United States Geological Survey Research Articles

Seismic Activity Analysis in California: Patterns, Trends, and Predictive Modeling

For decades, California has been considered one of the most seismically active areas in the United States, if not the most, due to the frequent occurrence of earthquakes from tectonic activity, notably along the San Andreas Fault. Understanding seismic activity is a matter not only of safety but also of major importance for urban planning. In the recent past, Machine learning algorithms (ML) have emerged as a promising invention for advancing earthquake prediction by facilitating the pinpointing of patterns in large datasets that may be hard to detect through traditional techniques. The primary objective of this research project is to assess historical seismic data to identify trends and patterns in California’s seismic activity. Equally important, the goals of this research focused on developing predictive models that can provide insight into the possibility of seismic events in the future. To assess seismic activity in California, data were gathered from a credible and reputable source, most notably, the United States Geological Survey (USGS) Earthquake Database, which provides detailed records of earthquakes spanning over six decades. This dataset included all recorded seismic events in California, capturing the key details about the place of occurrence in latitude and longitude, the magnitude of the seismic event, the depth at which it happened, and the time when the seismic activity took place. To devise and curate a reliable earthquake prediction algorithm for California, distinct machine learning models were considered, comprising, Random Forest, XG-Boost, and Logistic Regression. Overall, the Random Forest algorithm exemplified high accuracy. Optimizing this algorithm will lead to more reliable predictions, potentially aiding disaster preparedness and risk mitigation in California's earthquake-prone areas. The findings from seismic activity analysis have deep implications for urban planning and disaster preparedness in California. Knowledge of the pattern, place, and magnitude of earthquakes over time will help urban planners and policymakers structure communities that can remain resilient during such calamities. For instance, higher earthquake frequencies would automatically call for increased stringency in building codes, especially along fault lines, to ensure that houses situated on such lines are designed to withstand major seismic activity. This operation may even limit or reallocate urban development out of high-risk zones into less risky zones.

Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data

The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for automated flood inundation mapping, existing segmentation models face challenges due to noises like cloud cover and tree canopies. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), terrain guidance was utilized by recent graphical models such as hidden Markov trees (HMTs) to improve segmentation quality. Unfortunately, these methods either can only handle a small area where water levels at different locations are assumed to be consistent, or require restricted assumptions such as there is only one river channel. This paper presents an algorithm for flood extent mapping on large-scale Earth imagery, applicable to a large geographic area with multiple river channels. Since water level can vary a lot from upstream to downstream, we propose to detect river pixels in order to partition the remaining pixels into localized zones, each with a unique water level. In each zone, water at all locations flow to the same river entry point. Pixels in each zone are organized by an HMT to capture water flow directions guided by elevations. Moreover, a novel regularization scheme is designed to enforce inter-zone consistency by penalizing pixel-pairs of adjacent zones that violate terrain guidance. Efficient parallelization is made possible by coloring the zone adjacency graph to identify zones and zone-pairs that have no dependency and hence can be processed in parallel, and incremental one-pass terrain-guided scanning is conducted wherever applicable to reuse computations. Experiments demonstrate that our solution is more accurate than existing solutions, and can efficiently and accurately map out flooding pixels in a giant area of size 24,805 × 40,129. Despite the imbalanced workloads caused by a few large zonal HMTs dominating the serial computing time, our parallelization approach is effective and manages to achieve up to 14.3 × speedup on a machine with Intel Xeon Gold 6126 CPU @ 2.60GHz (24 cores, 48 threads) using 32 threads.

Nature vs. nurture: Drivers of site productivity in loblolly pine (Pinus taeda L.) forests in the southeastern US

