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At least 505 records · Page 28Linked to original sources
USGS2013

Hurricane Sandy science plan: impacts to coastal ecosystems, habitats, and fish and wildlife

Hurricane Sandy devastated some of the most heavily populated eastern coastal areas of the Nation. With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes: • Coastal topography and bathymetry • Impacts to coastal beaches and barriers • Impacts of storm surge, including disturbed estuarine and bay hydrology • Impacts on environmental quality and persisting contaminant exposures • Impacts to coastal ecosystems, habitats, and fish and wildlife This fact sheet focuses on impacts to coastal ecosystems, habitats, and fish and wildlife.

Fact Sheet↗
USGS2013

Hurricane Sandy science plan: coastal topographic and bathymetric data to support hurricane impact assessment and response

Hurricane Sandy devastated some of the most heavily populated eastern coastal areas of the Nation. With a storm surge peaking at more than 19 feet, the powerful landscape-altering destruction of Hurricane Sandy is a stark reminder of why the Nation must become more resilient to coastal hazards. In response to this natural disaster, the U.S. Geological Survey (USGS) received a total of $41.2 million in supplemental appropriations from the Department of the Interior (DOI) to support response, recovery, and rebuilding efforts. These funds support a science plan that will provide critical scientific information necessary to inform management decisions for recovery of coastal communities, and aid in preparation for future natural hazards. This science plan is designed to coordinate continuing USGS activities with stakeholders and other agencies to improve data collection and analysis that will guide recovery and restoration efforts. The science plan is split into five distinct themes: • Coastal topography and bathymetry • Impacts to coastal beaches and barriers • Impacts of storm surge, including disturbed estuarine and bay hydrology • Impacts on environmental quality and persisting contaminant exposures • Impacts to coastal ecosystems, habitats, and fish and wildlife This fact sheet focuses on coastal topography and bathymetry. This fact sheet focuses on coastal topography and bathymetry.

Gulf Coast region↗
USGS2014

Linkage of the Soil and Water Assessment Tool and the Texas Water Availability Model to simulate the effects of brush management on monthly storage of Canyon Lake, south-central Texas, 1995-2010

