctly predicted six of those days as avalanche days. This case study allowed for a greater understanding of a wet slab and glide avalanche cycle that occurred during a prolonged spring warming event and can serve as a reference for future similar cycles.

Conference Paper↗
USGS1995

Propagation and composition of the flood wave on the upper Mississippi River, 1993

During spring and summer 1993, record flooding inundated much of the upper Mississippi River Basin. The magnitude of the damages-in terms of property, disrupted business, and personal trauma was unmatched by any other flood disaster in United States history. Property damage alone is expected to exceed $10 billion. Damaged highways and submerged roads disrupted overland transportation throughout the flooded region. The Mississippi and the Missouri Rivers were closed to navigation before, during, and after the flooding. Millions of acres of productive farmland remained under water for weeks during the growing season. Rills and gullies in many tilled fields are the result of the severe erosion that occurred throughout the Midwestern United States farmbelt. The hydrologic effects of extended rainfall throughout the upper Midwestern United States were severe and widespread. The banks and channels of many rivers were severely eroded, and sediment was deposited over large areas of the basin's flood plain. Record flows submerged many areas that had not been affected by previous floods. Industrial and agricultural areas were inundated, which caused concern about the transport and fate of industrial chemicals, sewage effluent, and agricultural chemicals in the floodwaters. The extent and duration of the flooding caused numerous levees to fail. One failed levee on the Raccoon River in Des Moines, Iowa, led to flooding of the city's water treatment plant. As a result, the city was without drinking water for 19 days. As the Nation's principal water-science agency, the U.S. Geological Survey (USGS) is in a unique position to provide an immediate assessment of some of the hydrological effects of the 1993 flood. The USGS maintains a hydrologic data network and conducts extensive water-resources investigations nationwide. Long-term data from this network and information on local and regional hydrology provide the basis for identifying and documenting the effects of the flooding . During the flood, the USGS provided continuous streamflow and related information to the National Weather Service (NWS), the U.S. Army Corps of Engineers, the Federal Emergency Management Agency (FEMA), and many State and local agencies as part of its role to provide basic information on the Nation's surface- and ground-water resources at thousands of locations across the United States. The NWS has used the data in forecasting floods and issuing flood warnings. The data have been used by the Corps of Engineers to operate water diversions, dams, locks, and levees. The FEMA and many State and local emergency management agencies have used USGS hydrologic data and NWS forecasts as part of the basis of their local flood-response activities. In addition, USGS hydrologists are conducting a series of investigations to document the effects of the flooding and to improve understanding of the related processes. The major initial findings from these studies will be reported in this Circular series as results become available. U.S. Geological Survey Circular 1120, Floods in the Upper Mississippi River Basin, 1993, consists of individually published chapters that will document the effects of the 1993 flooding. The series includes data and findings on the magnitude and frequency of peak discharges; precipitation; water-quality characteristics, including nutrients and man-made contaminants; transport of sediment; assessment of sediment deposited on flood plains; effects of inundation on ground-water quality; flood-discharge volume; effects of reservoir storage on flood peaks; stream-channel scour at selected bridges; extent of floodplain inundation; and documentation of geomorphologic changes.

upper Mississippi River basin↗
USGS2006

Natural glide slab avalanches, Glacier National Park, USA: A unique hazard and forecasting challenge

In a museum of avalanche phenomena, glide cracks and glide avalanches might be housed in the “strange but true” section. These oddities are uncommon in most snow climates and tend to be isolated to specific terrain features such as bedrock slabs. Many glide cracks never result in avalanches, and when they do, the wide range of time between crack formation and slab failure makes them highly unpredictable. Despite their relative rarity, glide cracks and glide avalanches pose a regular threat and complex forecasting challenge during the annual spring opening of the Going-to-the-Sun Road in Glacier National Park, U.S.A. During the 2006 season, a series of unusual glide cracks delayed snow removal operations by over a week and provided a unique opportunity to record detailed observations of glide avalanches and characterize their occurrence and associated weather conditions. Field observations were from snowpits, crown profiles and where possible, measurements of slab thickness, bed surface slope angle, substrate and other physical characteristics. Weather data were recorded at one SNOTEL site and two automated stations located from 0.6-10 km of observed glide slab avalanches. Nearly half (43%) of the 35 glide slab avalanches recorded were Class D2-2.5, with 15% Class D3-D3.5. The time between glide crack opening and failure ranged from 2 days to over six weeks, and the avalanches occurred in cycles associated with loss of snow water equivalent and spikes in temperature and radiation. We conclude with suggest ions for further study.

