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At least 379 records · Page 21Linked to original sources
USGS2012

Threats and stressors to the health of the ecosystems of Lakes Mead and Mohave: Chapter 6 in A synthesis of aquatic science for management of Lakes Mead and Mohave

Ecosystem impacts from visitor activities or natural environmental change are important concerns in all units of the National Park system. Possible impacts to aquatic ecosystems at Lake Mead National Recreation Area (LMNRA) are of particular concern because of the designation of Lakes Mead and Mohave as critical habitat for the federally listed endangered razorback sucker (Xyrauchen texanus), the significance of the sport fishery, and the regional importance of its habitats to more than 90 documented species of waterbirds. Potential threats to shoreline habitats are of concern not only for their ecosystem values but also for maintaining the recreational setting. Many areas adjacent to the shorelines of Lakes Mead and Mohave are designated wilderness areas. For purposes of this document, stressors are any chemical, biological, or physical agent that has a detrimental effect on aquatic ecosystems at the organism, population, or community level. Human-made stressors at Lakes Mead and Mohave include direct effects of recreation on the lakes, like boating and fishing, as well as indirect effects of activities away from the lakes, such as growing population and increasing urbanization. Common natural environmental stressors include extended changes in climate (precipitation or temperature), or the erosion, transport, and loading of chemical constituents in rocks and sediments to aquatic environments. Human activity also can exacerbate natural stressors in a variety of ways.

Circular↗
USGS2013

U.S. Geological Survey water science strategy—Observing, understanding, predicting, and delivering water science to the Nation

Executive Summary This report expands the Water Science Strategy that began with the USGS Science Strategy, “Facing Tomorrow’s Challenges—U.S. Geological Survey Science in the Decade 2007–2017” (U.S. Geological Survey, 2007). This report looks at the relevant issues facing society and develops a strategy built around observing, understanding, predicting, and delivering water science for the next 5 to 10 years by building new capabilities, tools, and delivery systems to meet the Nation’s water-resource needs. This report begins by presenting the vision of water science for the USGS and the societal issues that are influenced by, and in turn influence, the water resources of our Nation. The essence of the Water Science Strategy is built on the concept of “water availability,” defined as spatial and temporal distribution of water quantity and quality, as related to human and ecosystem needs, as affected by human and natural influences. The report also describes the core capabilities of the USGS in water science—the strengths, partnerships, and science integrity that the USGS has built over its 134-year history.

Circular↗
USGS2003

Natural and anthropogenic factors affecting the structure of the benthic macroinvertebrate community in an effluent-dominated reach of the Santa Cruz River, AZ

This study provides an assessment of the ecological conditions of a 46-km effluent-dominated stream section of the Santa Cruz River in the vicinity of the International Waste Water Treatment Plant, Nogales, AZ. We associated changes in the structure of the macroinvertebrate community to natural and anthropogenic chemical and physical variables using multivariate analysis. The analysis shows that biological criteria for effluent-dominated streams can be established using macroinvertebrate community attributes only with an understanding of the contribution of three classes of variables on the community structure: (1) low flow hydrological discharge as affected by groundwater withdrawals, treatment plant discharge, and subsurface geomorphology; (2) chemical composition of the treatment plant discharge and natural dilution; and (3) naturally produced floods resulting from seasonality of precipitation. ?? 2003 Elsevier Science Ltd. All rights reserved.

Ecological Indicators↗
USGS2019

Columbia Environmental Research Center

The U.S. Geological Survey Columbia Environmental Research Center performs research to solve challenging environmental problems related to contaminants and habitat alterations in aquatic and terrestrial ecosystems. The research is interdisciplinary and pursued through partnerships within the U.S. Geological Survey and with national, international, state, and local agencies; nongovernmental organizations; and universities. Research is prioritized to provide science to the U.S. Department of the Interior and other natural resource management agencies to inform rehabilitation of degraded habitats and imperiled fish and wildlife populations. The Columbia Environmental Research Center was established in 1966 in Columbia, Missouri, as the U.S. Fish and Wildlife Service’s Fish Pesticide Research Laboratory; the Columbia Environmental Research Center was incorporated into the U.S. Geological Survey in 1996. The U.S. Geological Survey’s staff of 130 includes 90 scientists of which one-half have advanced degrees in ecology, toxicology, biology, biochemistry, chemistry, hydrology, geology, and information technology.

