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Research about western United States

Source-linked reports with geographic coverage including western United States.

At least 55 records · Page 3Linked to original sources
USGS2022

A fault‐based crustal deformation model with deep driven dislocation sources for the 2023 update to the U.S. National Seismic Hazard Model

A fault‐based crustal deformation model with deep driven dislocation sources is applied to estimate long‐term on‐fault slip rates and off‐fault moment rate distribution in the western United States (WUS) for the 2023 update to the National Seismic Hazard Model (NSHM). This model uses the method of Zeng and Shen (2017) to invert for slip rate and strain‐rate parameters based on inputs from Global Positioning System (GPS) velocities and geologic slip‐rate constraints. The model connects adjacent major fault segments in California and the Cascadia subduction zone to form blocks that extend to the boundaries of the study area. Faults within the blocks are obtained from the NSHM geologic fault section database. The model slip rates are determined using a least‐squares inversion with a normalized chi‐square of 6.6. I also apply a time‐dependent correction called “ghost transient” effect to account for the viscoelastic responses from large historic earthquakes along the San Andreas fault and Cascadia subduction zone. Major discrepancies between model slip rates and geologic slip rates along the San Andreas fault, for example, from the Cholame to the Mojave and San Bernardino segments of the San Andreas, are well reduced after the ghost transient correction is applied to GPS velocities. The off‐fault moment rate distribution is consistent with regional tectonics and seismicity patterns with a total rate of 1.6 × 10 19    N · m / yr "> 1.6 × 10 19 N ⋅ m / yr for the WUS.

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USGS2022

The effects of cheatgrass invasion on US Great Basin carbon storage depend on interactions between plant community composition, precipitation seasonality, and soil climate regime

Annual-grass invasions are transforming desert ecosystems in ways that affect ecosystem carbon (C) balance, but previous studies do not agree on the pattern, magnitude and direction of changes. A recent meta-analysis of 41 articles and 386 sites concludes that invasion by annual grasses such as cheatgrass ( Bromus tectorum L) reduces C in biomass across the Great Basin (Nagy et al., 2021). Reanalysis reveals that whether cheatgrass affects biomass C stocks is not generalizable, but rather depends on the considerable variation in climate across the subject sites. Our analysis suggests that accurate Great Basin-scale estimates of cheatgrass effects on C balance are not yet possible. Addition of climate variables to the meta-analysis reveals that cheatgrass invasion (a) reduced C in above-ground biomass in relatively summer-wet sites but not in summer-dry sites, (b) increased surface soil C in sites with intermediate resistance and resilience classifications (R&R) but not in low R&R sites—that is, mesic/aridic soil climates and (c) did not affect deep soil C. Considering that cheatgrass has expanded most in relatively summer-dry sites and mesic/aridic sites, omission of climate factors leads to model overestimates of cheatgrass effects on C when extrapolating to larger areas. Estimates of cheatgrass effects on C would also be improved if the analysis considered that (a) perennial grasslands are a common community state in the Great Basin that have intermediary C relative to annual grasslands and sagebrush stands, that is the omission of perennial grasslands from analysis inflates the baseline C storage of uninvaded Great Basin ecosystems, and( b) cheatgrass does not often exist in stable monocultures and soil carbon can reflect current or recent presence of other species. Synthesis and applications . Invasions often reveal heterogeneity in ecosystem structure and function that is not otherwise evident, and the heterogeneity can influence estimation of the net impacts of the invaders. For cheatgrass and other invaders, we propose that formally accounting for the spatial variability of invasion on ecosystem functions will improve the estimation of their net effect on ecosystem C, and thus improve prospects for adjusting management practices to optimize C sequestration.

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USGS2022

A sagebrush conservation design to proactively restore America’s sagebrush biome

A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time. First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term. A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.

