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USGS2003

Application of artificial neural networks to complex groundwater management problems

As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models.

Natural Resources Research↗

USGS2021

PS3: The Pheno-Synthesis software suite for integration and analysis of multi-scale, multi-platform phenological data

Phenology is the study of recurring plant and animal life-cycle stages which can be observed across spatial and temporal scales that span orders of magnitude (e.g., organisms to landscapes). The variety of scales at which phenological processes operate is reflected in the range of methods for collecting phenologically relevant data, and the programs focused on these collections. Consideration of the scale at which phenological observations are made, and the platform used for observation, is critical for the interpretation of phenological data and the application of these data to both research questions and land management objectives. However, there is currently little capacity to facilitate access, integration and analysis of cross-scale, multi-platform phenological data. This paper reports on a new suite of software and analysis tools – the “Pheno-Synthesis Software Suite,” or PS3 – to facilitate integration and analysis of phenological and ancillary data, enabling investigation and interpretation of phenological processes at scales ranging from organisms to landscapes and from days to decades. We use PS3 to investigate phenological processes in a semi-aride, mixed shrub-grass ecosystem, and find that the apparent importance of seasonal precipitation to vegetation activity (i.e., “greenness”) is affected by the scale and platform of observation. We end by describing potential applications of PS3 to phenological modeling and forecasting, understanding patterns and drivers of phenological activity in real-world ecosystems, and supporting agricultural and natural resource management and decision-making.

Ecological Informatics↗

USGS2021

Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate

Translocations are essential for re-establishing wildlife populations. As they sometimes fail, it is critical to assess factors that influence their success pre-translocation. Socioecological suitability models (SESMs) integrate social acceptance and ecological suitability to enable identification of areas where wildlife populations will expand, which makes it likely that SESMs will also be useful for predicting translocation success. To inform site selection for potential elk Cervus canadensis reintroduction to north-eastern Minnesota, United States, we developed broadscale maps of social acceptance from surveys of local residents and landowners, animal use equivalence (AUE) from forage measured in the field and empirical conflict risk from geospatial data. Resulting SESMs integrated social acceptance favourability scores, AUE and conflict risk, and weighted SESMs showed the relative influences of acceptance and conflict. Social acceptance was positive for local residents and landowners (mean ≥ 5.4; scale of 1–7). AUE (scaled to an elk home range) ranged between 1 and 9 elk/16 km 2 during winter, and from 14 to 83 elk/16 km 2 during summer. Human–elk conflict risk was low (mean ≤ 0.10; scaled 0–1), increasing from north to south. Geographical distributions differed for social acceptance, AUE and conflict risk, and weighted SESMs revealed unsuitable areas that were otherwise obscured. Synthesis and applications . Integrating human–wildlife conflict risk into SESMs shows where social acceptance of translocated species is likely to erode, even where viewed favourably pre-translocation, to inform translocation planning by highlighting interactions between key factors. Such integrated models supplement existing reintroduction biology frameworks by supporting decision-making and knowledge development. In north-eastern Minnesota, natural resource managers who are considering elk reintroductions are using SESMs reported here to identify where human–elk conflict is unlikely to result in an isolated elk population and where addressing concerns for area residents about conflict risk is essential.

Minnesota↗

USGS2013

Fort Collins Science Center Ecosystem Dynamics branch--interdisciplinary research for addressing complex natural resource issues across landscapes and time

The Ecosystem Dynamics Branch of the Fort Collins Science Center offers an interdisciplinary team of talented and creative scientists with expertise in biology, botany, ecology, geology, biogeochemistry, physical sciences, geographic information systems, and remote-sensing, for tackling complex questions about natural resources. As demand for natural resources increases, the issues facing natural resource managers, planners, policy makers, industry, and private landowners are increasing in spatial and temporal scope, often involving entire regions, multiple jurisdictions, and long timeframes. Needs for addressing these issues include (1) a better understanding of biotic and abiotic ecosystem components and their complex interactions; (2) the ability to easily monitor, assess, and visualize the spatially complex movements of animals, plants, water, and elements across highly variable landscapes; and (3) the techniques for accurately predicting both immediate and long-term responses of system components to natural and human-caused change. The overall objectives of our research are to provide the knowledge, tools, and techniques needed by the U.S. Department of the Interior, state agencies, and other stakeholders in their endeavors to meet the demand for natural resources while conserving biodiversity and ecosystem services. Ecosystem Dynamics scientists use field and laboratory research, data assimilation, and ecological modeling to understand ecosystem patterns, trends, and mechanistic processes. This information is used to predict the outcomes of changes imposed on species, habitats, landscapes, and climate across spatiotemporal scales. The products we develop include conceptual models to illustrate system structure and processes; regional baseline and integrated assessments; predictive spatial and mathematical models; literature syntheses; and frameworks or protocols for improved ecosystem monitoring, adaptive management, and program evaluation. The descriptions in this fact sheet provide snapshots of our three research emphases, followed by descriptions of select current projects.