Missouri↗
USGS2019

Updates to the Madison Lake (Minnesota) CE–QUAL–W2 water-quality model for assessing algal community dynamics

A previously developed CE–QUAL–W2 model for Madison Lake, Minnesota, simulated the algal community dynamics, water quality, and fish habitat suitability of Madison Lake under recent (2014) meteorological conditions. Additionally, this previously developed model simulated the complex interplay between external nutrient loading, internal nutrient loading from sediment release of phosphorus, and the organic matter decomposition of the algal biomass. However, the partitioning of Cyanophyta within the modeling framework was simplified to one group and did not account for how different Cyanophyta populations are affected by light conditions, use of nitrogen, temperature growth ranges, and differences in settling rates. Properly capturing Cyanophyta dynamics is important given the potential risks posed by potential large algal blooms. For example, when Cyanophyta form large blooms, recreational activities can become restricted in certain areas because of thick algal scums or algal mats, in addition to the possible production of a class of toxins, known as cyanotoxins, capable of threatening human health, domestic animals, and wildlife. Therefore, we updated the model to partition the Cyanophyta into a group that fixed nitrogen and a second, more buoyant Cyanophyta group that did not independently fix nitrogen. The U.S. Geological Survey, in cooperation with the St. Croix Watershed Research Station (Science Museum of Minnesota) with support from the Environmental and Natural Resources Trust Fund of Minnesota (Legislative-Citizen Commission on Minnesota Resources), updated the Madison Lake CE–QUAL–W2 model to address the shortcomings of simulating Cyanophyta in the previously developed model and better characterize Cyanophyta into two groups. In addition to updating the Cyanophyta group differentiation, the part of the model that handles the simulation of algal community dynamics was updated while preserving model predictive capabilities for nutrients, water temperature, and dissolved oxygen. The calibration and validation of the model was done under recent meteorological conditions with large and persistent Cyanophyta blooms (2014 and 2016). Overall, the model simulations predicted the persistently large total phosphorus concentrations in the hypolimnion of Madison Lake and key differences in nutrient concentrations between 2014 and 2016. The Cyanophyta bloom persistence throughout the summer was also simulated by the model in 2014 and 2016, a critical goal of the model update. Finally, monthly total phosphorus budgets were calculated for the updated Madison Lake model for 2014 and 2016.

Minnesota↗
USGS2025

Long Term Resource Monitoring procedures—Aquatic vegetation monitoring

This standard operating procedure (SOP) manual describes the collection of standardized, long-term data for aquatic vegetation communities in selected study pools of the Upper Mississippi River System in the United States. The primary intent of the data collection is to assess the status and trends that aid in understanding the unique river ecosystem and to guide large-scale ecological restoration of the river and its biological communities, like aquatic plants and their dependent wildlife. This SOP is an update to the version published in 2000 and reflects modifications to sample sizes and additions of new data collection procedures. All long-term monitoring programs and their SOPs must be adapted to changing conditions and be improved through learning, and this SOP clarifies procedures and adds new elements since the initial SOP was written more than 25 years ago. The SOP is intended for multiple audiences, including vegetation specialists through the Upper Mississippi River Restoration Program, data analysts using the publicly available data generated through this SOP, and natural resource managers and restoration practitioners who need data and science to guide some decisions. This SOP may be transferable and adaptable to other ecosystems when the aquatic plant community is the focus.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗
USGS2012

Rapid movement of frozen debris-lobes: implications for permafrost degradation and slope instability in the south-central Brooks Range, Alaska

We present the results of a reconnaissance investigation of unusual debris mass-movement features on permafrost slopes that pose a potential infrastructure hazard in the south-central Brooks Range, Alaska. For the purpose of this paper, we describe these features as frozen debris-lobes. We focus on the characterisation of frozen debris-lobes as indicators of various movement processes using ground-based surveys, remote sensing, field and laboratory measurements, and time-lapse observations of frozen debris-lobe systems along the Dalton Highway. Currently, some frozen debris-lobes exceed 100 m in width, 20 m in height and 1000 m in length. Our results indicate that frozen debris-lobes have responded to climate change by becoming increasingly active during the last decades, resulting in rapid downslope movement. Movement indicators observed in the field include toppling trees, slumps and scarps, detachment slides, striation marks on frozen sediment slabs, recently buried trees and other vegetation, mudflows, and large cracks in the lobe surface. The type and diversity of observed indicators suggest that the lobes likely consist of a frozen debris core, are subject to creep, and seasonally unfrozen surface sediment is transported in warm seasons by creep, slumping, viscous flow, blockfall and leaching of fines, and in cold seasons by creep and sliding of frozen sediment slabs. Ground-based measurements on one frozen debris-lobe over three years (2008–2010) revealed average movement rates of approximately 1 cm day −1 , which is substantially larger than rates measured in historic aerial photography from the 1950s to 1980s. We discuss how climate change may further influence frozen debris-lobe dynamics, potentially accelerating their movement. We highlight the potential direct hazard that one of the studied frozen debris-lobes may pose in the coming years and decades to the nearby Trans Alaska Pipeline System and the Dalton Highway, the main artery for transportation between Interior Alaska and the North Slope.

