United States Geological Survey Research Articles

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Wetland Changes and Ecosystem Services Valuation of Kapla Beel in Assam

Background: Wetlands are productive ecosystem with immense potentiality of providing various goods and services. But they are fast depleting due to various natural and anthropogenic causes. Kapla beel is a freshwater wetland located in Barpeta district in the lower Brahmaputra valley of Assam. Area of the Kapla beel has been decreasing over the years. This study shall make an attempt to analyse the causes of land use and land cover changes of Kapla beel of Assam and assess its ecosystem services value changes during the period 1990 to 2022. Methods: To assess the changes, two Landsat images for the year 1990 and 2022 of Kapla beel were taken from the United State Geological Survey (USGS) Earth Explorer. Land-use change analysis was assessed by comparing the areas occupied by each land use in each period using the ArcGIS software. The dynamicity index is used to calculate the rate of change in land cover. In order to assess the changes in the wetland ecosystem service value due to wetland changes, Benefit Transfer Method is used which involves transferring monetary values of ecosystem services from previous studies. Result: The valuation of ecosystem services underlines the fact that wetlands are highly productive and economically viable ecosystems. Accounting of ecosystem services provides information about the need for optimal decision -making towards judicious use of wetland resources for their sustenance.

Implementation and experience of using methods for analyzing the isotopic composition of Sm and Nd, Rb and Sr in rock samples at the Geoanalitik Center for Collective Use

Research subject. AGV-2 and BHVO-2 geochemical reference materials for studying the Sm, Nd and Rb, Sr isotope systems along with various rocks and mineral samples. Materials and Methods. An analysis of the isotopic composition of Sm, Nd and Rb, Sr was carried out using Neptune Plus and Triton Plus mass spectrometers. Aim. To implement analytical techniques for the isotopic composition of Sm and Nd, Rb, and Sr in various rock and mineral samples using two types of multicollector mass spectrometers – inductively coupled plasma NeptunePlus and thermal ionization TritonPlus (Thermo-Fisher), as well as a description of the procedure of processing experimental data and the experience in using techniques at the “Geoanalitik” shared research facilities of the IGG UB RAS for the period 2015-2023. Results. The analytical techniques implemented included (1) column chromatography using various ion-exchange resins, optimised for the ratio of labour costs/quality of analytical results; (2) the measurement of isotope ratios using two types of mass spectrometers; (3) the correction of mass bias of isotope ratios and the determination of Sm and Nd, Rb, and Sr concentrations by the isotope dilution method using 149Sm+150Nd and 85Rb+84Sr spikes. Testing of the techniques was carried out using the AGV-2 and BHVO-2 geochemical reference materials; their metrological characteristics were presented. When using TritonPlus, the reproducibility (BHVO-2, n=60) of measurements of 143Nd/144Nd, 147Sm/144Nd isotope ratios and Sm and Nd concentrations are ±0.000020, ±0.0004, ±1.3 and ±0.4, respectively; indicators of correctness of determining the ratios 143Nd/144Nd and 147Sm/144Nd – 0.001 and 0.25% and concentrations of Sm and Nd – 2%; the reproducibility (BHVO-2, n=63) of measurements of 87Sr/86Sr, 87Rb/86Sr isotope ratios and concentrations (Rb and Sr) are ±0.0025%, ±1.5%, ±2%, respectively. The uncertainty of a single measurement of the 143Nd/144Nd and 87Sr/86Sr isotope ratio, represented by the standard error of the average single measurement in the sample, does not exceed 0.0025%. Conclusions: the results obtained for the geochemical reference materials are in satisfactory agreement with those provided in the GeoReM database, as well as with the certified values provided by the United States Geological Survey (USGS). The described analytical techniques are used at the “Geoanalitik” shared research facilities of the IGG UB RAS to analyse various rock and mineral samples. The work presents a number of experimental results obtained, which are subsequently used in geochronological applications.