The U.S. Geological Survey (USGS), in cooperation with the Texas State Soil and Water Conservation Board, developed and applied an approach to create a linkage between the published upper Guadalupe River Soil Water Assessment Tool (SWAT) brush-management (ashe juniper [ Juniperus ashei ]) model and the full authorization version Guadalupe River Water Availability Model (WAM). The SWAT model was published by the USGS, and the Guadalupe River WAM is available from the Texas Commission on Environmental Quality. The upper Guadalupe River watershed is a substantial component of the Guadalupe River WAM. This report serves in part as documentation of a proof of concept on the feasibility of linking these two water-resources planning models for the purpose of simulating possible increases in water storage in Canyon Lake as a result of different brush-management scenarios. The SWAT-WAM linkage for the upper Guadalupe River is documented with a principal objective to evaluate the distributional characteristics of the monthly water storage of Canyon Lake during selected drought conditions. Focus is on the relative evaluation of select scenarios of large-scale or “extensive” brush management within the upper Guadalupe River watershed. There are six SWAT simulations for the upper Guadalupe River watershed that include a baseline (0-percent management of treatable ashe juniper, the baseline scenario from a previous study in which no percentage of ashe juniper is numerically replaced with grassland) along with five scenarios (extensions of SWAT simulations from a previous study) of 20-, 40-, 60-, 80-, and 100-percent random (numerical) replacement of treatable ashe juniper with grasslands throughout the upper Guadalupe River watershed in south-central Texas. SWAT is a process-based, semidistributed, water-balance model designed to predict the effects of landscape management decisions on water yields. A watershed is subdivided into subbasins, and each subbasin is associated with a single reach on the stream network. In general a WAM, such as the Guadalupe River WAM, provides analysis of generalized water rights in a river and reservoir framework. A WAM accommodates hydrology and water usage through several input files containing water rights, watershed parameters, and naturalized streamflow time series. A WAM is generalized for application to rivers and reservoir systems, and input datasets are uniquely developed for a river basin of concern. The extractions of SWAT output for the five extensive brush-management and baseline scenarios were offset by –21 years and, in general, the results were then mapped to the WAM input-flow file. The offset of –21 years was chosen arbitrarily for technical reasons and means that the period of monthly record 1995–2010 of the upper Guadalupe River SWAT became the synthetic period of monthly record 1974–89, hereinafter 1974–89 (synthetic) period, of the Guadalupe River WAM. The relative (between scenario to baseline) effects of extensive brush-management scenarios by using the SWAT-WAM linkage were evaluated, and two critical intermediate results were total inflow to Canyon Lake from 1995 to 2010 and the monthly storage of Canyon Lake from 1974 to 1989 (synthetic). The first quartile or lower 25th percentile of monthly storage of Canyon Lake for the baseline scenario is 381,000 acre-feet (acre-ft) for the hereinafter 1974–89 (synthetic) period. This lower quartile was chosen for analysis for two critical purposes. First, Canyon Lake is managed with a conservation pool of about 386,200 acre-ft capacity (as recognized by the WAM) and is at or near conservation capacity about 50 percent or more of the time; further, there is intrinsic data censoring that occurs for the monthly storage distribution because Canyon Lake is at or near conservation pool elevation the majority of the time. This intrinsic censoring has the effect of creating a bounded distribution with a left or low-volume tail. Statistical assessment of the brush-management scenarios beginning with the 381,000 acre-ft censoring threshold provides readily interpretable results. Second, the quantification of brush management during periods lacking abundant rainfall, which were defined in this study as months for which Canyon Lake storage was below the 25th percentile for the simulation period, are of substantial interest to water-resource managers and stakeholders in the context of water-supply enhancement. A statistical assessment of the SWAT-WAM linkage for the low-volume tail of the distribution of monthly storage of Canyon Lake is the focus of analysis and interpretation. Drought periods for the analysis are defined as the months (consecutive or not) during which Canyon Lake is below the 25th percentile of storage (381,000 acre-ft) for the baseline scenario. Such months are referred to as being within the “Drought Quartile.” The Drought Quartile is a conceptual and heuristically determined waypoint for the analysis and is not related to any administrative definition of drought by stakeholders or policy makers. The five scenarios and the baseline scenario simulated in the upper Guadalupe River SWAT were all passed through the Guadalupe River WAM by the SWAT-WAM linkage described in this report. A comparison of the mean increase per month in reservoir storage for Canyon Lake conditioned for the Drought Quartile was made. For each of the five brush-management and baseline scenarios, the months with storage below 381,000 acre-ft were extracted. The mean monthly storages during the Drought Quartile were computed for each of the five scenarios and the baseline scenario. The mean of the baseline scenario was 376,458 acre-ft and subsequently was subtracted from the mean monthly storage during the Drought Quartile for each of the five scenarios. The mean monthly offset storages of Canyon Lake during the Drought Quartile were 110 acre-ft (20 percent); 448 acre-ft (40 percent); 754 acre-ft (60 percent); 1,080 acre-ft (80 percent); and 1,090 acre-ft (100 percent). A particular mean was interpreted as follows: the value of 754 acre-ft for the 60-percent brush-management scenario implies that, on average, this scenario indicates an additional 754 acre-ft per month of storage in Canyon Lake relative to the baseline during the Drought Quartile. All of the five scenarios resulted in an increase on average to water supply relative to the baseline scenario during the Drought Quartile through the SWAT-WAM linkage.

Texas↗
USGS2022

Value of information and decision pathways: Concepts and case studies

Information used in decision making arises from the structuring of observations and data. The collection, dissemination, and use of information has monetary and non-monetary costs (e.g., competition for attention) and necessitates trade-offs. Understanding the benefits of having information (i.e., the value of information, VOI), including resulting societal outcomes, is useful to information producers/funders and decision makers. Using theory, use cases, and hypotheticals, we describe how information (e.g., geospatial information) is valued and incorporated in decisions and actions related to managing natural resources, environments, and the impacts of natural and anthropogenic hazards. We discuss the nature of information and how it relates to models (conceptual, mental, scientific), beliefs, knowledge, and economic analyses. VOI approaches and behavioral factors that potentially affect information use and value are summarized. Framing of information and VOI through data to decision pathways (DDPs) at first simplifies understanding, then illustrates the benefits of information, and the human and societal challenges encountered in valuing and using it. We present approaches to overcome these challenges. Our transdisciplinary analysis concludes with a summary of critical issues affecting DDPs and VOI, and suggestions for improving both economic analyses and the actionability and use of information.