Forest productivity is one of the most important aspects of forest management, landscape planning, and climate change assessment. However, although there are multiple elements known to affect productivity, most of them rely on the “nature” of the edaphic, climatic, and geographic conditions, and only some specific aspects can be modified through forest management or “nurture”. Through evaluation of site resource availability and an understanding of the main drivers of productivity, management can present solutions to overcome site resource limitations to productivity. Therefore, understanding the implications of a specific management regime requires understanding what drives productivity across large spatial extents and among different management regimes. In this study, we used data from over 1 million hectares of industrial forestland, covering over 6000 different soils and several management regimes of Pinus taeda L. plantations, as well as plot-based data from the Forest Inventory and Analysis (FIA) program, facilitating a comparison of planted and natural Pinus taeda stands. Combined with US Geological Survey LiDAR data, we computed site index and generated wall-to-wall productivity maps for planted Pinus taeda stands in the southeastern US, as well as point-based site index estimates for the FIA dataset. We modeled site index using a random forest algorithm considering edaphic, geologic, and physiographic province information based on the Forest Productivity Cooperative “SPOT” system, and also included climate and management history data. Our model predicted site index with an R2 of 0.701 and RMSE of 1.41 m on the industrial data and R2 of 0.417 and RMSE of 1.84 m for the FIA data. We found that year of establishment of the forest, physiographic province, and geology, were the most important drivers of site index. The soil classification modifier indicating root restrictions were the most important soil-specific variable. Additionally, we found an average increase in site index of 3.05 m since the 1950s for all FIA data, and an average increase of 4.73 m for all industrial data since the 1970s. For the latest period analyzed (2000–2012), average site index in planted FIA plots was 1.2 m higher than naturally regenerated FIA plots, and site index in all industrial forestland had a site index almost 3 m greater than planted FIA plots. Overall, we believe this work sets the foundation for better understanding of forest productivity and highlights the importance of intensive silviculture to improve productivity, and as an additional tool to achieve the economic, environmental, and social objectives.

Development of a 3D Geologic Model Used in the Seismic Hazard and Liquefaction Hazard Analysis of Madison County, Tennessee

ABSTRACT Madison County of western Tennessee is approximately 100 km southeast of the New Madrid seismic zone and, thus, is subject to potentially dangerous seismic shaking and liquefaction events. Holocene floodplain alluvium and associated Pliocene/Pleistocene terraces of the Forked Deer and Hatchie Rivers cover a large part of Madison County. Underlying these unconsolidated sediments are approximately ∼650 m of poorly consolidated Paleogene and Cretaceous terrestrial and marine sediments, which are underlain by Paleozoic limestone bedrock. Uniform regional geology used in the 2014 United States Geological Survey seismic hazard maps assumes a homogenous seismic velocity structure above bedrock. Three-dimensional geologic mapping of the ∼700 m of Quaternary, Neogene, Paleogene, and Cretaceous sediments conducted in this study provides a more detailed velocity structure, resulting in a more accurate and detailed map of the expected ground motions in Madison County during future earthquakes. In addition, the surface geologic map reveals regions that are susceptible to liquefaction. This paper discusses the methods used to construct the surface geology map, subsurface structure contour maps, isopach maps, and 3D geologic model used in the seismic hazard and liquefaction hazard analyses of Madison County, Tennessee, and summaries of the results of these analyses are presented.

Analysis of Land Use and Occupation in a Neighborhood of the City of Manaus-AM Using Geotechnologies

Objective: The objective of this study is to analyze the land use and occupation that occurred between 1999 and 2021 in the Tarumã neighborhood located in the city of Manaus-AM, with the aim of carrying out, through remote sensing techniques, the investigation of the environmental impacts that occurred, considering the classes of soil, vegetation and hydrography. Theoretical Framework: The accelerated population expansion and the lack of environmental planning contribute to the increasing degradation of natural resources. The disorderly distribution of the territory represents one of the main problems in Manaus. Method: Land use and occupation mapping was obtained from the supervised classification of Landsat 7 and Landsat 8 satellite images, RGB bands, through the United States Geological Survey – USGS (Earth Explore). Data comparisons were made based on the temporal analysis of the images and shapefile. Results and Discussion: The results obtained identified the class transformations that occurred in the last 22 years, which corresponds to 41.71% of primary vegetation, 8.98% of secondary vegetation, 17.70% of exposed soil. In the urban area, there was interference of 30.16%, with a 1.74% decrease in the water class, including the Environmental Preservation Area Research Implications: The practical and theoretical implications of this study favor the understanding, measurement and prediction of environmental impacts caused by the ordered and disordered urban occupation of cities. Originality/Value: This study contributes to the monitoring of occupations in the Tarumâ neighborhood, contributing to decision-making in preservation areas.