The 2023 US National Seismic Hazard Model: Subduction ground-motion models

The US Geological Survey National Seismic Hazard Models (NSHMs) are used to calculate earthquake ground-shaking intensities for design and rehabilitation of structures in the United States. The most recent 2014 and 2018 versions of the NSHM for the conterminous United States included major updates to ground-motion models (GMMs) for active and stable crustal tectonic settings; however, the subduction zone GMMs were largely unchanged. With the recent development of the next generation attenuation-subduction (NGA-Sub) GMMs, and recent progress in the utilization of “M9” Cascadia earthquake simulations, we now have access to improved models of ground shaking in the US subduction zones and the Seattle basin. The new NGA-Sub GMMs support multi-period response spectra calculations. They provide global models and regional terms specific to Cascadia and terms that account for deep-basin effects. This article focuses on the updates to subduction GMMs for implementation in the 2023 NSHM and compares them to the GMMs of previous NSHMs. Individual subduction GMMs, their weighted averages, and their impact on the estimated mean hazard relative to the 2018 NSHM are discussed. The updated logic trees include three of the new NGA-Sub GMMs and retain two older models to represent epistemic uncertainty in both the median and standard deviation of ground-shaking intensities at all periods of interest. Epistemic uncertainty is further represented by a three-point logic tree for the NGA-Sub median models. Finally, in the Seattle region, basin amplification factors are adjusted at long periods based on the state-of-the-art M9 Cascadia earthquake simulations. The new models increase the estimated mean hazard values at short periods and short source-to-site distances for interface earthquakes, but decrease them otherwise, relative to the 2018 NSHM. On softer soils, the new models cause decreases to the estimated mean hazard for long periods in the Puget Lowlands basin but increases within the deep Seattle portion of this basin for short periods relative to the 2018 NSHM.

Burden of malignant mesothelioma in China during 1990-2019 and the projections through 2029

ObjectiveTo provide the most up-to-date data on the burden of malignant mesothelioma (MM) and the projections through 2029 in China. MethodsData on patients diagnosed with MM from China during 1990-2019 were obtained from the Global Burden of Disease (GBD) 2019 database, including annual cases and deaths data and age-standardized rates of incidence, mortality, and disability-adjusted life-years (DALYs) associated with MM among different age groups. Temporal trends during 1990-2019 were analyzed by the Joinpoint regression models using 95% confidence interval (CI), while the projections through 2029 were calculated by Bayesian age-period-cohort model. Data on the production and consumption of asbestos in China were obtained from the United States Geological Survey on Mineral Commodity Summaries during 1996-2023. ResultsWe observed a significant elevation in incident new cases and deaths over last 3 decades, increasing from 1193 in 1990 to 2815 in 2019 for incident cases and from 1134 in 1990 to 2773 in 2019 for death cases. We found roughly 6% increase in the proportion of incident cases for those aged > 70 years (30% in 2019 versus 24% in 1990), while for the proportion of deaths similar elevation for those aged > 70 years was also found. Additionally, men had significantly higher DALYs due to MM across age groups compared to women. Asbestos consumption in China dramatically dropped since 2012 and reached bottom in 2017 with 230 kilotons. By 2029, the projected age-standardized rate for incidence and mortality is expected to reach 1.2 per million for both. ConclusionWe found, for the first time using GBD data on the Chinese population, that the burden of MM has been significantly increasing in China over the last three decades and will continue to increase in the upcoming decade, suggesting an urgent need for a complete ban on chrysotile asbestos in China.

Tracing the contribution of cattle farms to methane emissions through bibliometric analyses