Frontiers in Environmental Science (Environmental ↗
USGS2022

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

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

Earth System Science Data↗
USGS2004

Incorporating uncertainty into mercury-offset decisions with a probabilistic network for National Pollutant Discharge Elimination System permit holders: An interim report

This interim report describes an alternative approach for evaluating the efficacy of using mercury (Hg) offsets to improve water quality. Hg-offset programs may allow dischargers facing higher-pollution control costs to meet their regulatory obligations by making more cost effective pollutant-reduction decisions. Efficient Hg management requires methods to translate that science and economics into a regulatory decision framework. This report documents the work in progress by the U.S. Geological Survey’s Western Geographic Science Center in collaboration with Stanford University toward developing this decision framework to help managers, regulators, and other stakeholders decide whether offsets can cost effectively meet the Hg total maximum daily load (TMDL) requirements in the Sacramento River watershed. Two key approaches being considered are: (1) a probabilistic approach that explicitly incorporates scientific uncertainty, cost information, and value judgments; and (2) a quantitative approach that captures uncertainty in testing the feasibility of Hg offsets. Current fate and transport-process models commonly attempt to predict chemical transformations and transport pathways deterministically. However, the physical, chemical, and biologic processes controlling the fate and transport of Hg in aquatic environments are complex and poorly understood. Deterministic models of Hg environmental behavior contain large uncertainties, reflecting this lack of understanding. The uncertainty in these underlying physical processes may produce similarly large uncertainties in the decisionmaking process. However, decisions about control strategies are still being made despite the large uncertainties in current Hg loadings, the relations between total Hg (HgT) loading and methylmercury (MeHg) formation, and the relations between control efforts and Hg content in fish. The research presented here focuses on an alternative analytical approach to the current use of safety factors and deterministic methods for Hg TMDL decision support, one that is fully compatible with an adaptive management approach. This alternative approach uses empirical data and informed judgment to provide a scientific and technical basis for helping National Pollutant Discharge Elimination System (NPDES) permit holders make management decisions. An Hg-offset system would be an option if a wastewater-treatment plant could not achieve NPDES permit requirements for HgT reduction. We develop a probabilistic decision-analytical model consisting of three submodels for HgT loading, MeHg, and cost mitigation within a Bayesian network that integrates information of varying rigor and detail into a simple model of a complex system. Hg processes are identified and quantified by using a combination of historical data, statistical models, and expert judgment. Such an integrated approach to uncertainty analysis allows easy updating of prediction and inference when observations of model variables are made. We demonstrate our approach with data from the Cache Creek watershed (a subbasin of the Sacramento River watershed). The empirical models used to generate the needed probability distributions are based on the same empirical models currently being used by the Central Valley Regional Water Quality Control Cache Creek Hg TMDL working group. The significant difference is that input uncertainty and error are explicitly included in the model and propagated throughout its algorithms. This work demonstrates how to integrate uncertainty into the complex and highly uncertain Hg TMDL decisionmaking process. The various sources of uncertainty are propagated as decision risk that allows decisionmakers to simultaneously consider uncertainties in remediation/implementation costs while attempting to meet environmental/ecologic targets. We must note that this research is on going. As more data are collected, the HgT and cost-mitigation submodels are updated and the uncertainties may be reduced. Subsequently, the value of using a probabilistic framework for estimating and explicitly stating these uncertainties within a decisionmaking process can be estimated when new data are collected. Future work includes the design and implementation of a Bayesian network decision support system (BN-DSS) to produce mitigation scenarios for offset-project evaluation in the Cache Creek watershed. The decisionmaker, a wastewater-treatment plant, is expected to evaluate potential Hg-offset programs in terms of changes in HgT load changes, MeHg-production potential, project cost, and other suitability criteria. Subsequently, scenarios can be analyzed by performing sensitivity analyses and ranking environmental and economic uncertainties in terms of the decisionmaker’s preferences and risk choices. Such an analysis allows decisionmakers and stakeholders to explore various scenarios and predict the consequences of different stated preferences over outcomes.