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USGS2022

Ungulate migrations of the western United States, volume 2

Migration is widespread across taxonomic groups and increasingly recognized as fundamental to maintaining abundant wildlife populations and communities. Many ungulate herds migrate across the western United States to access food and avoid harsh environmental conditions. With the advent of global positioning system (GPS) collars, researchers can describe and map the year-round movements of ungulates at both large and small spatial scales. The migrations can traverse landscapes that are a mix of different jurisdictional ownership and management. Today, the landscapes migrating herds traverse are increasingly threatened by fencing, high-traffic roads, oil and gas development, and other types of permanent development. Through the use of GPS collars, a model of science-based conservation emerged in which migration corridors, stopovers, and winter ranges can be mapped in detail, thereby allowing threats and conservation opportunities to be identified and remedied. In 2018, the U.S. Geological Survey (USGS) assembled a Corridor Mapping Team (CMT) to work collaboratively with western states to map migrations of Odocoileus hemionus (mule deer), Cervus canadensis (elk), and Antilocapra americana (pronghorn). Led by the USGS Wyoming Cooperative Fish and Wildlife Research Unit, the team consists of Federal scientists, university researchers, and biologists and analysts from participating State and Tribal agencies. The first set of maps described a total of 42 migrations across 5 western states and was published in 2020 as the first volume of this report series. This second volume describes an additional 65 migrations mapped within 9 western states and select Tribal lands. As the western United States continues to grow, this report series and the associated map files released by the USGS will allow for migration maps to be used for conservation planning by a wide array of State and Federal stakeholders to reduce barriers to migration caused by fences, roads, and other development.

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USGS2022

Compendium to invasive annual grass spatial products for the western United States, January 2010-February 2021

Invasive annual grasses (IAGs) degrade native plant communities, alter fire cycles, impact ecosystem processes, and threaten the persistence of some species. Therefore, controlling the spread of IAGs has become a land management priority in the western United States. A wide array of geospatial data has been developed in the last decade to help land managers combat the invasion and expansion of non-native grasses by identifying areas where these species are likely to occur. However, choosing the most appropriate spatial product to address specific management concerns is a daunting task for many land managers, particularly with the rapid increase in the number of IAG spatial products available. To aid potential users in assessing these products, we reviewed and summarized 23 datasets that captured the three IAG species of most concern to rangeland management— Bromus tectorum (cheatgrass), Taeniatherum caput-medusae (medusahead), and Ventenata dubia (ventenata). To be included in this review, products were required to include part of the western United States, be regional or National in scale, and have been published between January 2010 and February 2021. This review, part of a series of informational data resources, is the compendium to an Excel-readable database and provides a 2-page summary of each spatial data product to assist land managers in understanding and selecting the best available spatial data for their management needs.

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USGS2022

A user guide to selecting invasive annual grass spatial products for the western United States

Invasive annual grasses (IAGs)—including Bromus tectorum (cheatgrass), Taeniatherum caput-medusae (medusahead), and Ventenata dubia (ventenata) species—present significant challenges for rangeland management by altering plant communities, impacting ecosystem function, reducing forage for wildlife and livestock, and increasing fire risk. Numerous spatial data products are used to map IAGs, and understanding the similarities, differences, and potential tradeoffs among these products is key to selecting the right maps for specific applications. This short guide outlines considerations for selecting regional- and national-scale spatial data to support the management of IAGs.

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USGS2022

Age-specific survival rates, causes of death, and allowable take of golden eagles in the western United States

In the United States, the Bald and Golden Eagle Protection Act prohibits take of golden eagles ( Aquila chrysaetos ) unless authorized by permit, and stipulates that all permitted take must be sustainable. Golden eagles are unintentionally killed in conjunction with many lawful activities (e.g., electrocution on power poles, collision with wind turbines). Managers who issue permits for incidental take of golden eagles must determine allowable take levels and manage permitted take accordingly. To aid managers in making these decisions in the western United States, we used an integrated population model to obtain estimates of golden eagle vital rates and population size, and then used those estimates in a prescribed take level (PTL) model to estimate the allowable take level. Estimated mean annual survival rates for golden eagles ranged from 0.70 (95% credible interval = 0.66–0.74) for first-year birds to 0.90 (0.88–0.91) for adults. Models suggested a high proportion of adult female golden eagles attempted to breed and breeding pairs fledged a mean of 0.53 (0.39–0.72) young annually. Population size in the coterminous western United States has averaged ~31,800 individuals for several decades, with λ = 1.0 (0.96–1.05). The PTL model estimated a median allowable take limit of ~2227 (708–4182) individuals annually given a management objective of maintaining a stable population. We estimate that take averaged 2572 out of 4373 (59%) deaths annually, based on a representative sample of transmitter-tagged golden eagles. For the subset of golden eagles that were recovered and a cause of death determined, anthropogenic mortality accounted for an average of 74% of deaths after their first year; leading forms of take over all age classes were shooting (~670 per year), collisions (~611), electrocutions (~506), and poisoning (~427). Although observed take overlapped the credible interval of our allowable take estimate and the population overall has been stable, our findings indicate that additional take, unless mitigated for, may not be sustainable. Our analysis demonstrates the utility of the joint application of integrated population and prescribed take level models to management of incidental take of a protected species.