Evaluating the impact of uncertainty in ground motion forecasts for post-earthquake impact modeling applications

The US Geological Survey’s (USGS) ShakeMap system provides a rapid characterization of strong ground shaking in areas directly affected by an earthquake. This study focuses on studying the aggregate effects of macroseismic shaking estimates from ShakeMap, expressed in terms of modified Mercalli intensity (MMI), when accounting for the uncertainty in forecasted ground motions. We use a Monte Carlo approach to generate numerous spatially correlated realizations of ground motions by utilizing a combination of circulant embedding and kriging techniques for efficiently handling the correlations. We then assessed the aggregate effects of shaking by looking at bin counts across these realizations. We demonstrate that the aggregate shaking regarding the mean macroseismic intensity estimates (from the ShakeMap output) is a biased representation of the aggregate shaking when shaking uncertainty is included. Incorporating shaking uncertainty can help to improve various downstream earthquake impact applications, such as the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) overall earthquake fatality distribution or estimates of shaking-induced ground failure impacts from consequential earthquakes.

Recommendations for Better Collaborative Groundwater Monitoring for the US High Plains Aquifer

Groundwater is a critical resource for the Great Plains region of the United States, providing drinking water for over 2 million people. However, the High Plains Aquifer (HPA) is under significant threat from over-extraction—defined as the excessive withdrawal of groundwater beyond its natural replenishment rate. This overuse jeopardizes not only water availability but also equitable access and allocation. Effective groundwater monitoring is essential to track trends in water availability, assess the impacts of extraction, and develop strategies to ensure long-term sustainability. Without comprehensive monitoring, it is difficult to address key issues like contamination, depletion, and groundwater quality degradation. While several state-level frameworks exist to enhance groundwater monitoring, they operate independently, leading to gaps in data sharing and collaboration, especially for a transboundary resource like the HPA. The Environmental Protection Agency (EPA) and U.S. Geological Survey (USGS) are well-positioned to play a more central role in this process. The EPA, with its mandate to protect water quality, and the USGS, with its expertise in nationwide data collection, are critical to supporting a collaborative and comprehensive groundwater monitoring system. By facilitating the integration of state-level efforts, these federal agencies can help ensure that groundwater monitoring is both consistent and accurate, enabling effective decision-making at regional and national levels. This policy memo provides a detailed analysis of current state-level efforts, highlights the role of the EPA and USGS in addressing governance challenges, and proposes a transboundary governance mechanism to enhance collaborative groundwater monitoring. The intended audience includes policymakers at the EPA and USGS, as well as water resource managers who are key to implementing these changes.

HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous US

Regional- and continental-scale hydrologic models are increasingly important forecasting tools, yet they rely on highly variable channel parameters (e.g. width, depth, hydraulic resistance) that remain unquantified for millions of stream reaches across the country. Existing hydrologic models utilize over-simplified channel geometries that directly impact the accuracy of streamflow estimates, with cascading effects for water resource and hazard prediction. Here, we present a hydraulic geometry dataset, termed ‘HyG’, derived from discharge field measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). The HyG dataset includes (1) at-a-station hydraulic geometry parameters, (2) at-a-station hydraulic resistance (Manning’s n) calculated from the Manning equation, (3) daily discharge percentiles, and (4) regionalized downstream hydraulic geometry parameters based on HUC4 (Hydrologic Unit Code 4), derived from a total of 4,051,682 individual measurements from 66,841 total gages. The regionalized HyG dataset can be used directly to improve channel representations in models over CONUS. The original HyG relationships can also be regionalized for finer scales if required.

A Perspective on the Rupture States of Complex Earthquake Scenarios in the 2023 USGS Seismicity Model for the Western U.S. Faults