Methane contributes to global warming, and livestock is one of the sources of methane production. However, methane emission studies using bibliometric tools in livestock are lacking. Given the negative impact of climate change on the ecosystem and the rise in methane emissions, it is essential to conduct a bibliometrics study to provide an overview and research trends. We used the Bibliometrix package and VOSviewer to decipher bibliometric indices for methane emissions in cattle farms (MECF). Current dataset were collected from the Web of Science (Core Collection) database, and 8,998 publications were analyzed. The most co-occurring keywords scientists preferred were methane (1528), greenhouse gas (443), methane emissions (440), and cattle (369). Methane was the most frequently used keyword in the published scientific literature. Thematic evolution of research themes and trend results highlighted carbon dioxide, methane, dairy cattle, cattle, and risk factors during 1999-2017. Chinese Academy of Sciences ranked on top with 485 publications, followed by Agriculture & Agri-Food Canada, University of Colorado, National Oceanic and Atmospheric Administration, and Aarhus University. Chinese Academy of Sciences was also the most cited organization, followed by the University of Colorado, Agriculture & Agri-Food Canada, National Oceanic and Atmospheric Administration, and United States Geological Survey. Source analysis showed that the Science of the Total Environment was cited with the highest total link strength. “Science of the Total Environment” ranked first in source core 1 with 290 citation frequencies, followed by “Journal of Dairy Science” with 223 citation frequencies. Currently, no bibliometric study has been conducted on MECF, and to fill this knowledge gap, we carried out this study to highlight methane emissions in cattle farms, aiming at a climate change perspective. In this regard, we focused on the research productivity of countries authors, journals and institutions, co-occurrence of keywords, evolution of research trends, and collaborative networking. Based on relevance degree of centrality, methane emissions and greenhouse gases appeared as basic themes, cattle, and dairy cattle appeared as emerging/declining themes, whereas, methane, greenhouse gas and nitrous oxide appeared to fall amongst basic and motor themes. On the other hand, beef cattle, rumen and dairy cow seem to be between motor and niche themes, and risk factors lie in niche themes. The present bibliometric analysis provides research progress on methane emissions in cattle farms. Current findings may provide a framework for understanding research trends and themes in methane emissions in cattle farms (MECF) research.

County-Level Atrazine Use and Gastroschisis

The incidence of gastroschisis, a birth defect involving the herniation of the small bowel through the abdominal wall, has increased in the US since the 1960s. The pesticide atrazine is a hypothesized cause of gastroschisis; however, examination of the association between atrazine and gastroschisis has been limited. To evaluate national trends in gastroschisis incidence, maternal and infant characteristics associated with gastroschisis, and whether county-level atrazine use is associated with gastroschisis. This retrospective, repeated cross-sectional study examined birth certificate data of all live births in the US and data on atrazine use from the US Geological Survey from January 1, 2009, through December 31, 2019. The data analysis was performed between August 5, 2021, and May 26, 2023. County-level atrazine use. The primary outcome was gastroschisis incidence. Covariates included maternal age, race and ethnicity, body mass index (measured by weight in kilograms divided by height in meters squared), parity, insurance type, Chlamydia infection during pregnancy, smoking, and rurality. Mixed-effects logistic regression models (year fixed effects and county random effects) were constructed using different county-level atrazine exposure variables (1-, 5-, and 10-year means). Between 2009 and 2019, 39 282 566 live births were identified, with 10 527 infant diagnoses of gastroschisis. Infants with gastroschisis were more likely to have mothers who identified as non-Hispanic White (61% vs 54%; P < .001), had a lower body mass index (median [IQR], 23.4 [20.8-27.2] vs 25.4 [22.0-30.8]; P < .001), were more likely to be nulliparous (median [IQR], 0 [0-1] vs 1 [0-2]; P < .001), and were more commonly covered by Medicaid (63% vs 43%; P < .001). During the study period, the rate (per 1000 live births) of gastroschisis decreased from 0.31 (95% CI, 0.29-0.33) to 0.22 (95% CI, 0.21-0.24). The median (IQR) county-level atrazine use estimates were higher among infants with gastroschisis (1 year, 1389 [IQR, 198-10 162] vs 1023 [IQR, 167-6960] kg; 5 years, 1425 [IQR, 273-9895] vs 1057 [IQR, 199-6926] kg; 10 years, 1508 [IQR, 286-10 271] vs 1113 [IQR, 200-6650] kg; P < .001). In adjusted models, higher county levels of atrazine (each 100 000-kg increase) were associated with a higher incidence of gastroschisis (1 year: adjusted odds ratio [AOR], 1.12 [95% CI, 1.01-1.24]; 5 years: AOR, 1.15 [95% CI, 1.02-1.30]; 10 years: AOR, 1.21 [95% CI, 1.07-1.38]). In this cross-sectional study, higher county levels of atrazine were associated with infant diagnoses of gastroschisis. While atrazine is the second-most used herbicide in the US, numerous countries around the world have banned it out of concern for adverse effects on human health. These findings suggest that exploring alternatives to atrazine in the US may be warranted.