Fact Sheet↗

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 ↗

USGS1981

Economics and coal resource appraisal: strippable coal in the Illinois Basin ( USA)

Coal-resource appraisals generally describe the location and general characteristics of coal beds. Estimates are made of the average overburden depth (depth of the coal bed below the surface), bed thickness, and perhaps certain chemical properties of the coal [1]. Although such resource compilations represent an important initial step, neither they nor current estimates provide sufficient information to determine the costs of alternative options for National energy policy. Because coal is expected to provide an increasing part of future overall U.S. energy supply, it is crucial for long term planning that coalresource appraisals convey sufficient information regarding the degree of economic resource scarcity (escalation of unit costs for mining remaining deposits as the best deposits in an area are mined out) expected as coal consumption increases. However, assumptions embodied in most large-scale models of coal supply [13; 20] imply that the coal reserves that exist can be commercially produced as needed. As demonstrated by the embargo of 1973-1974 and more recent difficulties in obtaining crude oil, even a slight temporary commodity shortfall will result in significant economic losses when the commodity supplies a large proportion of the total energy used by an economy. For the United States, crude oil resource estimates did not give warning of a decline in domestic petroleum product. In this paper we argue that coal-resource estimates, as they are now made, will not give warning of future supply difficulties. A method for incorporating an economic dimension into appraisals of strippable coal resources is presented and is applied to a major U.S. coal-producing region, the Illinois part of the Illinois basin. Illinois accounts for nearly 70% of the demonstrated strippable coal reserve base of the Illinois basin [12]. In particular, a long-run incremental cost function (that is unit costs vs. cumulative reserves extracted) is estimated for strippable coal in Illinois. The estimated cost function exhibits an initial range over which costs increase at a constant rate-followed by a range where costs increase very rapidly and the function becomes quite inelastic. This long-run incremental cost function is significant for two reasons. Comparison of the demonstrated reserve base for strippable coal (14.8 billion tons) with cumulative production (since 1920 of 1.1 billion tons) might suggest that future depletion will not be significant when, in fact, the cost function presented here indicates otherwise. Because the only states having more reserves of strippable coal than Illinois are Montana and Wyoming [18], these results have implications for national coal-resource appraisal. Secondly, most models used to study the effects of alternative public policies in coal markets are static in nature [15; 16] and cannot consider the effects of increasing costs. Furthermore, models which are multi-period in nature [11; 13] appear to have little empirical basis for their assumptions regarding the escalation of production costs resulting from depletion. The plan of the paper is as follows. First a description of the analytical approach for constructing the long-run incremental cost-reserve function is discussed. Following this, the descriptions of the Illinois basin and the basic physical data are presented. In the concluding section, the main results are presented and their implications are explored

Southern Economic Journal↗

USGS2013

Geohydrology, geochemistry, and groundwater simulation (1992-2011) and analysis of potential water-supply management options, 2010-60, of the Langford Basin, California