Abstract The 2023 U.S. Geological Survey (USGS) seismicity model for faults in the western United States (USGS-SMF-WUS) includes many earthquake scenarios (EQS) that rupture multifault sections (MFS), reflecting decades of observations that large crustal earthquakes often rupture sections of different neighboring faults in complex ways. Many of the complex MFS scenarios include rupture sections, on different faults, that are not contiguous, with spacing up to 15 km. The USGS-SMF-WUS uses an inversion methodology, referred to as magnitude-rate optimization, to optimally formulate the occurrence rates of the EQS in such a way that they collectively release the accumulating seismic moment rates on faults, at subsection level, defined by various types of geologic, geodetic, and seismologic data and constraints. This raises questions about the treatment of the MFS scenarios in the USGS-SMF-WUS regarding their rupture states and how that could potentially impact the magnitude-rate optimization and ground motions (GMs) for hazard analysis. Among many different possible rupture states for the MFS earthquakes, the two extreme scenarios could be visualized as (1) a series of noncontiguous sections rupturing in such a way that their total seismic moment and GM footprints are scaled with the sum of their rupture areas and (2) a series of noncontiguous triggered events, grouped together based on the spacing between the participating fault sections and other criteria, each releasing seismic moment and generating GM footprints that are scaled with its own rupture area. It will be shown later that the average slips on the participating sections for these two extreme rupture states would not be the same. In addition, the GM footprints from these two rupture states would be different and the differences are frequency dependent. This is especially the case when considering the complexity of rupture directivity on the participating fault sections in the USGS-SMF-WUS. It is the objective of this study to explore and discuss the implications of the triggered rupture state assumption in estimating the average slips and GM footprints of the MFS earthquakes in the USGS-SMF-WUS.

Comparison of groundwater distribution, exchange and storage between cropland and forestland over the past 115 years

ABSTRACT Using the US Geological Survey groundwater model, we compared spatial distribution, flow exchange, and storage of groundwater between cropland and forestland in the Upper Yazoo River Watershed, Mississippi, from 1900 to 2014. Under normal climate, average groundwater head declined 2.7 m in cropland but only 1 m in forestland over the 115 years. The average groundwater flow from forestland to cropland surpassed the reverse flow by a factor of 238, owing to a higher elevation in forestland and intensive groundwater pumping in cropland. Cropland was a net groundwater sink while forestland was a net groundwater source under all climates. Our findings underscore the discernible impacts of climate changes on groundwater storage and suggest that afforestation in the region would be an alternative for saving groundwater resources and supplying more waters to croplands. This study offers valuable insights into groundwater supply planning not only in Mississippi but also in comparable situations worldwide.

Field-scale variability and dynamics of soil moisture in Southwestern Nigeria

This research leverages field-based sensors and Geographic Information Systems (GIS) to elucidate soil moisture dynamics across diverse land uses in Southwestern Nigeria, including Cocoa, Cassava, Oil Palm, and Riparian ecosystems. The study synthesizes soil moisture data and geospatial coordinates from strategically selected land covers with satellite-derived soil moisture records from NASA's Prediction of Worldwide Energy Resources (POWER) and the US Geological Survey. Through a robust analytical framework combining cross-correlation functions, wavelet analysis, and inverse distance weighting interpolation, we revealed distinct soil moisture patterns ranging from 0 to 47.9% in arable farms, 0 to 46.8% in built-up areas, 0 to 49.9% in cocoa farms, 0.2 to 49.7% in oil palm farms, and 5.6 to 94.1% in riparian vegetation. The coefficient of determination values between 0.7 and 0.97 (p ≤ 0.05) underscore the significant influence of periodic land use on soil moisture during the study period. A comparative analysis of ground-based and satellite data revealed a potential overestimation of minimum soil moisture values by 66% and an underestimation of maximum values by 4%. This investigation provides critical insights into the interplay between hydrological variables and agro-meteorological factors, informing the management and assessment of agricultural resources.

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

How has the latest IMERG V07 improved the precipitation estimates and hydrologic utility over CONUS against IMERG V06?

Precipitation, a crucial element of the water cycle, significantly impacts surface streamflow and flooding dynamics. The latest version of Integrated Multi-satellitE Retrievals for GPM (IMERG V07) has garnered global attention for its advancements over its predecessor, IMERG V06. However, the improvement in precipitation rates has not yet been fully quantified, especially when translated into improvements in hydrologic predictions. In this study, we aim to quantify the improvements of IMERG V07 over V06 in the contiguous United States (CONUS) in the aspects of (1) Evaluating the accuracy of precipitation data against Multi-Radar Multi-Sensor (MRMS); and (2) Comparing their hydrologic performance using a hydrologic model, the Coupled Routing and Excess Storage (CREST), against United States Geological Survey (USGS) streamgages. This study mainly finds that: (1) Metrics for both precipitation and streamflow from CREST show that IMERG V07 significantly outperforms IMERG V06. Specifically, the CC improved from 0.391 to 0.443 for precipitation and from 0.487 to 0.515 for streamflow; (2) The improvements in IMERG V07 are region-dependent. Significant improvements are found in basins with small areas (< 1000 km2), in mid-latitudes (41° N to 43° N), at low average elevations (< 800 m), and those located in the northeastern CONUS; (3) In certain cases, IMERG V07 demonstrates a better capability in estimating extreme precipitation, whereas IMERG V06 tends to underestimate it. This is also reflected in the streamflow data, where IMERG V07 better captures flood peaks compared to IMERG V06. This research enhances our understanding of flood dynamics by analyzing IMERG V07′s advancements and their effects on hydrologic predictions. It offers valuable insights into improved precipitation data’s role in hydrological modeling, giving potential benefits for simulating better flood prediction and helping in water management.