Montana↗
USGS2013

A twenty-first century California observing network for monitoring extreme weather events

During Northern Hemisphere winters, the West Coast of North America is battered by extratropical storms. The impact of these storms is of paramount concern to California, where aging water supply and flood protection infrastructures are challenged by increased standards for urban flood protection, an unusually variable weather regime, and projections of climate change. Additionally, there are inherent conflicts between releasing water to provide flood protection and storing water to meet requirements for water supply, water quality, hydropower generation, water temperature and flow for at-risk species, and recreation. In order to improve reservoir management and meet the increasing demands on water, improved forecasts of precipitation, especially during extreme events, is required. Here we describe how California is addressing their most important and costliest environmental issue – water management – in part, by installing a state-of-the-art observing system to better track the area’s most severe wintertime storms.

California↗
USGS2016

Iterative ecological forecasting: Needs, opportunities, and challenges

A fundamental environmental challenge facing humanity in the 21st century and beyond is predicting the impacts of global environmental change. This challenge is complicated by the fact that we live on a non-stationary, unreplicated planet that is rapidly moving outside the envelope of natural variability into an historical non-analog world. In other words, while the past helps inform us about how the world has worked, it may no longer be the relevant frame of reference for management, conservation, and sustainability. In this future world the two questions at the foundation of sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect this trajectory?” These are, at their heart, questions about ecological forecasting.

Conference Paper↗
USGS2015

Hail formation triggers rapid ash aggregation in volcanic plumes

During explosive eruptions, airborne particles collide and stick together, accelerating the fallout of volcanic ash and climate-forcing aerosols. This aggregation process remains a major source of uncertainty both in ash dispersal forecasting and interpretation of eruptions from the geological record. Here we illuminate the mechanisms and timescales of particle aggregation from a well-characterized ‘wet’ eruption. The 2009 eruption of Redoubt Volcano in Alaska incorporated water from the surface (in this case, a glacier), which is a common occurrence during explosive volcanism worldwide. Observations from C-band weather radar, fall deposits, and numerical modeling demonstrate that volcanic hail formed rapidly in the eruption plume, leading to mixed-phase aggregation of ~95% of the fine ash and stripping much of the cloud out of the atmosphere within 30 minutes. Based on these findings, we propose a mechanism of hail-like aggregation that contributes to the anomalously rapid fallout of fine ash and the occurrence of concentrically-layered aggregates in volcanic deposits.

Alaska↗
USGS2025

Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA

Glide avalanches present a significant and repetitive challenge to many operational forecasting programs, and they are likely to become more frequent. While the spatial location of glide release areas is extremely consistent, the onset of glide avalanche release is notoriously difficult to forecast, and their destructive potential can be immense. Thus, the timing and dynamics of glide avalanches is an important area of study. To better understand these processes, and to improve assessments of risk to transportation corridors and infrastructure, event documentation is key. Here, we survey a large glide avalanche event along the Going-to-the-Sun Road in Glacier National Park, Montana, USA, during road opening operations in the spring of 2022. Using three sets of terrestrial lidar data (pre-event, post-event, and snow-off), we quantified key aspects of the avalanche and created powerful visualizations for analysis. Further, we evaluated meteorological data from automated weather stations between the onset of glide cracking and avalanche release. Last, we synthesized lidar data with a numerical dynamics model to replicate the event in a simulated environment. Using the tuned model, we determined the critical mean snow depth in the release area necessary for an avalanche to reach the road (4.2 m). Our method may be of particular use for glide avalanches, which tend to release in roughly the same place and time each year at a known interface. This could make the calculated critical depths more consistently reliable and preclude the need for additional tuning in dynamics models. As 1) lidar technology continues to improve and reduce in cost, 2) transportation corridors continue to extend into avalanche terrain, and 3) glide avalanches potentially become increasingly frequent, the synthesis outlined here provides a valuable tool for operational forecasters considering infrastructure threatened by glide events.