Alaska↗
USGS2014

Variable population exposure and distributed travel speeds in least-cost tsunami evacuation modelling

Evacuation of the population from a tsunami hazard zone is vital to reduce life-loss due to inundation. Geospatial least-cost distance modelling provides one approach to assessing tsunami evacuation potential. Previous models have generally used two static exposure scenarios and fixed travel speeds to represent population movement. Some analyses have assumed immediate departure or a common evacuation departure time for all exposed population. Here, a method is proposed to incorporate time-variable exposure, distributed travel speeds, and uncertain evacuation departure time into an existing anisotropic least-cost path distance framework. The method is demonstrated for hypothetical local-source tsunami evacuation in Napier City, Hawke's Bay, New Zealand. There is significant diurnal variation in pedestrian evacuation potential at the suburb level, although the total number of people unable to evacuate is stable across all scenarios. Whilst some fixed travel speeds approximate a distributed speed approach, others may overestimate evacuation potential. The impact of evacuation departure time is a significant contributor to total evacuation time. This method improves least-cost modelling of evacuation dynamics for evacuation planning, casualty modelling, and development of emergency response training scenarios. However, it requires detailed exposure data, which may preclude its use in many situations.

Natural Hazards and Earth System Sciences↗
USGS2015

On a report that the 2012 M 6.0 earthquake in Italy was predicted after seeing an unusual cloud formation

Several recently published reports have suggested that semi-stationary linear-cloud formations might be causally precursory to earthquakes. We examine the report of Guangmeng and Jie (2013), who claim to have predicted the 2012 M 6.0 earthquake in the Po Valley of northern Italy after seeing a satellite photograph (a digital image) showing a linear-cloud formation over the eastern Apennine Mountains of central Italy. From inspection of 4 years of satellite images we find numerous examples of linear-cloud formations over Italy. A simple test shows no obvious statistical relationship between the occurrence of these cloud formations and earthquakes that occurred in and around Italy. All of the linear-cloud formations we have identified in satellite images, including that which Guangmeng and Jie (2013) claim to have used to predict the 2012 earthquake, appear to be orographic – formed by the interaction of moisture-laden wind flowing over mountains. Guangmeng and Jie (2013) have not clearly stated how linear-cloud formations can be used to predict the size, location, and time of an earthquake, and they have not published an account of all of their predictions (including any unsuccessful predictions). We are skeptical of the validity of the claim by Guangmeng and Jie (2013) that they have managed to predict any earthquakes.

Natural Hazards and Earth System Sciences↗
USGS2015

Discussion and Review of “Seismology of the Oso-Steelhead landslide” by Hibert, Stark, and Ekstrom

In this study, Hibert et al. use the seismic waves generated by the disastrous 22 March 2014 landslide near Oso, Washington to investigate the dynamics of the event by combining long-period source inversions and short period analysis. Overall the paper is concise and well written and takes an innovative approach by integrating both high- and low-frequency seismic data together in the interpretation. It also demonstrates the useful prospect of resolving a trajectory of the center of mass of the landslide from a few traces of noisy seismic data. However, the main point of discussion that I would like to address stems from differences between the findings by Hibert et al. and the findings of a recently published multidisciplinary paper on the Oso landslide (Iverson et al., 2015). I am a co-author on the paper and performed the seismic analysis, which was similar to that of Hibert et al., yet the interpretation of how the event unfolded differs significantly between the two papers. In my opinion, this stems largely from a difference in information, some of which Hibert et al. unfortunately did not have access to prior to submission because it was just published in Iverson et al. (2015). However, differences also seem to stem from technical details of the methods and data used. Typically, such details are only of interest to seismologists, but in this case they make a substantial difference in understanding how the event unfolded and how it became so disastrous. For that reason, they are worthy of discussion here. The crux of the difference lies in two points 1) Iverson et al. (2015) found a multi-stage initiation of the main event, while Hibert et al. did not and 2) Iverson et al. (2015) found the second high-frequency event to be from a significantly smaller debris fall whereas Hibert et al. estimate it to be a substantial mass. I detail these two points below, then follow with an additional comment regarding the trajectory estimation made by Hibert et al. and some minor comments.

