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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

At least 559 records · Page 31Linked to original sources
USGS2026

Resurvey of cross sections on the Green River in Browns Park, Colorado and Utah

This study resurveyed ten previously established cross sections and established eight new cross sections on the Green River in Browns Park to document changes in channel width, depth, and area since earlier surveys conducted in 1994. The measured area of the channel cross sections on the Green River in Browns Park generally increased between the initial surveys and 2019. This increase in cross-sectional area was observed in nine of the ten resurveyed cross sections and is indicative of net sediment erosion. The increase in cross sectional area occurred through both channel widening (bank retreat) and increases in depth (bed incision). An analysis of the contribution of bank versus bed changes to the overall area change suggests that the erosion is mostly from the bed of the channel. In addition, weak longitudinal trends in the bed-sand grain-size distribution are consistent with progressive depletion of the sand stored on the bed of the Green River in Browns Park. The findings from our cross-section resurvey support the conclusion that the Green River in Browns Park is experiencing progressive sediment loss and is in a state of sediment deficit.

Colorado, Utah↗
USGS2026

Improving building footprint extraction using NAIP and 3DEP lidar derived features with deep learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Texas↗
USGS2026

Development of liquefaction-targeted design basis in U.S. seismic provisions

Seismic design criteria for new buildings in the United States have historically focused on life safety performance objectives through minimizing the potential for structural collapse. Development of design criteria to meet this performance objective has evolved over time, leading to the current, risk-targeted maximum considered earthquake (MCE R ) design basis. Corresponding MCE R seismic design loads incorporate the full range of probabilistic ground motion hazard information available from U.S. Geological Survey National Seismic Hazard Models, as well as a representation of the uncertainty in structural collapse capacity. Despite these considerable advances in structural design criteria, guidelines for geotechnical hazards such as liquefaction-induced damage, which continue to be based on uniform ground-shaking hazard (i.e., MCE ground motions), are not well-connected with risk-based, collapse prevention performance objectives. This study, undertaken as part of Building Seismic Safety Council efforts to update recommended seismic provisions through the National Earthquake Hazards Reduction Program, presents probabilistic, liquefaction-targeted design objectives and corresponding ground motion parameters, with a focus on improving consistency in first-level screening criteria for liquefaction hazard assessment. This study identifies a potential maximum acceptable annualized probability of liquefaction triggering ( P L,max ) of 2.5% in 50 years, above which liquefaction consequences and potential mitigation measures would be considered. The P L,max -based design basis is calibrated to effective probabilistic liquefaction hazard levels obtained in practice using current MCE peak ground accelerations, and could be implemented via a new liquefaction-targeted maximum considered earthquake (MCE L ) peak ground acceleration ( PGA L ). The proposed PGA L is derived from probabilistic liquefaction hazard curves and includes uncertainties inherent to liquefaction hazard modeling. The potential design effects of the proposed PGA L are demonstrated via comparison with current MCE PGA levels, effects on first-level liquefaction screening analyses, and improvements in the consistency of P L,max levels across the United States, and for a wide range of site conditions.

conterminous United States↗
USGS2026

Life-history stages and behavior influence demographic classification of moose captured on remote cameras

Obtaining accurate information on demographic states, such as the age and sex classes of animals, is an important step for monitoring wildlife populations. Traditionally, demographic data are collected from harvest, aerial surveys and telemetry studies. However, these methods can be expensive, limited to small spatial scales, or biased due to human behavior. Remote cameras have become a mainstay for studying and monitoring wildlife as they are relatively inexpensive, can be deployed over large spatial scales, and effort can be accounted for during surveys. For some species, a variety of demographic information, such as sex and age classes, can be obtained from pictures. Moose Alces alces are a photogenic species found across boreal and semi-boreal forests of the Northern Hemisphere. Previous studies have used demographic data from remote cameras to estimate demographic parameters and population dynamics. A primary assumption is that these age and sex classes are accurately classified. However, numerous factors can influence the ability of observers to identify age and sex classes of moose captured on cameras. We used data from 84 cameras from a 3-year period (2021–2024) in northern Maine, USA, to evaluate how temporal, environmental, site-level, and endogenous factors influence observers' ability to classify age and sex classes of moose. Using Bayesian categorical regression models, we found that temporal variability, position and proximity of moose from cameras, and the behavior of moose influenced our ability to identify age and sex classes. This information can be used to decide which periods to use data for population modeling and how to design studies to reduce the amount of uncertainty associated with different age and sex classes. We anticipate that our approach could also be used for other species whose age and sex classes can be differentiated using remote cameras.