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USGS2022

Multi-species inference of exotic annual and native perennial grasses in rangelands of the western United States using Harmonized Landsat and Sentinel-2 data

The invasion of exotic annual grass (EAG), e.g., cheatgrass ( Bromus tectorum ) and medusahead ( Taeniatherum caput-medusae ), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early stages of invasion or growth, can be problematic because of overlapping controls and similar phenological characteristics among native and other exotic vegetation. Subsequently, refining and developing tools capable of quantifying the abundance and phenology of annual and perennial grass species would be beneficial to help inform conservation and management efforts at local to regional scales. Here, we deploy an enhanced version of the U.S. Geological Survey Rangeland Exotic Plant Monitoring System to develop timely and accurate maps of annual (2016–2020) and intra-annual (May 2021 and July 2021) abundances of exotic annual and perennial grass species throughout the rangelands of the western United States. This monitoring system leverages field observations and remote-sensing data with artificial intelligence/machine learning to rapidly produce annual and early season estimates of species abundances at a 30-m spatial resolution. We introduce a fully automated and multi-task deep-learning framework to simultaneously predict and generate weekly, near-seamless composites of Harmonized Landsat Sentinel-2 spectral data. These data, along with auxiliary datasets and time series metrics, are incorporated into an ensemble of independent XGBoost models. This study demonstrates that inclusion of the Normalized Difference Vegetation Index and Normalized Difference Wetness Index time-series data generated from our deep-learning framework enables near real-time and accurate mapping of EAG (Median Absolute Error (MdAE): 3.22, 2.72, and 0.02; and correlation coefficient (r): 0.82, 0.81, and 0.73; respectively for EAG, cheatgrass, and medusahead) and native perennial grass abundance (MdAE: 2.51, r:0.72 for Sandberg bluegrass ( Poa secunda )). Our approach and the resulting data provide insights into rangeland grass dynamics, which will be useful for applications, such as fire and drought monitoring, habitat suitability mapping, as well as land-cover and land-change modelling. Spatially explicit, timely, and accurate species-specific abundance datasets provide invaluable information to land managers.

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USGS2022

An initial assessment of plankton tow detection probabilities for dreissenid mussels in the western United States

Early detection of dreissenid mussels ( Dreissena polymorpha and D. rostriformis bugensis ) is crucial to mitigating the economic and environmental impacts of an infestation. Plankton tow sampling is a common method used for early detection of dreissenid mussels, but little is known about the sampling intensity required for a high probability of early detection using the method. We used implicit dynamic occupancy models to estimate plankton tow detection probabilities of dreissenid mussels from a long-term data set containing plankton tow samples collected across central and western United States. We fit models using a) the entire data set, including water bodies with unknown occupancy status in addition to heavily infested water bodies, b) a data subset that included water bodies with paired water temperature data, and c) a data subset that included water bodies with lower dreissenid densities. For the entire data set, we found that estimated detection probabilities varied by water body size and ranged from approximately 0.10 to 0.86. For the water temperature subset, we observed the same pattern between detection probability and water body size as we did for the full data but additionally found that the estimated detection probabilities were much higher when water temperatures were above 12 °C. For the lower dreissenid density subset, we found that the estimated probability of detecting dreissenid mussels with a single aggregated plankton tow sample was near zero. Given these estimates, we conclude that the number of aggregated plankton tow samples taken per water body in the data is far fewer than the number needed to ensure a high probability of detecting dreissenid mussels, especially if they are at low densities. We summarize the analyses with a discussion of plankton tow sampling protocol changes needed to improve estimates of dreissenid detection probabilities.