Open-File Report↗
USGS2018

Before-after, control-impact analysis of evidence for the impacts of water level on Walleye, Northern Pike and Yellow Perch in lakes of the Rainy-Namakan complex (MN, USA and ON, CA)

Water level (WL) fluctuations in lakes influence many aspects of ecosystem processes. Concern about the potential impact of WL fluctuations on fisheries was one of the factors that motivated the decision in 2000 to alter the management of WL in the Rainy-Namakan reservoir complex (on the border between the U.S. state of Minnesota and the Canadian province of Ontario). We used a Before-After, Control-Impact (BACI) framework to identify potential impacts of the change in WL management to Walleye, Northern Pike and Yellow Perch catch per unit effort (CPUE). The CPUE of these species from 1990±1999 and from 2005±2014 were compared in four impact lakes (Lake Kabetogama, Namakan Lake, Rainy Lake and Sand Point Lake) and two control lakes (Lake of the Woods and Lake Vermilion) using a simple Bayesian model. Changes in fish CPUE in the impact lakes were often similar to changes that occurred in at least one control lake. The only change that was not similar to changes in control lakes was an increase of Yellow Perch in Lake Kabetogama. The two control lakes often differed substantially from each other, such that if only one had been available our conclusions about the role of WL management on fisheries would be very different. In general, identifying cause-and-effect relationships in observational field data is very difficult, and the BACI analysis used here does not specify a causative mechanism, so co-occurring environmental and management changes may obscure the effect of WL management.

Minnesota, Ontario↗
USGS2000

Assessing land-use impacts on biodiversity using an expert systems tool

Habitat alteration, in the form of land-use development, is a leading cause of biodiversity loss in the U.S. and elsewhere. Although statutes in the U.S. may require consideration of biodiversity in local land-use planning and regulation, local governments lack the data, resources, and expertise to routinely consider biotic impacts that result from permitted land uses. We hypothesized that decision support systems could aid solution of this problem. We developed a pilot biodiversity expert systems tool (BEST) to test that hypothesis and learn what additional scientific and technological advancements are required for broad implementation of such a system. BEST uses data from the U.S. Geological Survey's Gap Analysis Program (GAP) and other data in a desktop GIS environment. The system provides predictions of conflict between proposed land uses and biotic elements and is intended for use at the start of the development review process. Key challenges were the development of categorization systems that relate named land-use types to ecological impacts, and relate sensitivities of biota to ecological impact levels. Although the advent of GAP and sophisticated desktop GIS make such a system feasible for broad implementation, considerable ongoing research is required to make the results of such a system scientifically sound, informative, and reliable for the regulatory process. We define a role for local government involvement in biodiversity impact assessment, the need for a biodiversity decision support system, the development of a prototype system, and scientific needs for broad implementation of a robust and reliable system.

Landscape Ecology↗
USGS2007

The effects of acidic mine drainage from historical mines in the Animas River watershed, San Juan County, Colorado—What is being done and what can be done to improve water quality?

Historical production of metals in the western United States has left a legacy of acidic drainage and toxic metals in many mountain watersheds that are a potential threat to human and ecosystem health. Studies of the effects of historical mining on surface water chemistry and riparian habitat in the Animas River watershed have shown that cost-effective remediation of mine sites must be carefully planned. of the more than 5400 mine, mill, and prospect sites in the watershed, ∼80 sites account for more than 90% of the metal loads to the surface drainages. Much of the low pH water and some of the metal loads are the result of weathering of hydrothermally altered rock that has not been disturbed by historical mining. Some stream reaches in areas underlain by hydrothermally altered rock contained no aquatic life prior to mining. Scientific studies of the processes and metal-release pathways are necessary to develop effective remediation strategies, particularly in watersheds where there is little land available to build mine-waste repositories. Characterization of mine waste, development of runoff profiles, and evaluation of ground-water pathways all require rigorous study and are expensive upfront costs that land managers find difficult to justify. Tracer studies of water quality provide a detailed spatial analysis of processes affecting surface- and ground-water chemistry. Reactive transport models were used in conjunction with the best state-of-the-art engineering solutions to make informed and cost-effective remediation decisions. Remediation of 23% of the high-priority sites identified in the watershed has resulted in steady improvement in water quality. More than $12 million, most contributed by private entities, has been spent on remediation in the Animas River watershed. The recovery curve for aquatic life in the Animas River system will require further documentation and long-term monitoring to evaluate the effectiveness of remediation projects implemented.