Montana↗
USGS2014

Flood-inundation maps for the Susquehanna River near Harrisburg, Pennsylvania, 2013

A series of 28 digital flood-inundation maps was developed for an approximate 25-mile reach of the Susquehanna River in the vicinity of Harrisburg, Pennsylvania. The study was selected by the U.S. Army Corps of Engineers (USACE) national Silver Jackets program, which supports interagency teams at the state level to coordinate and collaborate on flood-risk management. This study to produce flood-inundation maps was the result of a collaborative effort between the USACE, National Weather Service (NWS), Susquehanna River Basin Commission (SRBC), The Harrisburg Authority, and the U.S. Geological Survey (USGS). These maps are accessible through Web-mapping applications associated with the NWS, SRBC, and USGS. The maps can be used in conjunction with the real-time stage data from the USGS streamgage 01570500, Susquehanna River at Harrisburg, Pa., and NWS flood-stage forecasts to help guide the general public in taking individual safety precautions and will provide local municipal officials with a tool to efficiently manage emergency flood operations and flood mitigation efforts. The maps were developed using the USACE HEC–RAS and HEC–GeoRAS programs to compute water-surface profiles and to delineate estimated flood-inundation areas for selected stream stages. The maps show estimated flood-inundation areas overlaid on high-resolution, georeferenced, aerial photographs of the study area for stream stages at 1-foot intervals between 11 feet and 37 feet (which include NWS flood categories Action, Flood, Moderate, and Major) and the June 24, 1972, peak-of-record flood event at a stage of 33.27 feet at the Susquehanna River at Harrisburg, Pa., streamgage.

Pennsylvania↗
USGS2014

Developing and testing temperature models for regulated systems: a case study on the Upper Delaware River

Water temperature is an important driver of many processes in riverine ecosystems. If reservoirs are present, their releases can greatly influence downstream water temperatures. Models are important tools in understanding the influence these releases may have on the thermal regimes of downstream rivers. In this study, we developed and tested a suite of models to predict river temperature at a location downstream of two reservoirs in the Upper Delaware River (USA), a section of river that is managed to support a world-class coldwater fishery. Three empirical models were tested, including a Generalized Least Squares Model with a cosine trend (GLScos), AutoRegressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN). We also tested one mechanistic Heat Flux Model (HFM) that was based on energy gain and loss. Predictor variables used in model development included climate data (e.g., solar radiation, wind speed, etc.) collected from a nearby weather station and temperature and hydrologic data from upstream U.S. Geological Survey gages. Models were developed with a training dataset that consisted of data from 2008 to 2011; they were then independently validated with a test dataset from 2012. Model accuracy was evaluated using root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), and index of agreement (d) statistics. Model forecast success was evaluated using baseline-modified prime index of agreement (md) at the one, three, and five day predictions. All five models accurately predicted daily mean river temperature across the entire training dataset (RMSE = 0.58–1.311, NSE = 0.99–0.97, d = 0.98–0.99); ARIMA was most accurate (RMSE = 0.57, NSE = 0.99), but each model, other than ARIMA, showed short periods of under- or over-predicting observed warmer temperatures. For the training dataset, all models besides ARIMA had overestimation bias (PBIAS = −0.10 to −1.30). Validation analyses showed all models performed well; the HFM model was the most accurate compared other models (RMSE = 0.92, both NSE = 0.98, d = 0.99) and the ARIMA model was least accurate (RMSE = 2.06, NSE = 0.92, d = 0.98); however, all models had an overestimation bias (PBIAS = −4.1 to −10.20). Aside from the one day forecast ARIMA model (md = 0.53), all models forecasted fairly well at the one, three, and five day forecasts (md = 0.77–0.96). Overall, we were successful in developing models predicting daily mean temperature across a broad range of temperatures. These models, specifically the GLScos, ANN, and HFM, may serve as important tools for predicting conditions and managing thermal releases in regulated river systems such as the Delaware River. Further model development may be important in customizing predictions for particular biological or ecological needs, or for particular temporal or spatial scales.