Natural Hazards and Earth System Sciences↗
USGS2015

Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: Implications for flooding and drought

We evaluated the spatial and seasonal responses of precipitation in the Ganges and Brahmaputra basins as modulated by the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) modes using Global Precipitation Climatology Centre (GPCC) full data reanalysis of monthly global land-surface precipitation data from 1901 to 2010 with a spatial resolution of 0.5° × 0.5°. The GPCC monthly total precipitation climatology targeting the period 1951–2000 was used to compute gridded monthly anomalies for the entire time period. The gridded monthly anomalies were averaged for the years influenced by combinations of climate modes. Occurrences of El Niño alone significantly reduce (88% of the long-term average (LTA)) precipitation during the monsoon months in the western and southeastern Ganges Basin. In contrast, occurrences of La Niña and co-occurrences of La Niña and negative IOD events significantly enhance (110 and 109% of LTA in the Ganges and Brahmaputra Basin, respectively) precipitation across both basins. When El Niño co-occurs with positive IOD events, the impacts of El Niño on the basins' precipitation diminishes. When there is no active ENSO or IOD events (occurring in 41 out of 110 years), precipitation remains below average (95% of LTA) in the agriculturally intensive areas of Haryana, Uttar Pradesh, Rajasthan, Madhya Pradesh, and Western Nepal in the Ganges Basin, whereas precipitation remains average to above average (104% of LTA) across the Brahmaputra Basin. This pattern implies that a regular water deficit is likely, especially in the Ganges Basin, with implications for the agriculture sector due to its reliance on consistent rainfall for successful production. Historically, major droughts occurred during El Niño and co-occurrences of El Niño and positive IOD events, while major flooding occurred during La Niña and co-occurrences of La Niña and negative IOD events in the basins. This observational analysis will facilitate well-informed decision making in minimizing natural hazard risks and climate impacts on agriculture, and supports development of strategies ensuring optimized use of water resources in best management practice under a changing climate.

Brahmaputra Basin, Ganges Basin↗
USGS2019

Spatial distribution of water level impact to back-barrier bays

Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70-100%) and tidal frequencies (10-55%). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

Natural Hazards and Earth System Sciences↗
USGS2021

A regional spatio-temporal analysis of large magnitude snow avalanches using tree rings

Snow avalanches affect transportation corridors and settlements worldwide. In many mountainous regions, robust records of avalanche frequency and magnitude are sparse or non-existent. However, dendrochronological methods can be used to fill this gap and infer historical avalanche patterns. In this study, we developed a tree-ring-based avalanche chronology for large magnitude avalanche events (size ≥ ∼ D 3 "> ≥ ∼ D 3 ) using dendrochronological techniques for a portion of the US northern Rocky Mountains. We used a strategic sampling design to examine avalanche activity through time and across nested spatial scales (i.e., from individual paths, four distinct subregions, and the region). We analyzed 673 samples in total from 647 suitable trees collected from 12 avalanche paths from which 2134 growth disturbances were identified over the years 1636 to 2017 CE. Using existing indexing approaches, we developed a regional avalanche activity index to discriminate avalanche events from noise in the tree-ring record. Large magnitude avalanches, common across the region, occurred in 30 individual years and exhibited a median return interval of approximately 3 years (mean = 5.21 years). The median large magnitude avalanche return interval (3–8 years) and the total number of avalanche years (12–18) varies throughout the four subregions, suggesting the important influence of local terrain and weather factors. We tested subsampling routines for regional representation, finding that sampling 8 random paths out of a total of 12 avalanche paths in the region captures up to 83 % of the regional chronology, whereas four paths capture only 43 % to 73 %. The greatest value probability of detection for any given path in our dataset is 40 %, suggesting that sampling a single path would capture no more than 40 % of the regional avalanche activity. Results emphasize the importance of sample size, scale, and spatial extent when attempting to derive a regional large magnitude avalanche event chronology from tree-ring records.