Groundwater withdrawals began in 1992 from the Langford Basin within the Fort Irwin National Training Center (NTC), California. From April 1992 to December 2010, approximately 12,300 acre-feet of water (averaging about 650 acre-feet per year) has been withdrawn from the basin and transported to the adjacent Irwin Basin. Since withdrawals began, water levels in the basin have declined by as much as 40 feet, and the quality of the groundwater withdrawn from the basin has deteriorated. The U.S. Geological Survey collected geohydrologic data from Langford Basin during 1992–2011 to determine the quantity and quality of groundwater available in the basin. Geophysical surveys, including gravity, seismic refraction, and time-domain electromagnetic induction surveys, were conducted to determine the depth and shape of the basin, to delineate depths to the Quaternary-Tertiary interface, and to map the depth to the water table and changes in water quality. Data were collected from existing wells and test holes, as well as 11 monitor wells that were installed at 5 sites as part of this study. Water-quality samples collected from wells in the basin were used to determine the groundwater chemistry within the basin and to delineate potential sources of poor-quality groundwater. Analysis of stable isotopes of oxygen and hydrogen in groundwater indicates that present-day precipitation is not a major source of recharge to the basin. Tritium and carbon-14 data indicate that most of the basin was recharged prior to 1952, and the groundwater in the basin has an apparent age of 12,500 to 30,000 years. Recharge to the basin, estimated to be less than 50 acre-feet per year, has not been sufficient to replenish the water that is being withdrawn from the basin. A numerical groundwater-flow model was developed for the Langford Basin to better understand the aquifer system used by the Fort Irwin NTC as part of its water supply, and to provide a tool to help manage groundwater resources at the NTC. Measured groundwater-level declines since the initiation of withdrawals (1992–2011) were used to calibrate the groundwater-flow model. The simulated recharge was about 46 acre-feet per year, including approximately 6 acre-feet per year of natural recharge derived from precipitation runoff and as much as 40 acre-feet per year of underflow from the Irwin Basin. Between April 1992 and December 2010, an average of about 650 acre-feet per year of water was withdrawn from the Langford Basin. Groundwater withdrawals in excess of natural recharge resulted in a net loss of 11,670 acre-feet of groundwater storage within the basin for the simulation period. The Fort Irwin NTC is considering various groundwater-management options to address the limited water resources in the Langford Basin. The calibrated Langford Basin groundwater-flow model was used to evaluate the hydrologic effects of four groundwater-withdrawal scenarios being considered by the Fort Irwin NTC over the next 50 years (January 2011 through December 2060). Continuation of the 2010 withdrawal rate in the three existing production wells will result in 70 feet of additional drawdown in the central part of the basin. Redistributing the 2010 withdrawal rate equally to the three existing wells and two proposed new wells in the northern and southern parts of the basin would result in about 10 feet less drawdown in the central part of the basin but about 100 feet of additional drawdown in the new well in the northern part of the basin and about 50 feet of additional drawdown in the new well in the southern part of the basin. Reducing the withdrawals from the three existing production wells in the central part of the basin from about 45,000 acre-feet to about 32,720 acre-feet would result in about 40 feet of additional drawdown in the central basin near the pumping wells, about 25 feet less than if withdrawals were not reduced. The combination of reducing and redistributing the cumulative withdrawals to the three existing and two proposed new wells results in about 40 feet of additional drawdown in the central and southern parts of the basin and about 70 feet in the northern part of the basin. These results show that reducing and redistributing the groundwater withdrawals would maintain the upper aquifer at greater than 50 percent of its predevelopment saturated thickness throughout the groundwater basin. The scenarios simulated for this study demonstrate how the calibrated model can be utilized to evaluate the hydrologic effects of different water-management strategies.

California↗

USGS2018

Interstate water management of a “hidden” resource - Physical principles of groundwater hydrology

Groundwater systems are dynamic geologic environments in which water continuously flows from recharge areas to discharge areas at streams, springs, wetlands, coastal waters, and wells. Natural, predevelopment conditions within groundwater systems are changed by the introduction of wells and other human stresses that modify existing groundwater levels, flow paths, and hydrologic budgets. Groundwater serves the Nation as an important water supply, but in some instances such stresses can have adverse impacts that include excessive ground water-level declines, aquifer-storage reductions, and streamflow depletions. Many of the Nation’s aquifer systems extend over thousands of square miles and their hydrologic boundaries may be distant from jurisdictional boundaries that can be the focus of groundwater disputes. Effective interstate management of groundwater resources is benefited by an understanding of the regional-scale controls that affect groundwater conditions at the local scale. Numerical models are the most effective approach for accounting for all of the relevant hydrologic processes that affect groundwater systems and their response to natural and manmade stresses. This paper provides a brief background on some of the basic principles of groundwater hydrology that are relevant to interstate management of this important natural resource.