Impacts of Gorkha Earthquakes: A Comprehensive Study

Nepal is highly vulnerable to seismic activity. This review seeks to explore the impact of earthquakes on human populations in terms of casualties and injuries, as well as to pinpoint the risk factors associated with these outcomes. An earthquake measuring 7.8 on the Richter scale struck near Gorkha, Nepal, on April 25, 2015, resulting in the loss of nearly nine thousand lives, extensive property damage, and the destruction of numerous historical and cultural landmarks. Data sourced from the UNDRR, Ministry of Home Affairs, and the United States Geological Survey were analyzed using the ArcGIS software tool. The study offers a comprehensive examination of earthquake-prone regions at regional, national, and global levels. By analyzing recent significant earthquakes along with their associated losses and damages, it underscores the importance of precautionary measures and risk analysis in reducing vulnerability and enhancing preparedness among researchers and stakeholders.

State of the Data: Assessing the FAIRness of US Geological Survey Data

In response to recent shifts towards open science that emphasize transparency, reproducibility, and access to research data, the US Geological Survey (USGS) conducted a study to assess the degree to which USGS data assets meet the FAIR data principles (Findable, Accessible, Interoperable, and Reusable). The USGS designed and applied a methodology for quantitative analysis of FAIR characteristics. A new rubric was derived from a crosswalk of existing FAIR evaluation frameworks and customized for the USGS. The rubric, consisting of 62 yes/no questions, was applied to 392 metadata records of USGS data products published between 1987 and 2022. Results were analyzed to show which FAIR characteristics were most and least present in the metadata and how these scores changed after the implementation of data policy requirements in 2016. Aggregated scores showed specific areas of strength and needed improvements. The greatest increases in FAIR scores over time were for elements that were required by new data policies, especially in the ‘Findable’ category. Based on the results, this paper presents strategies to further improve USGS alignment with FAIR. The suggested strategies are organized in four key areas: USGS data repository characteristics, training and communities of practice, data management policy considerations, and metadata standards, tools, and best practices.

Significant Earth’s responses of the 2022 Tonga eruption across Taiwan from multiple sensor observations

On 15 January 2022, a massive underwater volcano erupted in the Tonga region, releasing a significant amount of volcanic ash and gases into the atmosphere. The United States Geological Survey (USGS) estimated the seismic source to have a surface wave magnitude (Ms) of 5.8. This eruption was observed from space, and the resulting atmospheric shockwave swept across the Pacific Ocean. Reports from various locations worldwide indicated rapid fluctuations in air pressure following this event. Taiwan, situated in the western Pacific, approximately 8,500 km from the eruption source, observed significant changes. During this volcanic eruption, both rapid air pressure changes and several significant changes in the Earth’s physical parameters were observed in Taiwan. The Tonga eruption is a unique event, and comprehensive observations provide an opportunity to explore and explain the mechanisms behind this extreme event. Data from ground surface air pressure gauges, coastal tide gauges, underwater pressure gauges, infrasound sensors, digital microphones, and seismometers were collected. These data were analyzed to identify their origin and explain their interactions. The results of this study first present the detailed propagation characteristics of air pressure waves in the Taiwan region and verify the occurrence of a specific tsunami phenomenon caused by the atmospheric disturbance from the Tonga eruption. It follows a distinct mechanism, explaining its arrival time and wave height distribution around Taiwan, which is different from conventional tsunamis of tectonic origins, which are formed by rapid changes in water caused by earthquakes or underwater landslides.