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USGS2022

Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized weight-of-evidence and logistic regression methods to estimate resource favorability, but these analyses relied upon some expert decisions. While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also introduce human bias into assessments. This bias presents a source of error that may affect the performance of the models and resulting resource estimates. Our study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment. The six new favorability maps required far less expert decision-making, but broadly agree with the previous assessment. Despite the fact that the 2008 assessment results employed linear methods, the non-linear machine learning algorithms (i.e., support-vector machines and XGBoost) produced greater agreement with the previous assessment than the linear machine learning algorithm (i.e., logistic regression). It is not surprising that geothermal systems depend on non-linear combinations of features, and we postulate that the expert decisions during the 2008 assessment accounted for system non-linearities. Substantial challenges to applying machine learning algorithms to predict geothermal resource favorability include severe class imbalance (i.e., there are very few known geothermal systems compared to the large area considered), and while there are known geothermal systems (i.e., positive labels), all other sites have an unknown status (i.e., they are unlabeled), instead of receiving a negative label (i.e., the known/proven absence of a geothermal resource). We address both challenges through a custom undersampling strategy that can be used with any algorithm and then evaluated using F1 scores.

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USGS2021

OpenET: Filling a critical data gap in water management for the western United States

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

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USGS2021

Ecological potential fractional component cover based on Long-Term satellite observations across the western United States

Rangelands have immense inherent spatial and temporal variability, yet land condition and trends are often assessed at a limited number of spatially “representative” points. Spatially comprehensive, and quantitative, Ecological Potential (EP) data provide a baseline for comparison to current rangeland vegetation conditions and trends. Here, we define EP as potential fractional cover (bare ground, herbaceous, litter, shrub, and sagebrush) represented in the least disturbed areas and most productive years of the Landsat satellite archive (1985-present) for each 30-m pixel. We produce EP maps across rangelands in the western United States by training regression tree models using Rangeland Condition Monitoring Assessment and Projection (RCMAP) time-series fractional cover maps in ecologically intact sites (with limited annual herbaceous cover, no recent disturbance or vegetation treatment, and less bare ground cover than expected). As independent predictor variables in these models, we use digital soils and topography data and six bimonthly composites of the 90th percentile of Normalized Difference Vegetation Index (NDVI) and associated spectral bands from the 1985–2020 Landsat archive. EP predictions were successful in capturing biophysical gradients present in the independent variables and depicting potential cover in the absence of disturbance; we found no influence of fires or land treatments in the data. Next, we compared EP to contemporary (2018) cover, to create departure maps that can be used as a screening tool indicating degradation and providing an early warning of vegetation state change. Finally, we used a dichotomous key to convert the 1985 and 2018 RCMAP cover and EP cover into vegetation states important to land management decisions (invaded sagebrush steppe , annual grasslands, etc.). We found that in 1985, 21.2% of the study area had a different vegetation state than EP, and this percentage increased to 24.2% by 2018. More than 50% of the EP native sagebrush steppe was converted to an annual grassland, perennial grassland, or non-sagebrush shrub by 2018, and an additional 7% was classified as invaded sagebrush steppe, at risk of transition to another state. EP products provide a spatio-temporal reference of vegetation conditions from the last three decades across rangelands in the western United States. Use of the EP reference can improve adaptive management practice by providing monitoring and control data, which are often lacking, and assist in differentiating treatment effect from confounding factors.

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USGS2017

Leaching of plutonium from a radioactive waste glass by eight groundwaters from the western United States

The leachability of a radioactive waste glass formulated to Battelle Pacific Northwest Laboratory specification 80-270 has been studied using eight actual groundwaters with a range of chemical compositions as leachants. Waters collected from the Grande Ronde Basalt (Washington State) and from alluvial deposits in the Hualapai Valley (Arizona) were the most effective at removing plutonium from this glass. Leaching was shown to be incongruent; plutonium was removed from the glass more slowly than the overall glass matrix. The results of these experiments indicate the need to study the leachability of actual waste forms using the actual projected groundwaters that are most likely to come into contact with the waste should a radioactive waste repository be breached.

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USGS2011

Proposed standard weight (W(s)) equations for interior cutthroat trout

We developed standard weight (W(s); length-specific standard weight for the species) equations for inland cutthroat trout Oncorhynchus clarki using the regression-line-percentile technique. Length and weight data from samples of 117 cutthroat trout populations (48 lentic and 69 lotic) over the interior range of the species were used. Separate W(s) equations were developed for lentic and lotic populations, as well as an overall equation. Relative weight (W(r); individual weight/W(s)) values did not change systematically with increasing fish length. No significant differences in mean W(r) were found among subspecies of cutthroat trout. Differences between lotic and lentic populations suggested the need for two separate equations.