Montana↗
USGS2023

The influence of large woody debris on post-wildfire debris flow sediment storage

Debris flows transport large quantities of water and granular material, such as sediment and wood, and this mixture can have devastating impacts on life and infrastructure. The proportion of large woody debris (LWD) incorporated into debris flows can be enhanced in forested areas recently burned by wildfire, because wood recruitment into channels accelerates in burned forests. In this study, we explored how LWD transported in post-wildfire debris flows acts to retain sediment within small headwater channels. We found that the largest amount of debris flow sediment is stored in channel reaches with a ratio of LWD length to channel width between 0.25 and 1. Moreover, the largest debris flow deposits forced by LWD were found in low-angle channel reaches. We examined two models for debris flow volume estimation: (1) the current volume prediction model used in U.S. Geological Survey debris flow hazard assessments, and (2) a regional model developed to predict the sediment yield associated with debris-laden flows. We found that the regional model better matched the magnitude of the observed sediment at the terminal fan, suggesting the utility of regionally calibrated parameters for debris flow volume prediction. However, large wood created sediment storage upstream of the terminal fan, and this volume was of the same magnitude as the total volume at the terminal fans. Finally, we demonstrate a method for estimating debris flow velocity based on estimates of the critical velocity required to break wood, which can be used in future field studies to estimate minimum debris flow velocity values.

New Mexico↗
USGS2023

Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system ( https://sitkalandslide.org/ , last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

Alaska↗
USGS2024

Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling

Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional ( > 100 km 2 ) landslide studies remains limited due, in part, to the large computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units that leverages tools from within TauDEM and GRASS, using an R interface. The algorithm uses geomorphic laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the often ambiguous determination of slope unit size. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility and event-specific landslide occurrence maps.

Natural Hazards and Earth Systems Sciences (NHESS)↗
USGS2024

Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA

Moderate- or high-severity fires promote increases in runoff and erosion, leading to a greater likelihood of extreme geomorphic responses, including debris flows. In the first several years following fire, the majority of debris flows initiate when runoff rapidly entrains sediment on steep slopes. From a hazard perspective, it is important to be able to anticipate when and where watershed responses will be dominated by debris flows rather than flood flows. Rainfall intensity averaged over a 15 min duration, I 15 , in particular, has been identified as a key predictor of debris flow likelihood. Developing effective warning systems and predictive models for post-fire debris flow hazards therefore relies on high-temporal resolution rainfall data at the time debris flows initiate. In this study, we documented the geomorphic response of a series of watersheds following a wildfire in western New Mexico, USA, with an emphasis on constraining debris flow timing within rainstorms to better characterize debris-flow-triggering rainfall intensities. We estimated temporal changes in soil hydraulic properties and ground cover in areas burned at different severities over >2 years to offer explanations for observed differences in spatial and temporal patterns in debris flow activity. We observed 16 debris flows, all of which initiated during the first several months following the fire. The average recurrence interval of the debris-flow-triggering I 15 is 1.3 years, which highlights the susceptibility of recently burned watersheds to runoff-generated debris flows in this region. All but one of the debris flows initiated in watersheds burned primarily at moderate or high soil burn severity. Since soil hydraulic properties appeared to be relatively resilient to burning, we attribute reduced debris flow activity at later times to decreases in the fraction of bare ground. Results provide additional constraints on the rainfall characteristics that promote post-fire debris flow initiation in a region where fire size and severity have been increasing.

New Mexico↗
USGS2024

Evaluation of debris-flow building damage forecasts

Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building damage forecasts for the 9 January 2018 Montecito postfire debris-flow runout event, in which over 500 buildings were damaged. We constructed forecasts using either peak debris-flow depth or momentum flux as the hazard intensity measure and applied each approach using three debris-flow runout models (RAMMS, FLO-2D, and D-Claw). Generated forecasts were based on averaging multiple simulations that sampled a range of debris-flow volume and mobility, reflecting typical sources and magnitude of pre-event uncertainty. We found that only forecasts made with momentum flux and the D-Claw model could correctly predict the observed number of damaged buildings and the spatial patterns of building damage. However, the best forecast only predicted 50 % of the observed damaged buildings correctly and had coherent spatial patterns of incorrectly predicted building damage (i.e., false positives and false negatives). These results indicate that forecasts made at the building level reliably reflect the spatial pattern of damage but do not support interpretation at the individual building level. We found the event size strongly influences the number of damaged buildings and the spatial pattern of debris-flow depth and velocity. Consequently, future research on the link between precipitation and the volume of sediment mobilized may have the greatest effect on reducing uncertainty in building damage forecasts. Finally, because we found that both depth and velocity are needed to predict building damage, comparing debris-flow models against spatially distributed observations of building damage is a more stringent test for model fidelity than comparison against the extent of debris-flow runout.

Natural Hazards and Earth System Sciences↗