Colorado, Montana↗
USGS2017

Translating statistical species-habitat models to interactive decision support tools

Understanding species-habitat relationships is vital to successful conservation, but the tools used to communicate species-habitat relationships are often poorly suited to the information needs of conservation practitioners. Here we present a novel method for translating a statistical species-habitat model, a regression analysis relating ring-necked pheasant abundance to landcover, into an interactive online tool. The Pheasant Habitat Simulator combines the analytical power of the R programming environment with the user-friendly Shiny web interface to create an online platform in which wildlife professionals can explore the effects of variation in local landcover on relative pheasant habitat suitability within spatial scales relevant to individual wildlife managers. Our tool allows users to virtually manipulate the landcover composition of a simulated space to explore how changes in landcover may affect pheasant relative habitat suitability, and guides users through the economic tradeoffs of landscape changes. We offer suggestions for development of similar interactive applications and demonstrate their potential as innovative science delivery tools for diverse professional and public audiences.

PLoS ONE↗
USGS2016

Prioritizing landscapes for longleaf pine conservation

We developed a spatially explicit model and map, as a decision support tool (DST), to aid conservation agencies creating or maintaining open pine ecosystems. The tool identified areas that are likely to provide the greatest benefit to focal bird populations based on a comprehensive landscape analysis. We used NLCD 2011, SSURGO, and SEGAP data to map the density of desired resources for open pine ecosystems and six focal species of birds and 2 reptiles within the historic range of longleaf pine east of the Mississippi River. Binary rasters were created of sites with desired characteristics such as land form, hydrology, land use and land cover, soils, potential habitat for focal species, and putative source populations of focal species. Each raster was smoothed using a kernel density estimator. Rasters were combined and scaled to map priority locations for the management of each focal species. Species’ rasters were combined and scaled to provide maps of overall priority for birds and for birds and reptiles. The spatial data can be used to identify high priority areas for conservation or to compare areas under consideration for maintenance or creation of open pine ecosystems.

Cooperator Science Series↗
USGS2014

Using natural range of variation to set decision thresholds: a case study for great plains grasslands

Natural range of variation (NRV) may be used to establish decision thresholds or action assessment points when ecological thresholds are either unknown or do not exist for attributes of interest in a managed ecosystem. The process for estimating NRV involves identifying spatial and temporal scales that adequately capture the heterogeneity of the ecosystem; compiling data for the attributes of interest via study of historic records, analysis and interpretation of proxy records, modeling, space-for-time substitutions, or analysis of long-term monitoring data; and quantifying the NRV from those data. At least 19 National Park Service (NPS) units in North America’s Great Plains are monitoring plant species richness and evenness as indicators of vegetation integrity in native grasslands, but little information on natural, temporal variability of these indicators is available. In this case study, we use six long-term vegetation monitoring datasets to quantify the temporal variability of these attributes in reference conditions for a variety of Great Plains grassland types, and then illustrate the implications of using different NRVs based on these quantities for setting management decision thresholds. Temporal variability of richness (as measured by the coefficient of variation, CV) is fairly consistent across the wide variety of conditions occurring in Colorado shortgrass prairie to Minnesota tallgrass sand savanna (CV 0.20–0.45) and generally less than that of production at the same sites. Temporal variability of evenness spans a greater range of CV than richness, and it is greater than that of production in some sites but less in other sites. This natural temporal variability may mask undesirable changes in Great Plains grasslands vegetation. Consequently, we suggest that managers consider using a relatively narrow NRV (interquartile range of all richness or evenness values observed in reference conditions) for designating a surveillance threshold, at which greater attention to the situation would be paid, and a broader NRV for designating management thresholds, at which action would be instigated.