Delaware, New York, Pennsylvania↗
USGS2024

A case study and comparison of mid-winter warming and solar driven wet slab avalanche cycles

Wet slab avalanches are poorly understood and often difficult to forecast. Yet, wet slab avalanches can be destructive and may become more common in a changing climate. As the onset of wet avalanches moves earlier in the winter season due to climate change, understanding snowpack and meteorological characteristics of wet slab avalanches will become increasingly important. In this study, we examined two recent late-January wet slab cycles triggered by warming and solar input in the Rocky Mountains of Idaho and Montana, United States. We posed two questions to help us understand these potentially increasingly frequent, mid-winter wet slab cycles: i) what are the weather and snowpack patterns of two mid-winter wet slab cycles and ii) how did these non-rain-on-snow, mid-winter warming events compare to the historical climate normal? In both locations, a short-lived ridge of high pressure strengthened over the region from late January to early February 2024. Temperatures rapidly increased to 6° C at middle and upper-elevation locations and remained above freezing for 48 to 72 hours. Wet slab avalanches (Montana: n=68, Idaho: n=11) released on a layer of facets resting on a crust formed in late December and slab depths averaged 40 to 150 cm. Avalanches primarily occurred on southeast, south, and southwest aspects at middle and upper elevations. Additionally, positive January monthly temperature trends exist in both locations from 1990 to 2020, and these two cycles from 2024 highlight impacts of midwinter warming events with unstable snowpack conditions. These cycles also highlight how a relatively uncommon mid-winter warming event with low radiative input can produce very large destructive wet slab avalanches. Understanding mid-winter warming events and associated wet slab avalanche cycles will help us prepare and forecast for potentially more common scenarios like this in the future.

Idaho, Montana↗
USGS2018

The 3D Elevation Program—Flood risk management

Flood-damage reduction in the United States has been a longstanding but elusive societal goal. The national strategy for reducing flood damage has shifted over recent decades from a focus on construction of flood-control dams and levee systems to a three-pronged strategy to (1) improve the design and operation of such structures, (2) provide more accurate and accessible flood forecasting, and (3) shift the Federal Emergency Management Agency (FEMA) National Flood Insurance Program to a more balanced, less costly flood-insurance paradigm. Expanding the availability and use of high-quality, three-dimensional (3D) elevation information derived from modern light detection and ranging (lidar) technologies to provide essential terrain data poses a singular opportunity to dramatically enhance the effectiveness of all three components of this strategy. Additionally, FEMA, the National Weather Service, and the U.S. Geological Survey (USGS) have developed tools and joint program activities to support the national strategy. The USGS 3D Elevation Program (3DEP) has the programmatic infrastructure to produce and provide essential terrain data. This infrastructure includes (1) data acquisition partnerships that leverage funding and reduce duplicative efforts, (2) contracts with experienced private mapping firms that ensure acquisition of consistent, low-cost 3D elevation data, and (3) the technical expertise, standards, and specifications required for consistent, edge-to-edge utility across multiple collection platforms and public access unfettered by individual database designs and limitations. High-quality elevation data, like that collected through 3DEP, are invaluable for assessing and documenting flood risk and communicating detailed information to both responders and planners alike. Multiple flood-mapping programs make use of USGS streamflow and 3DEP data. Flood insurance rate maps, flood documentation studies, and flood-inundation map libraries are products of these programs.

Fact Sheet↗
USGS2022

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata . We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

New Hampshire↗
USGS2014

Development of the Coastal Storm Modeling System (CoSMoS) for predicting the impact of storms on high-energy, active-margin coasts

The Coastal Storm Modeling System (CoSMoS) applies a predominantly deterministic framework to make detailed predictions (meter scale) of storm-induced coastal flooding, erosion, and cliff failures over large geographic scales (100s of kilometers). CoSMoS was developed for hindcast studies, operational applications (i.e., nowcasts and multiday forecasts), and future climate scenarios (i.e., sea-level rise + storms) to provide emergency responders and coastal planners with critical storm hazards information that may be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. The prototype system, developed for the California coast, uses the global WAVEWATCH III wave model, the TOPEX/Poseidon satellite altimetry-based global tide model, and atmospheric-forcing data from either the US National Weather Service (operational mode) or Global Climate Models (future climate mode), to determine regional wave and water-level boundary conditions. These physical processes are dynamically downscaled using a series of nested Delft3D-WAVE (SWAN) and Delft3D-FLOW (FLOW) models and linked at the coast to tightly spaced XBeach (eXtreme Beach) cross-shore profile models and a Bayesian probabilistic cliff failure model. Hindcast testing demonstrates that, despite uncertainties in preexisting beach morphology over the ~500 km alongshore extent of the pilot study area, CoSMoS effectively identifies discrete sections of the coast (100s of meters) that are vulnerable to coastal hazards under a range of current and future oceanographic forcing conditions, and is therefore an effective tool for operational and future climate scenario planning.