Preliminary Observations of the 5 April 2024 Mw 4.8 New Jersey Earthquake

Abstract On 5 April 2024, 10:23 a.m. local time, a moment magnitude 4.8 earthquake struck Tewksbury Township, New Jersey, about 65 km west of New York City. Millions of people from Virginia to Maine and beyond felt the ground shaking, resulting in the largest number (&amp;gt;180,000) of U.S. Geological Survey (USGS) “Did You Feel It?” reports of any earthquake. A team deployed by the Geotechnical Extreme Events Reconnaissance Association and the National Institute of Standards and Technology documented structural and nonstructural damage, including substantial damage to a historic masonry building in Lebanon, New Jersey. The USGS National Earthquake Information Center reported a focal depth of about 5 km, consistent with a lack of signal in Interferometric Synthetic Aperture Radar data. The focal mechanism solution is strike slip with a substantial thrust component. Neither mechanism’s nodal plane is parallel to the primary northeast trend of geologic discontinuities and mapped faults in the region, including the Ramapo fault. However, many of the relocated aftershocks, for which locations were augmented by temporary seismic deployments, form a cluster that parallels the general northeast trend of the faults. The aftershocks lie near the Tewksbury fault, north of the Ramapo fault.

THE POPULATION OF MILA FACING THE EARTHQUAKE OF AUGUST 07, 2020

The surge in urbanization within regions vulnerable to seismic activity poses a significant escalation in seismic risks. Effective mitigation strategies, reliant on improved architectural practices and behavioral adaptations, are imperative to curtail the toll of seismic events. Seismic vulnerability, intricately intertwined with seismic hazard, building conditions, and populace preparedness, underscores the need for comprehensive risk assessment. Although studies elucidating seismic risk knowledge and behaviors are scant in Algeria, they are more prevalent in Mediterranean regions. This paper delves into the examination of risk culture, discerning both direct and indirect causative factors behind the seismic disaster that befell Mila on August 7, 2020.Employing a research methodology that integrates the method Did You Feel It (DYFI USGS ) with a survey model used in Beirut, Lebanon.The findings offer notable insights for two principal reasons. Firstly, local perceptions of the seismic event's severity surpassed estimates by the US Geological Survey (USGS). Secondly, despite the relatively minor magnitude of seismic effects in the El Kherba neighborhood, the populace displayed inadequate preparedness for future earthquakes. Additionally, site-specific factors exerted substantial influence on crisis outcomes. In this context, the preparedness of Algerian society for seismic risks depends on understanding the phenomenon, recognizing associated hazards, and identifying available resources. Contribution: this study serves as a pivotal resource for fostering proactive local population preparedness and mitigating natural risks. Furthermore, it seeks to augment the efficacy of policies aimed at managing natural disaster risks in Algeria.

Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes

Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.

USING THE INTEGRAL INDICATOR TO DETERMINE TI-ZR PRIORITY AREAS OF PLACERS

The article presents the author’s method of selection of priority areas of Ti-Zr placers using an integral indicator. As you know, the integral indicator is the ratio of the total beneficial effect from the development of the deposit to the total costs of operation, it is calculated as the difference between the value of conventional ilmenite (according to the US Geological Survey) (Mineral Commodity..., 2023) and the cost of obtaining overburden and processing the productive layer. Based on the methodology developed and presented in the article, an experiment was conducted on the data of one of the placer deposits located within the Ukrainian placer sub-province. It is proposed to develop it in small blocks that ensure high productivity, minimal impact on the environment and allow replenishment the mineral and raw material base of the titanium industry of Ukraine. This technique can be used at any deposit of titanium-zirconium ores both in Ukraine and abroad.