Book chapter↗
USGS2016

ChemCam activities and discoveries during the nominal mission of the Mars Science Laboratory in Gale crater, Mars

At Gale crater, Mars, ChemCam acquired its first laser-induced breakdown spectroscopy (LIBS) target on Sol 13 of the landed portion of the mission (a Sol is a Mars day). Up to Sol 800, more than 188 000 LIBS spectra were acquired on more than 5800 points distributed over about 650 individual targets. We present a comprehensive review of ChemCam scientific accomplishments during that period, together with a focus on the lessons learned from the first use of LIBS in space. For data processing, we describe new tools that had to be developed to account for the uniqueness of Mars data. With regard to chemistry, we present a summary of the composition range measured on Mars for major-element oxides (SiO 2 , TiO 2 , Al 2 O 3 , FeO T , MgO, CaO, Na 2 O, K 2 O) based on various multivariate models, with associated precisions. ChemCam also observed H, and the non-metallic elements C, O, P, and S, which are usually difficult to quantify with LIBS. F and Cl are observed through their molecular lines. We discuss the most relevant LIBS lines for detection of minor and trace elements (Li, Rb, Sr, Ba, Cr, Mn, Ni, and Zn). These results were obtained thanks to comprehensive ground reference datasets, which are set to mimic the expected mineralogy and chemistry on Mars. With regard to the first use of LIBS in space, we analyze and quantify, often for the first time, each of the advantages of using stand-off LIBS in space: no sample preparation, analysis within its petrological context, dust removal, sub-millimeter scale investigation, multi-point analysis, the ability to carry out statistical surveys and whole-rock analyses, and rapid data acquisition. We conclude with a discussion of ChemCam performance to survey the geochemistry of Mars, and its valuable support of decisions about selecting where and whether to make observations with more time and resource-intensive tools in the rover's instrument suite. In the end, we present a bird's-eye view of the many scientific results: discovery of felsic Noachian crust, first observation of hydrated soil, discovery of manganese-rich coatings and fracture fills indicating strong oxidation potential in Mars' early atmosphere, characterization of soils by grain size, and wide scale mapping of sedimentary strata, conglomerates, and diagenetic materials.

Journal of Analytical Atomic Spectrometry↗
USGS2016

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi -season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea ( Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary ( kharif ) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second ( rabi ) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

International Journal of Digital Earth↗
USGS1999

Thickness of unconsolidated deposits in the towns of Solon and Taylor, Cortland County, New York