Conference Paper↗

USGS2026

The functional effects of African lions on co-occurring carnivores differ across species pairs and with changes in resource availability and lion abundance

Apex carnivores are known to regulate ecosystem structure and function, including via interactions with syntopic, competitively inferior carnivores. These effects may be dependent on relative carnivore density and resource availability or productivity. We investigated the functional effect of African lions as an apex carnivore on the presence of co-occurring large carnivore species across two adjoining National Parks that contrast in relative densities of carnivores and prey. We employed two-species occupancy models from track data to test statistical interactions between lions and the other syntopic large carnivore species, while accounting for each species’ habitat selection. We further investigated the influence of anthropogenic and natural variables on these co-occurrence dynamics. Our models revealed that the occurrence of each carnivore species was best predicted by access to their own key resources. We also found significant statistical interactions between lions and cheetahs, lions and leopards, and lions and spotted hyenas in resource-rich landscapes. Finally, we found limited support for the competition exclusion hypothesis between most species, with the exception of lion-African wild dog co-occurrence patterns. Species’ co-occurrence dynamics were all influenced by resource availability, with lion-leopard and lion-cheetah co-occurrence decreasing strongly with increasing resource availability. Most species co-occurrence declined with increasing occurrence of lions. The patterns revealed by this study improves predictions of how changes in resource availability and carnivore occurrence could impact carnivore community dynamics and the functional role of apex carnivores.

Kruger National Park, Limpopo National Park↗

USGS2021

Geologic assessment of undiscovered oil and gas resources in the Cherokee Platform area of Kansas, Oklahoma, and Missouri

In 2015, the U.S. Geological Survey completed a geology-based assessment to estimate the volumes of undiscovered, technically recoverable petroleum resources in the Cherokee Platform Province area of southeastern Kansas, northeastern Oklahoma, and southwestern Missouri. The U.S. Geological Survey identified four stratigraphic intervals that contain petroleum source rocks: (1) thin shales in the Middle to Upper Ordovician Simpson Group, (2) shales within the Upper Devonian to Lower Mississippian Woodford Shale and stratigraphically equivalent Chattanooga Shale, (3) coals and coal-associated shales and mudstones in the Middle Pennsylvanian (Desmoinesian) Cherokee and Marmaton Groups, and (4) thin marine shales within the Marmaton Group and the Upper Pennsylvanian (Missourian) Kansas City and Lansing Groups. Based on the nature of the petroleum accumulations, the characterization of the compositions and thermal maturity of the organic matter in the rocks, and the compositions of the produced petroleum, the U.S. Geological Survey identified three total petroleum systems (TPS) containing four assessment units (AU): the Paleozoic Composite TPS with the Paleozoic Conventional Assessment Unit (AU), the Woodford/Chattanooga TPS with the Woodford Shale Oil AU and the Woodford Biogenic Gas AU, and the Desmoinesian Coal TPS with the Desmoinesian Coalbed Gas AU. Assessment unit summaries follow 1. Three source rock intervals have contributed geochemically distinct oils to reservoirs within the Paleozoic Conventional AU. These intervals are the Simpson Group; the Woodford and Chattanooga Shales; and the Marmaton, Kansas City, and Lansing Groups. The major petroleum source rocks are the Woodford and Chattanooga Shales. The Paleozoic Conventional AU includes reservoirs that range in age from the Upper Cambrian Arbuckle Group to the lower Permian Chase Group. Most oil production in the province has been from Pennsylvanian sandstone reservoirs. Estimated undiscovered petroleum resources for this AU are a mean of 3 million barrels of oil (MMBO), 140 billion cubic feet of gas (BCFG), and 4 million barrels of natural gas liquids (MMBNGL). 2. The Woodford Shale Oil AU contains undiscovered continuous petroleum resources within the Woodford Shale and Chattanooga Shale. The geologic model for the AU assumes that petroleum resources remain trapped within the shale following petroleum migration. For most of the AU, organic matter within the Woodford Shale and Chattanooga Shale is thermally mature with respect to petroleum generation as shown by vitrinite reflectance values between 0.6 and 1 percent. Petroleum has been produced from the Woodford Shale and Chattanooga Shale. Estimated undiscovered petroleum resources for this AU are means of 460 MMBO, 640 BCFG, and 7 MMBNGL. 3. The Woodford Shale Biogenic Gas AU contains undiscovered continuous petroleum resources in the east-central portion of the Cherokee Platform Province near the Ozark uplift where the Woodford Shale and Chattanooga Shale are at depths of 1,250 ft or shallower. At those depths, methanogenesis and(or) biodegradation of thermogenic natural gases can be found where the shale may be more fractured and more susceptible to groundwater penetrations. The mean assessed volume of undiscovered gas for this assessment unit is 416 BCFG and 1 MMBNGL. 4. The Desmoinesian Coalbed Gas AU contains undiscovered continuous petroleum resources within the Middle Pennsylvanian coals and coal-associated shales and mudstones. The boundaries for the Desmoinesian Coalbed Gas AU are, in part, defined by the extent, depth, and thickness of the coals. Within the Desmoinesian Coalbed Gas AU, a sweet spot area was delineated based on a 10 foot or greater net coal thickness. Gas analytical data show that natural gas produced from the coals has a mixed biogenic and thermogenic origin and that there is significant migration of natural gas into the coals from adjacent conventional sandstone reservoirs. The estimated mean volume of undiscovered gas is 10.0 trillion cubic ft of gas (TCFG), and 23 MMBNGL. For the three continuous (unconventional) assessment units and one conventional assessment unit in the Cherokee Platform Province, total mean volumes of undiscovered petroleum resources are estimated to be 463 MMBO, 11.2 TCFG and 35 MMBNGL.