Maine↗
USGS2026

Modeling legacy nitrogen transport under instantaneous, steady-state, and transient groundwater flow conditions

In hydrologic settings where groundwater discharge contributes substantially to surface waters, legacy nitrogen in groundwater can confound surface water nitrogen loads estimated exclusively from current terrestrial sources. Additionally, legacy nitrogen in groundwater can contribute to lagged responses to nitrogen management efforts. Some methods of estimating groundwater contributions to surface water nitrogen loads account for legacy nitrogen, while others do not. The resulting differences are rarely quantified. We used a numerical modeling framework to compare three methods of estimating time-varying annual groundwater nitrogen loads to surface water receptors on eastern Long Island, New York. The instantaneous load method used steady-state contributing areas and includes no temporal groundwater lag. The second method used numerical simulations of nitrogen loads under steady-state flow, which captures groundwater transport lags but omits the annual variability in transient hydrologic stresses. The third method numerically simulated both transient groundwater flow and nitrogen transport to explicitly capture the effects of legacy nitrogen in groundwater. Depending on antecedent nitrogen and hydrologic conditions, historical nitrogen loads estimated from the numerical simulations were sometimes similar (<10% difference) and other times substantially different (±100%) from the instantaneous load estimates. Additionally, simulated future surface water nitrogen loads responded asymptotically over several decades following reductions in terrestrial nitrogen sources, further highlighting the effect of groundwater transport lag times. The comparison of the three methods, quantification of historical interannual variability, and prediction of lagged responses to nitrogen source reductions provide important context for decision makers using estimated groundwater nitrogen loads to help evaluate nitrogen management efficacy.

New York↗
USGS2026

Structure-aware unsupervised transmission corridor structure extraction from 3DEP lidar using graph attention embeddings and reinforcement-learned DBSCAN

Transmission corridors are critical components of power delivery infrastructure, and their large-area monitoring increasingly relies on national-scale airborne lidar programs. This study presents an unsupervised, scalable framework for extracting transmission corridor structures (including conductors and supporting towers) from U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) lidar point clouds. The workflow begins with height-above-ground filtering and statistical outlier removal to isolate elevated infrastructure candidates. Local Principal Component Analysis (PCA) is then used to compute fine-scale geometric descriptors. To incorporate spatial context and improve structural separability in feature space, we learn point embeddings using a Graph Attention Network (GAT) trained with a structure-consistency triplet objective, yielding representations tailored for downstream density-based grouping rather than semantic classification. To address the spatial non-stationarity of point density and noise across large scenes, we formulate Density-Based Spatial Clustering of Applications with Noise (DBSCAN) parameter selection as a reinforcement learning problem and employ Proximal Policy Optimization (PPO) to adapt 𝜀 and minPts at the block level based on embedding statistics. The final extraction is produced via block-wise DBSCAN and spatial merging to support efficient processing of multi-million-point clouds. Experiments on USGS 3DEP data over Houston, Texas, show that the proposed method produces spatially coherent candidate corridor structures from unstructured lidar. Quantitative assessment is conducted using OpenStreetMap (OSM)-referenced spatial-agreement metrics under a consistent vector-reference protocol, alongside comparative baselines and ablation studies, to examine the relative contributions of graph embedding learning and RL-based adaptive clustering.