Introduction Siting of waste-disposal facilities in Cortland County poses a potential threat to local ground-water resources. An especially sensitive waste-disposal siting issue arose in 1988, when the New York State Low-Level Radioactive Waste Siting Commission (NYSLLWSC) identified 15 sites in six towns (Towns of Solon, Taylor, Freetown, Cincinnatus, Marathon, and Willet) in the eastern part of the county for possible disposal of low-level radioactive waste (New York State Low-Level Radioactive Waste Siting Commission, 1988). Eventually, two sites in the Town of Taylor became finalist sites; one was selected from the list of 15 potential sites, and the other was offered by a private landowner. Little information was available on geohydrologic conditions in eastern Cortland County, such as the extent of aquifers and the thickness of unconsolidated deposits of low permeability (such as clay and till), even though these two criteria were among those used by NYSLLWSC for selection of potential disposal sites. The source of information on thickness of drift over bedrock was the surficial geologic map of New York (Muller and Cadwell, 1986). The siting effort was terminated before a final selection was made, but the issue had made county managers aware that detailed information on the extent and thickness of unconsolidated deposits (particularly till, which typically has low permeability and can limit the migration of contaminants) is needed before sound decisions on waste-disposal siting can be made. Glaciers deposited till nearly everywhere over bedrock in the uplands of central New York, but the thickness of the till varies greatly from place to place. An analysis by Coates (1966) of 400 drillers' logs of wells in a 2,000-mi 2 area in the uplands of south-central New York (south of the Cortland County) indicated that (1) till is thin or absent on hilltops and is thickest on the lower parts of hills, (2) overall till thickness averages 60 ft, and (3) till thickness on the south, east, west, and north slopes averages 92, 52, 62, and 22 ft, respectively. Hills that have thick till on their south slopes have been referred to as till-shadowed hills by Coates (1974), who attributes this characteristic to glaciers that deposited thick amounts of till on the downflow side of a hill (analogous to flowing streams or wind that deposit sediment on the lee side of an object). Because the till on the south slopes is relatively thick and typically has low permeability, these slopes have been considered as potential areas for waste-disposal sites. In 1997, the U.S. Geological Survey (USGS), in cooperation with the Cortland County Department of Planning, began a 1-year study to map the thickness of unconsolidated deposits and the extent of valley-fill aquifers in the Towns of Solon and Taylor (an area of 60 mi 2 ) in eastern Cortland County. This report (1) depicts the thickness of unconsolidated deposits and the extent of valley-fill aquifers in the Towns of Solon and taylor in eastern Cortland County, (2) examines whether the "till-shadowed hill" concept developed by Coates (1966) is applicable in this area, and (3) provides three schematic geologic sections showing the thickness of unconsolidated deposits in the uplands in the northwestern part of the study area.

New York↗
USGS2025

Estimated average annualized tsunami losses for the United States

Tsunami hazards are substantial threats to coastal communities across the United States (U.S.) and its territories. U.S. states and territories collaborate through the National Tsunami Hazard Mitigation Program (NTHMP) to develop their own tsunami-hazard information for outreach and evacuation planning. An effort to curate this tsunami-hazard information to support comprehensive risk analysis at the national level has not yet been completed. In support of this effort, the Federal Emergency Management Agency (FEMA) collaborated with the NTHMP, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS) starting in 2023. This collaboration included the collection and analysis of existing tsunami hazard data and methods in the U.S. Tsunami subject matter experts identified and selected scientifically defensible methods for estimating the risks to buildings and populations in coastal communities. These efforts may support decision making regarding resilience policies, priorities, strategies and funding levels. Tsunamis can be triggered by earthquakes, subaerial or submarine landslides, volcanic eruptions, glacial calving, near-earth objects, weather or other events. These events can cause severe destruction, injuries, and loss of life due to powerful currents and flooding. Tsunamis pose a substantial threat to the western United States and all U.S. territories, as described below. ■ Hawaii is threatened by distant tsunamis due to its central location in the Pacific Ocean basin and has a history of local events. ■ Alaska, particularly the Aleutian Islands, faces local tsunami threats due to proximity to the Alaska-Aleutian Subduction Zone, as well as distant tsunamis from around the Pacific Ocean basin. ■ The western coast of the U.S. is threatened by distant tsunamis from around the Pacific Ocean basin and local source tsunamis from earthquakes generated within the Cascadia Subduction Zone in the Pacific Northwest. ■ American Samoa faces local tsunami threats from earthquakes generated in the nearby Tonga Trench, as well as distant tsunami threats. ■ Guam and the Commonwealth of the Northern Mariana Islands are threatened by local tsunamis from the nearby Mariana Subduction Zone, as well as distant sources from around the Pacific Ocean Basin. ■ Puerto Rico and the United States Virgin Islands are threatened by multiple local and distant tsunami sources, such as the Puerto Rico Trench (PRT), given their location in the complex seismic region of the Caribbean Sea. Several historical events stand out because of their catastrophic impacts. ■ In the Pacific Northwest, the 1700 Cascadia earthquake caused a tsunami that affected coastal Native American communities, though the extent of the damage is not fully documented (Ludwin, et al., 2005). ■ In Puerto Rico, the 1918 earthquake triggered a tsunami that caused $77 million in damage in 2022 dollars and 116 fatalities, primarily along the western coast (Coffman et al., 1982). ■ The 1946 Aleutian Islands earthquake triggered a massive tsunami that devastated Hilo, Hawaii, killing 158 people and resulting in approximately $375 million in damage (adjusted to 2022 dollars) (Fisher et al., 2023). ■ The 1964 Alaska earthquake (M 9.2) generated tsunamis that caused severe destruction in some communities across Alaska, Oregon, and California. This disaster led to a total of 124 fatalities and approximately $2.9 billion in property damage (adjusted to 2022 dollars) (Brocher et al., 2014) (Alaska Science Center, 2024). ■ In American Samoa, a tsunami generated by the 2009 Samoa earthquake (Mw 8.1) caused widespread devastation, resulting in 34 confirmed fatalities (Apatu et al., 2013) and economic losses exceeding $160 million (adjusted to 2022 dollars) (DHS, 2011). More recent events, including the 2010 Chile earthquake, the 2011 Japan earthquake, and the 2022 Tonga volcanic eruption, resulted in millions of dollars in damage to numerous ports and harbors in the U.S. South Pacific territories, Hawaii, and along the west coast of the U.S. (Lynett, et al., 2022) (Wilson, et al., 2013). Since these events, the expansion of the built environment in lowlying areas along the coast has increased the exposure of buildings and people, thereby further escalating community risk from tsunamis. This report provides a comprehensive national assessment of earthquake-generated tsunami risk. It does not include impacts from tsunamis generated by landslides, volcanic eruptions, glacial calving, near-earth objects, weather, or other events. This study is based on the best available hazard data from the U.S. Pacific Coast (California, Oregon and Washington), Alaska, Hawaii, U.S. Pacific Territories (American Samoa, Guam and Commonwealth of the Northern Mariana Islands) and Caribbean Territories (Puerto Rico and United States Virgin Islands). Tsunami risks associated with states along the East Coast, Gulf Coast, and Great Lakes are not included in this study because Hazus 6.1 software (FEMA 2024a) does not currently include the ability to analyze tsunami risk in those states. Once modeling capabilities and tsunami hazard data become available for additional states, FEMA may incorporate these data into future editions of this study.