Natural Hazards↗
USGS2024

ENSO-based outlook of droughts and agricultural outcomes in Afghanistan

Drought is one of the key drivers of food insecurity in Afghanistan, which is among the most food insecure countries in the world. In this study, we build on previous research and seek to answer the central question: “What is the influence of El Niño-Southern Oscillation (ENSO) on drought outlooks and agricultural yield outcome in Afghanistan, and how do these influences vary spatially ?” We do so by utilizing multiple indicators of droughts and available wheat yield reports. We find a clear distinction in the probability of drought (defined as being in the lower tercile) in Afghanistan during La Niña compared to El Niño events since 1981. The probability of drought in Afghanistan increased during La Niña, particularly in the North, Northeast, and West regions. La Niña events are related to an increase in the probability of snow drought, particularly in parts of the Amu Darya basin. It is found that relative to El Niño events, snow water equivalent [total runoff] during La Niña events January–March (March–July total runoff) decreases between 9% and 30% (28%–42%) for the five major basins in the country. The probability of agricultural drought during La Niña events is found to be higher than 70% in the rainfed and irrigated areas of the Northeast, North, and West regions. This result is at least partly supported by reported wheat yield composites related to La Niña events that tend to be lower than for El Niño events across all regions in the case of rainfed wheat (statistically significant in Northeast, West, and South regions) and in some cases for irrigated wheat. The results of this study have direct implications for improving early warning of worsening food insecurity in Afghanistan during La Niña events, given that we now have long-lead and skillful forecasts of ENSO up to 18–24 months in advance, which could potentially be used to provide earlier warning of worsening food insecurity in Afghanistan

Weather and Climate Extremes↗
USGS2016

Climate regulates alpine lake ice cover phenology and aquatic ecosystem structure

High-elevation aquatic ecosystems are highly vulnerable to climate change, yet relatively few records are available to characterize shifts in ecosystem structure or their underlying mechanisms. Using a long-term dataset on seven alpine lakes (3126 to 3620 m) in Colorado, USA, we show that ice-off dates have shifted seven days earlier over the past 33 years and that spring weather conditions – especially snowfall – drive yearly variation in ice-off timing. In the most well-studied lake, earlier ice-off associated with increases in water residence times, thermal stratification, ion concentrations, dissolved nitrogen, pH, and chlorophyll-a. Mechanistically, low spring snowfall and warm temperatures reduce summer stream flow (increasing lake residence times) but enhance melting of glacial and permafrost ice (increasing lake solute inputs). The observed links among hydrological, chemical, and biological responses to climate factors highlight the potential for major shifts in the functioning of alpine lakes due to forecasted climate change.

Colorado↗
USGS2018

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Proceedings of the National Academy of Sciences of↗
USGS2022

Understanding and modeling tephra transport: Lessons learned from the 18 May 1980 eruption of Mount St. Helens

Discoveries made during the 18 May 1980 eruption of Mount St. Helens advanced our understanding of tephra transport and deposition in fundamental ways. The eruption enabled detailed, quantitative observations of downwind cloud movement and particle sedimentation, along with the dynamics of co-pyroclastic-density current (PDC) clouds lofted from ground-hugging currents. The deposit was mapped and sampled over more than 150,000 km 2 within days of the event and remains among the most thoroughly documented tephra deposits in the world. Abundant observations were made possible by the large size of the eruption, its occurrence in good weather during daylight hours, cloud movement over a large, populated continent, and the availability of images from recently deployed satellites. These observations underpinned new, quantitative models for the rise and growth of volcanic plumes, the importance of umbrella clouds in dispersing ash, and the roles of particle aggregation and gravitational instabilities in removing ash from the atmosphere. Exceptional detail in the eruption chronology and deposit characterization helped identify the eruptive phases contributing to deposition in different sectors of the distal deposit. The eruption was the first to significantly impact civil aviation, leading to the earliest documented case of in-flight engine damage. Continued eruptive activity in 1980 also motivated pioneering use of meteorological models to forecast ash-cloud movement. In this paper, we consider the most important discoveries and how they changed the science of tephra transport.

Washington↗