Groundwater Sustainability and Land Subsidence in California’s Central Valley

The Central Valley of California is one of the most prolific agricultural regions in the world. Agriculture is reliant on the conjunctive use of surface-water and groundwater. The lack of available surface-water and land-use changes have led to pumping-induced groundwater-level and storage declines, land subsidence, changes to streamflow and the environment, and the degradation of water quality. As a result, in part, the Sustainable Groundwater Management Act (SGMA) was developed. An examination of the components of SGMA and contextualizing regional model applications within the SGMA framework was undertaken to better understand and quantify many of the components of SGMA. Specifically, the U.S. Geological Survey (USGS) updated the Central Valley Hydrologic Model (CVHM) to assess hydrologic system responses to climatic variation, surface-water availability, land-use changes, and groundwater pumping. MODFLOW-OWHM has been enhanced to simulate the timing of land subsidence and attribute its inelastic and elastic portions. In addition to extending CVHM through 2019, the new version, CVHM2, includes several enhancements as follows: managed aquifer recharge (MAR), pumping with multi-aquifer wells, inflows from ungauged watersheds, and more detailed water-balance subregions, streamflow network, diversions, tile drains, land use, aquifer properties, and groundwater level and land subsidence observations. Combined with historical approximations, CVHM2 estimates approximately 158 km3 of storage loss in the Central Valley from pre-development to 2019. About 15% of the total storage loss is permanent loss of storage from subsidence that has caused damage to infrastructure. Climate extremes will likely complicate the efforts of water managers to store more water in the ground. CVHM2 can provide data in the form of aggregated input datasets, simulate climatic variations and changes, land-use changes or water management scenarios, and resulting changes in groundwater levels, storage, and land subsidence to assist decision-makers in the conjunctive management of water supplies.

Total irrigation by crop in the Continental United States from 2008 to 2020

We provide a dataset of irrigation water withdrawals by crop, county, year, and water source within the United States. We employ a framework we previously developed to establish a companion dataset to our original estimates. The main difference is that we now use the U.S. Geological Survey (USGS) variable ‘irrigation — total’ to partition PCR-GLOBWB 2 hydrology model estimates, instead of ‘irrigation — crop’ as used in previous estimates. Our findings for Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) are similar to those of prior estimates but now have better spatial coverage, since several states are missing from the USGS ‘irrigation — crop’ variable that was originally used. Irrigation water use increases in this study, since more states are included and ‘irrigation — total’ includes more categories of irrigation than ‘irrigation — crop’. Notably, irrigation in the Mississippi Embayment Aquifer is now captured for rice and soy. We provide nearly 2.5 million data points with this paper (3,142 counties; 13 years; 3 water sources; and 20 crops).

Stage and discharge prediction from documentary time-lapse imagery

Imagery from fixed, ground-based cameras is rich in qualitative and quantitative information that can improve stream discharge monitoring. For instance, time-lapse imagery may be valuable for filling data gaps when sensors fail and/or during lapses in funding for monitoring programs. In this study, we used a large image archive (&gt;40,000 images from 2012 to 2019) from a fixed, ground-based camera that is part of a documentary watershed imaging project (https://plattebasintimelapse.com/). Scalar image features were extracted from daylight images taken at one-hour intervals. The image features were fused with United States Geological Survey stage and discharge data as response variables from the site. Predictions of stage and discharge for simulated year-long data gaps (2015, 2016, and 2017 water years) were generated from Multi-layer Perceptron, Random Forest Regression, and Support Vector Regression models. A Kalman filter was applied to the predictions to remove noise. Error metrics were calculated, including Nash-Sutcliffe Efficiency (NSE) and an alternative threshold-based performance metric that accounted for seasonal runoff. NSE for the year-long gap predictions ranged from 0.63 to 0.90 for discharge and 0.47 to 0.90 for stage, with greater errors in 2016 when stream discharge during the gap period greatly exceeded discharge during the training periods. Importantly, and in contrast to gap-filling methods that do not use imagery, the high discharge conditions in 2016 could be visually (qualitatively) verified from the image data. Half-year test sets were created for 2016 to include higher discharges in the training sets, thus improving model performance. While additional machine learning algorithms and tuning parameters for selected models should be tested further, this study demonstrates the potential value of ground-based time-lapse images for filling large gaps in hydrologic time series data. Cameras dedicated for hydrologic sensing, including nighttime imagery, could further improve results.