Kansas, Missouri, Oklahoma↗

USGS2003

Precipitation-Runoff Simulations of Current and Natural Streamflow Conditions in the Methow River Basin, Washington

Management of the water resources of the Methow River Basin is changing in response to the listing of three species of fish under the Endangered Species Act and the Washington State-legislated watershed-planning process. This report describes the construction and calibration of an enhanced precipitation-runoff model for the Methow River Basin and evaluates the model as a predictive tool for assessing the current and natural streamflow conditions. This study builds upon a previous precipitation-runoff model for the Methow River Basin and validates the current model using a new, more extensive streamflow data network. The major enhancement was the simulation of current flow conditions with the addition of irrigation diversions, returns, and application. The Geographic Information System Weasel characterized the physical properties of the basin and the Modular Modeling System, using the Precipitation-Runoff Modeling System, simulated the hydrologic flow. Streamflow was simulated for water years 1992-2001 to calibrate the model to measured streamflows. A sensitivity analysis was completed using nonlinear regression to determine hydrologic parameters pertinent to the modeling results. Simulated and measured streamflow generally showed close agreement, especially during spring runoff from snowmelt. Low-flow or baseflow periods, most restrictive to fish habitation, were simulated reasonably well yet possessed the most uncertainty. Simulations of annual mean streamflow as a percentage of measured annual mean streamflow for the 10-year calibration period at six of the seven streamflow-gaging stations ranged from -35.2 to +26.2 percent, with 65 percent of the simulated values within 15 percent. One station was intentionally calibrated to over-simulate discharge (simulated discharge greater than measured discharge) in order to compensate for observed channel losses not simulated by the model. Simulation of water years 1960-2001 demonstrated great variability in monthly streamflow statistics. The simulated mean monthly flows for 11 streamflow-gaging stations were an average of 2.5 percent higher for water years 1992-2001 than for the entire simulation period. If water year 2001, an extreme drought year, is omitted, simulated mean monthly flows for the 11 streamflow-gaging stations were an average of 9.0 percent higher than for the entire simulation period. The calibrated model also examined the effects of irrigation-canal seepage on streamflow. Irrigation-canal seepage contributed to streamflow throughout the year, with the greatest effect during the irrigation season. Management of the water resources of the Methow River Basin is changing in response to the listing of three species of fish under the Endangered Species Act and the Washington State-legislated watershed-planning process. This report describes the construction and calibration of an enhanced precipitation-runoff model for the Methow River Basin and evaluates the model as a predictive tool for assessing the current and natural streamflow conditions. This study builds upon a previous precipitation-runoff model for the Methow River Basin and validates the current model using a new, more extensive streamflow data network. The major enhancement was the simulation of current flow conditions with the addition of irrigation diversions, returns, and application. The Geographic Information System Weasel characterized the physical properties of the basin and the Modular Modeling System, using the Precipitation-Runoff Modeling System, simulated the hydrologic flow. Streamflow was simulated for water years 1992-2001 to calibrate the model to measured streamflows. A sensitivity analysis was completed using nonlinear regression to determine hydrologic parameters pertinent to the modeling results. Simulated and measured streamflow generally showed close agreement, especially during spring runoff from snowmelt. Low-flow or baseflow periods, most restrictive to fish habitation