A global fairtrade partnership needed to address injustices in the supply chains of clean energy technology materials

Abstract Renewable sources produced close to one-third of the world’s electricity in 2023. However, a limited but growing body of research suggests rapid renewable energy development is leading to conflict and resource exploitation in energy-transitioning communities. Such injustices are attributable to the extractivist nature of renewable energy development, where raw materials, also known as Clean Energy Technology Materials (CETMs), are in limited quantities and often concentrated in resource-constrained zones in the Global South. In this perspective, we call for an urgent need for energy justice considerations in CETM’s supply chain. We used demand projection data from 2020 to 2040 to look into the effects of important CETMs like nickel, cobalt, and lithium on distributive justice. We also examined the potential of these effects to tackle systemic injustices such as conflict, labor exploitation, and transactional colonialism. Next, we analyzed global mining production data from the United States Geological Survey using a CETM life cycle lens and found that increasing demand for these materials is exacerbating restorative injustices, particularly in the Global South. Finally, building on the above evidence, we called for the creation of multi-stakeholder partnerships and the establishment of fair trade standards across the critical CETM supply chain. Graphical abstract Highlights Here, we analyzed the projected demand growth for selected clean energy technology materials by 2040 relative to 2020 levels using data from the International Energy Agency, visualized their global mining production using data from the United States Geological Survey, explained how the demand for these materials is exacerbating certain injustices, and recommended multi-stakeholder partnerships across the supply chain of these materials. Discussion The rapid growth of renewable energy technologies is creating injustices throughout the supply chain of clean energy technology materials (CETM). A lack of any energy justice framework across CETMs’ extraction, processing, decommissioning, and recycling is exacerbating restorative injustices, especially in the Global South. By examining the projected demands and geospatial patterns for the extraction of minerals, metals, and other materials essential for clean energy technology development, the inequities faced by impoverished, marginalized, and Indigenous communities become apparent. We argue that if coffee can have fair trade standards across its supply chain, why can’t we have similar considerations for the CETMs? There is a need to include transparency in the sustainability, ethics, and energy efficiency of CETM extraction and processing through global partnerships across its supply chain.

Aerial imagery dataset of lost oil wells

Orphaned wells are wells for which the operator is unknown or insolvent. The location of hundreds of thousands of these wells remain unknown in the United States alone. Cost-effective techniques are essential to locate orphaned wells to address environmental problems. In this paper, we present a dataset consisting of 120,948 aerial images of recently documented orphan wells. Each of these 512 × 512 images is paired with segmentation masks that indicate the presence or absence of such well. These images, sourced from the National Agriculture Imagery Program, cover the continental United States with spatial resolutions ranging from 30 centimeters to 1 meter. Additionally, we included negative examples by selecting locations uniformly across the United States. Accompanying metadata includes the IDs and spatial resolution of the original images, which are available for free through the United States Geological Survey, and the pixel coordinates of documented orphaned wells identified in these images. This dataset is intended to support the development of deep-learning models that can help locating undocumented orphan wells from such imagery, thereby blunting the environmental damage they do.

Enhancing glacier monitoring through adaptive smoothing of MODIS NDSI time series

ABSTRACT Observation of glacier surface characteristics through remotely sensed time-series data is essential for understanding glacier seasonality, mass balance, and long-term trends. Yet, the reliability of these observations depends significantly on the quality of the time-series data. This study presents a meticulous preprocessing scheme to improve the quality of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) time-series data for glacier monitoring. We propose a three-step algorithm specifically crafted to overcome the challenges associated with cloud contamination reduction, outlier removal and data gap handling. This innovative approach iteratively compares the median values of automatically adjusted asymmetrical moving windows to achieve convergence, removing outliers using minimal window size to keep the temporal resolution as high as possible. The methodology’s effectiveness is demonstrated through its application to two glaciers from the United States Geological Survey (USGS) Benchmark Project, showcasing significant improvements in the quality of smoothed MODIS NDSI time series. These results affirm the efficacy of the proposed scheme in rendering a more reliable evaluation of glacier surface condition and seasonal fluctuations. Consequently, this study contributes significant methodological advancements to the fields of remote sensing and glaciology, enhancing the accuracy of glacier monitoring techniques.