Homogenized gridded dataset for drought and hydrometeorological modeling for the continental United States

We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast drought at short-range, seasonal to sub-seasonal, and inter-annual timescales with uncertainty, extending the reach and value of the current US Drought Outlook from the National Weather Service Climate Prediction Center. This novel data product provides the first common gridded dataset that includes critical variables used to inform hydrological and meteorological drought.

Empirical ground-motion basin response in the California Great Valley, Reno, Nevada, and Portland, Oregon

We assess how well the Next-Generation Attenuation-West 2 (NGA-West2) ground-motion models (GMMs), which are used in the US Geological Survey’s (USGS) National Seismic Hazard Model (NSHM) for crustal faults in the western United States, predict the observed basin response in the Great Valley of California, the Reno basin in Nevada, and Portland and Tualatin basins in Oregon. These GMMs rely on site parameters such as the time-averaged shear-wave velocity ( VS) in the upper 30 m of Earth’s crust ( VS30) and depths to 1.0 and 2.5 km/s shear-wave isosurfaces ( Z1.0 and Z2.5) to capture basin effects and were developed using observations and simulations primarily from the Los Angeles region in southern California. Using ground-motion records from mostly small-to-moderate earthquakes and mixed-effects regression analysis, we find that the GMMs perform well with our local basin-depth models for the California Great Valley. With our local basin-depth models for Reno, the GMMs do not perform as well for this relatively shallow basin and exhibit little sensitivity to the basin parameters used in the NGA-West2 GMMs. We also find good performance for the local Z1.0 model across the Portland region, whereas the local Z2.5 model provides little predictive power except at sites in the deepest part of the Tualatin basin. Additional work could improve the performance of the site and basin terms in the NGA-West2 GMMs for regions with geologic structure different than the deep basins in southern California and the Great Valley. In addition, we find significant discrepancies among the GMMs in how the uncertainty in the ground motion varies with basin depth and pseudospectral period. Our results can help guide seismic hazard analyses on whether to include these local basin-depth models.

A comprehensive investigation on the effect of UHI in Baripada city using geo‐spatial technique and MLTHP model

AbstractAccording to the Times of India, Baripada city recorded the highest temperature in the world on April 14, 2023, at 43.5°C. It is located in the south‐west direction of the Similipal Biosphere Reserve. Observing the third highest temperature in the world, we are interested in finding out the reasons affecting climate change. This paper investigates the reasons for the temperature change in Baripada city and its surroundings by considering factors like Normalized Difference Vegetation Index (NDVI), Normalized Multi‐band Drought Index (NMDI), Normalized Difference Water Index (NDWI), Normalized Difference Built‐up Index (NDBI), forest fires, rainfall patterns, wind patterns, and registration of motor vehicles. For this study, data has been collected from USGS (United States Geological Survey), NASA POWER, Forest Survey of India, ArcGIS Online, etc. The study of NDVI shows a significant relationship with the LST. Similarly, NMDI shows an abnormal change in the drought index from 0.092 in 1990 to 0.276 in 2022, and NDWI shows a continuous reduction of the water index of −0.382 in 1990 and −0.176 in 2010, followed by −0.212 in 2022. NDBI values show that the urban build‐up index is increased to an extent due to rapid urban settlements removing natural vegetation. The overall forest fire count found an abnormal rise in the Baripada region during 2011 and 2022 to 143 times. Focusing on all the above factors, an experimental study is finally carried out to confirm the rise in temperature using time‐series analysis and a multi‐layer locally tuned hidden layer perceptron (MLTHP) model to ensure the Times of India's report.