Water-Resources Investigations Report↗

USGS2014

Assessing and managing freshwater ecosystems vulnerable to global change

Freshwater ecosystems are important for global biodiversity and provide essential ecosystem services. There is consensus in the scientific literature that freshwater ecosystems are vulnerable to the impacts of environmental change, which may trigger irreversible regime shifts upon which biodiversity and ecosystem services may be lost. There are profound uncertainties regarding the management and assessment of the vulnerability of freshwater ecosystems to environmental change. Quantitative approaches are needed to reduce this uncertainty. We describe available statistical and modeling approaches along with case studies that demonstrate how resilience theory can be applied to aid decision-making in natural resources management. We highlight especially how long-term monitoring efforts combined with ecological theory can provide a novel nexus between ecological impact assessment and management, and the quantification of systemic vulnerability and thus the resilience of ecosystems to environmental change.

Florida Everglades↗

USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗

USGS2005

Incorporating uncertainty in watershed management decision-making: A mercury TMDL case study

Water quality impairment due to high mercury fish tissue concentrations and high mercury aqueous concentrations is a widespread problem in several sub-watersheds that are major sources of mercury to the San Francisco Bay. Several mercury Total Maximum Daily Load regulations are currently being developed to address this problem. Decisions about control strategies are being made despite very large uncertainties about current mercury loading behavior, relationships between total mercury loading and methyl mercury formation, and relationships between potential controls and mercury fish tissue levels. To deal with the issues of very large uncertainties, data limitations, knowledge gaps, and very limited State agency resources, this work proposes a decision analytical alternative for mercury TMDL decision support. The proposed probabilistic decision model is Bayesian in nature and is fully compatible with a "learning while doing" adaptive management approach. Strategy evaluation, sensitivity analysis, and information collection prioritization are examples of analyses that can be performed using this approach.

Conference Paper↗

USGS2008

Preliminary model of porphyry copper deposits

The U.S. Geological Survey (USGS) Mineral Resources Program develops mineral-deposit models for application in USGS mineral-resource assessments and other mineral resource-related activities within the USGS as well as for nongovernmental applications. Periodic updates of models are published in order to incorporate new concepts and findings on the occurrence, nature, and origin of specific mineral deposit types. This update is a preliminary model of porphyry copper deposits that begins an update process of porphyry copper models published in USGS Bulletin 1693 in 1986. This update includes a greater variety of deposit attributes than were included in the 1986 model as well as more information about each attribute. It also includes an expanded discussion of geophysical and remote sensing attributes and tools useful in resource evaluations, a summary of current theoretical concepts of porphyry copper deposit genesis, and a summary of the environmental attributes of unmined and mined deposits.

Open-File Report↗

USGS2012

Strategic directions for U.S. Geological Survey water science, 2012-2022 - Observing, understanding, predicting, and delivering water science to the Nation