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USGS2005

Understanding the spatial heterogeneity of global environmental change in mountain regions

One of the challenges for global environmental change research is to understand how future climate changes will be expressed in mountain regions. The physiographic complexity of mountains creates environments that can be highly variable over relatively short distances. This spatial heterogeneity reflects a hierarchy of environmental controls. At regional scales, insolation and atmospheric circulation features determine the dominant regional climate patterns that affect mountain regions. At finer spatial scales, substrate, aspect, elevation, and a number of other environmental factors influence ecosystem dynamics. Vegetation, for example, is affected by all levels of this hierarchy, from regional-scale climate regimes down to site-specific features, such as substrate type (cf. Körner, this volume).

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USGS2005

Water availability for the Western United States--Key scientific challenges

In the Western United States, the availability of water has become a serious concern for many communities and rural areas. Near population centers, surface-water supplies are fully appropriated, and many communities are dependent upon ground water drawn from storage, which is an unsustainable strategy. Water of acceptable quality is increasingly hard to find because local sources are allocated to prior uses, depleted by overpumping, or diminished by drought stress. Some of the inherent characteristics of the West add complexity to the task of securing water supplies. The Western States, including the arid Southwest, have the most rapid population growth in the United States. The climate varies widely in the West, but it is best known for its low precipitation, aridity, and drought. There is evidence that the climate is warming, which will have consequences for Western water supplies, such as increased minimum streamflow and earlier snowmelt events in snow-dominated basins. The potential for departures from average climatic conditions threatens to disrupt society and local to regional economies. The appropriative rights doctrine governs the management of water in most Western States, although some aspects of the riparian doctrine are being incorporated. The 'use it or lose it' provisions of Western water law discourage conservation and make the reallocation of water to instream environmental uses more difficult. The hydrologic sciences have defined the interconnectedness of ground water and surface water, yet these resources are still administered separately by most States. The definition of water availability has been expanded to include sustaining riparian ecosystems and individual endangered species, which are disproportionately represented in the Western States. Federal reserved rights, common in the West because of the large amount of Federal land, exist with quite senior priority dates whether or not water is currently being used. A major challenge for water users in the West is that these reserved rights may supersede other existing users. The minimum amount of water required, however, to sustain native peoples, a riparian system, or an endangered species eventually will need to be known in order to manage the available water supply. Periodic inventory and assessment of the amounts and trends of water available in surface water and ground water are needed to support water management. There is a widespread perception that the amount of available water is diminishing with time. This and other perceptions about water availability should be replaced by objective data and analysis. Some data are presented here for the major Western rivers that show that flows are not decreasing in most streams and rivers in the West. Systematic information is lacking to make broad assessments of ground-water availability, but available data for specific aquifers indicate that these aquifers are being depleted, especially near population centers. The complexity added to the issue of Western water availability by these and other factors gives rise to a significant role of science. Science has played a role in support of Western water development from the beginning, and the role has evolved and changed over time as society's values have changed. In this report, the role of science is discussed in three phases: (1) development and construction, (2) consequences and environmental awareness, and (3) sustainability. The development and construction phase includes some historical accounting of water development in the West and shows how some precedents set in those early days are still applied today.

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USGS2005

Compilation of data relating to the erosive response of 608 recently-burned basins in the western United States

This report presents a compilation of data on the erosive response, debris-flow initiation processes, basin morphology, burn severity, event-triggering rainfall, rock type, and soils for 608 basins recently burned by 53 fires located throughout the Western United States. The data presented here are a combination of those collected during our own field research and those reported in the literature. In some cases, data from a Geographic Information System (GIS) and Digital Elevation Models (DEMs) were used to supplement the data from the primary source. Due to gaps in the information available, not all parameters are characterized for all basins. This database provides a resource for researchers and land managers interested in examining relations between the runoff response of recently burned basins and their morphology, burn severity, soils and rock type, and triggering rainfall. The purpose of this compilation is to provide a single resource for future studies addressing problems associated with wildfire-related erosion. For example, data in this compilation have been used to develop a model for debris flow probability from recently burned basins using logistic multiple regression analysis (Cannon and others, 2004). This database provides a convenient starting point for other studies. For additional information on estimated post-fire runoff peak discharges and debris-flow volumes, see Gartner and others (2004).

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