Alaska, California, Hawaii Oregon, Washington↗
USGS2020

Good practices for species distribution modeling of deep-sea corals and sponges for resource management: Data collection, analysis, validation, and communication

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, we suggest analysts: 1) integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys); and 2) include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.

Frontiers in Marine Science↗
USGS2019

Geochemically distinct oil families in the onshore and offshore Santa Maria basins, California

The purpose of this work is to identify genetic affinities among 48 crude oil samples from the onshore and offshore Santa Maria basins. A total of 21 source-related biomarker and stable carbon isotope ratios among the samples were assessed to assure that they were unaffected by secondary processes. Chemometric analysis of these data identifies six oil families with map and stratigraphic distributions that reflect organofacies variations within the Miocene Monterey Formation source rock. The data comprise a training set that was used to create a chemometric decision tree to classify newly collected oil samples. Three onshore families originated from two synclines, which may contain one or more pods of thermally mature source rock. Multiple biomarker parameters indicate that the six oil families achieved early oil window maturity in the range of 0.6%–0.7% equivalent vitrinite reflectance. The offshore oil samples consist of one family from Point Pedernales field and two families from the “B” prospect. Geochemical characteristics of these families indicate origins under differing water column and sediment oxicity and carbonate versus siliceous and detrital input in ‘carbonate,’ ‘marl,’ and ‘shale’ organofacies like those in the lower calcareous–siliceous, carbonaceous marl, and clayey–siliceous members of the Monterey Formation elsewhere in coastal California. The corresponding lithofacies and organofacies appear to be linked to the early–middle Miocene climate optimum and subsequent paleoclimatic cooling after circa 14 Ma, a systematic up-section increase in the stable carbon isotope composition of related oil samples, decreased preservation of calcium carbonate shells from planktic foraminifera and coccoliths, and increased preservation of clay-sized siliceous shells of diatoms and radiolarians. The results show that organofacies within the Monterey source rock are responsible for many of the geochemical differences between the oil families. This paleoclimate–organofacies model for crude oil from the Monterey Formation can be used to enhance future exploration efforts in many areas of coastal California.

California↗