‘Dead birds flying': can north American rehabilitated raptors released into the wild mitigate anthropogenic mortality?

As the human footprint expands to meet societal energy needs, as do the impacts to wildlife. Raptors in particular are highly susceptible to anthropogenic caused mortality. Industry sectors are encouraged to offset these causes of mortality. Several options to mitigate these losses have been proposed, including raptor rehabilitation. However, its role as a conservation tool is untested. Currently no peer‐reviewed demographic analyses exist using post‐release data from rehabilitated raptors to evaluate its effectiveness at continental scales. Our objectives were to estimate annual survival of rehabilitated and wild raptors, and then use those estimates in demographic models to assess potential effects at individual and population levels. We hypothesized that rehabilitated raptors would survive similarly to their wild counterparts after an acclimation period, and that longer‐lived species (K‐selected) would benefit most from these releases. We used US Geological Survey Bird Banding Lab band‐recovery data (1974–2018) from 20 raptor species for modeling survival of rehabilitated individuals (n = 125 740) in comparison to wild birds (n = 1 913 352). Results from 17 species with adequate recovery data indicated that five species rehabilitated ≠ wild survival, two species had uncertain estimates, and 10 species rehabilitated ≈ wild survival by years two and three post‐release. We acquired admission (n = 69 707) and release (n = 25 740) data from 24 rehabilitation centers across the US (2012–2021). We integrated survival, fecundity and numbers of releases into demographic models. These models quantified the extent to which rehabilitated raptors may contribute to broader conservation efforts, especially in the context of individual take. All but two species, had measurable numbers of individuals added to the population regardless of the number of releases. The general pattern was for K‐selected species to yield larger benefits from rehabilitated supplementation to the population. These results provide evidence that rehabilitation may serve as mitigation tool to offset incidental take.

An On-Site InSAR Terrain Imaging Method with Unmanned Aerial Vehicles.

An on-site InSAR imaging method carried out with unmanned aerial vehicles (UAVs) is proposed to monitor terrain changes with high spatial resolution, short revisit time, and high flexibility. To survey and explore a specific area of interest in real time, a combination of a least-square phase unwrapping technique and a mean filter for removing speckles is effective in reconstructing the terrain profile. The proposed method is validated by simulations on three scenarios scaled down from the high-resolution digital elevation models of the US geological survey (USGS) 3D elevation program (3DEP) datasets. The efficacy of the proposed method and the efficiency in CPU time are validated by comparing with several state-of-the-art techniques.

Evaluating Source, Scale Risk, and Corrosion Risk of the Produced Water Samples from the Appalachian Basin Based on a Geochemical Database

Summary Statistically, oil and gas production can generate up to 20 times the oil equivalent of produced water. The composition of produced water samples reflects its source, its interactions with reservoir rocks, and downhole (DH) facilities, which are critical for basin evolution, water source determination, and the monitoring, management, and optimization of oil and gas production. For example, scale and corrosion, two of the most severe flow assurance issues accompanied by produced water, can lead to billions of dollars lost every year. However, few studies have developed a standard protocol to extract such valuable information from produced water compositions due to a lack of data and professional models. Using produced water geochemical data from the Appalachian Basin, one of the largest natural gas producers in the US, from the United States Geological Survey (USGS) Produced Waters Geochemical Database (PWGD), we developed a standard protocol to investigate the produced water source, evolution history, and scale and corrosion risks under both DH and surface conditions by means of incorporating the professional models for water-rock interaction and corrosion. The results show that the produced water from the Appalachian Basin possibly evolves from seawater evaporation following a typical evolution pattern of ion concentration and water isotopes, while a group of time-elapsed samples indicates that such an evolution pattern can also be due to the mixture of the injected water and reservoir water. In addition, most produced water samples show obvious risks of mineral scaling (e.g., calcite, barite, and siderite) and CO2 corrosion with corresponding mitigation strategies recommended. This study not only developed a reliable data processing and analysis protocol but also showed the valuable information a systematic analysis of produced water samples can provide for actual oil and gas production.