Executive Summary This report expands the Water Science Strategy that was begun in the USGS Science Strategy, “Facing Tomorrow’s Challenges—U.S. Geological Survey Science in the Decade 2007–2017” (U.S. Geological Survey, 2007). The 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 Strategic Science Plan 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 130-year history. Nine priority actions are presented in the report, which combine and elevate the numerous specific strategic actions listed throughout the report. Priority actions were developed as a means of providing the audience of this report with a list for focused attention, even if resources and time limit the ability of managers to address all of the strategic actions in the report. Priority actions focus on the following: Improve integrated science planning for water. Expand and enhance water-resource monitoring networks. Characterize the water cycle through development of state-of-the-art 3-D/4-D hydrogeologic framework models at multiple scales. Clarify the linkage between human water use (engineered hydrology) and the water cycle (natural hydrology). Advance ecological flow science. Provide flood-inundation science and information. Develop rapid deployment teams for water-related emergencies. Conduct integrated watershed assessment, research, and modeling. Deliver water data and analyses to the Nation. The body of the report is presented as a hierarchal set of 5 goals, 14 objectives, and 27 strategic actions that the USGS should undertake to advance water science through year 2022. The goals deal with: Providing society the information it needs regarding the amount and quality of water in all components of the water cycle at high temporal and spatial resolution, nationwide; Advancing our understanding of processes that determine water availability; Predicting changes in the quantity and quality of water resources in response to changing climate, population, land use, and management scenarios; Anticipating and responding to water-related emergencies and conflicts; and Delivering timely hydrologic data, analyses, and decision-support tools seamlessly across the Nation to support water-resource decisions. Scientific information produced on water resources would be without value if it were not communicated to society in a fashion that can inform decisions and actions. Therefore, the chapter following the goals describes how the USGS should inform, involve, and educate society about the science it produces. This includes discussions on local outreach and the use of social media for effective communication. This report concludes with a chapter devoted to the crosscutting science issues of the Water Mission Area with the other USGS Mission Areas: Climate and Land Use Change, Core Science Systems, Ecosystems, Energy and Minerals, Environmental Health Science, and Natural Hazards. Not one of these Mission Areas stands alone—all must work together and integrate their actions to fulfill the USGS science mission for the future. This final chapter identifies the important linkages that must be realized and maintained for this integration to occur.

Open-File Report↗

USGS2023

A review of common natural disasters as analogs for asteroid impact effects and cascading hazards

Modern civilization has no collective experience with possible wide-ranging effects from a medium-sized asteroid impactor. Currently, modeling efforts that predict initial effects from a meteor impact or airburst provide needed information for initial preparation and evacuation plans, but longer-term cascading hazards are not typically considered. However, more common natural disasters, such as volcanic eruptions, earthquakes, wildfires, dust storms, and hurricanes, are likely analogs that can provide the scope and scale of these potential effects. These events, especially the larger events with cascading effects, are key for understanding the scope and complexity of mitigation, relief, and recovery efforts for a medium-sized asteroid impact event. This paper reviews the initial and cascading effects of these natural hazards, describes the state of the art for modeling these hazards, and discusses the relevance of these hazards to expected long-term effects of an asteroid impact. Emergency managers, resource managers and planners, and research scientists involved in mitigation and recovery efforts would likely derive significant benefit from a framework linking multiple hazard models to provide a seamless sequence of related forecasts.

Natural Hazards↗

USGS2011

Probabilistic estimates of number of undiscovered deposits and their total tonnages in permissive tracts using deposit densities

Empirical evidence indicates that processes affecting number and quantity of resources in geologic settings are very general across deposit types. Sizes of permissive tracts that geologically could contain the deposits are excellent predictors of numbers of deposits. In addition, total ore tonnage of mineral deposits of a particular type in a tract is proportional to the type’s median tonnage in a tract. Regressions using size of permissive tracts and median tonnage allow estimation of number of deposits and of total tonnage of mineralization. These powerful estimators, based on 10 different deposit types from 109 permissive worldwide control tracts, generalize across deposit types. Estimates of number of deposits and of total tonnage of mineral deposits are made by regressing permissive area, and mean (in logs) tons in deposits of the type, against number of deposits and total tonnage of deposits in the tract for the 50th percentile estimates. The regression equations ( R 2 = 0.91 and 0.95) can be used for all deposit types just by inserting logarithmic values of permissive area in square kilometers, and mean tons in deposits in millions of metric tons. The regression equations provide estimates at the 50th percentile, and other equations are provided for 90% confidence limits for lower estimates and 10% confidence limits for upper estimates of number of deposits and total tonnage. Equations for these percentile estimates along with expected value estimates are presented here along with comparisons with independent expert estimates. Also provided are the equations for correcting for the known well-explored deposits in a tract. These deposit-density models require internally consistent grade and tonnage models and delineations for arriving at unbiased estimates.

Natural Resources Research↗