Texas↗
USGS2026

2026–2031 U.S. Geological Survey strategy for a hazard ready Nation—Actionable science for risk reduction

Executive Summary The USGS Strategy for a Hazard Ready Nation provides an approach that U.S. Geological Survey (USGS) researchers and staff can use to deliver actionable, user-focused science that supports risk-informed decision making aimed at reducing risk and losses across the Nation. The Strategy builds upon successful efforts within the USGS and seeks to promote and prioritize similar work in the future under an organized framework. Rooted in four priorities—(1) center end-user needs, (2) enhance research relevance, (3) support product and service development and implementation, and (4) improve communication—the USGS Strategy for a Hazard Ready Nation is designed to focus and guide, not prescribe, activities. Strategic actions are proposed to achieve these four priorities in a timely fashion. The USGS plans to continually build upon the Strategy for a Hazard Ready Nation to deliver actionable information for risk-informed decision making.

Circular↗
USGS2026

U.S. Geological Survey Monitoring Milestones—Oe-151 at Woodgate, NY (433112075091501)

On July 9, 1926, monitoring well Oe-151 at Woodgate, New York (USGS ID 433112075091501) recorded its first groundwater data. Since then, the well has provided water data nearly continuously and has now reached a 100-year milestone for data collection. The well is part of the U.S. Geological Survey (USGS) Climate Response Network (CRN), which is a national network of wells selected to monitor natural groundwater conditions. Well Oe-151 is the first well in the network to reach a 100-year Centennial milestone.

New York↗
USGS2026

Timescales of cumulate mobilization and mixing for the 1868 A.D. eruption of Mauna Loa, Island of Hawai‘i

The deadly 1868 A.D. eruption of Mauna Loa’s lower Southwest Rift Zone (Island of Hawai‘i) included a M7.9 earthquake and associated tsunami and landslides, demonstrating the severe hazards posed by Earth’s largest active subaerial volcano. To better understand the relationship between intense seismic activity, dike emplacement, magma storage, transport histories, and mobilization of olivine cumulates at Mauna Loa, we examine compositional zoning of olivine in the 1868 lava flows. Samples range from basalt (< 10% olivine) to picrite (30–40% olivine). The olivine cargo is heterogeneous (Fo 78.2–89.2 ; forsterite = [Mg/(Mg + Fe) × 100]) but dominated by ~ Fo 89 cores that lie above the Fe-Mg equilibrium field of host glasses. Crystal rims < Fo 80 are due to post-eruptive modification in slow cooling lava flows. Minor element compositions fall within the range of other Mauna Loa olivine erupted in the past 200 years. Olivine crystals exhibit both normal and complex Fo zoning patterns that yield timescales of diffusive re-equilibration that range from 3 to 258 days, with 72% of crystals recording 71 days or less. These timescales correspond to magmatic priming of the summit reservoir system ~ 2 months prior to the eruption and the M7.9 earthquake likely facilitated the transport of the crystal-rich summit-derived magmas downrift shortly prior to eruption. If the recently proposed faster Fe-Mg diffusion coefficient is used, timescales instead range from < 1 day to 25 days, with most recording 1 week or less. In this scenario, most of the olivine zoning would have to have been generated after the M7.9 earthquake perturbed the system.

Hawaii↗
USGS2026

Seeing is believing: Livestream video monitoring of Hawaiian eruptions

Livestream video has become a crucial tool for volcano monitoring in recent years, building upon the use of webcam snapshots that have been common for the past two decades. In Hawaii, livestream video was first tested in 2018, and today, livestream video is a vital tool for the Hawaiian Volcano Observatory and partner agencies (National Park Service and Hawaiʻi County Civil Defense) as well as the public. Its role in volcano monitoring has been highlighted by the 2024–present summit eruption of Kīlauea—where three livestream video feeds provide a continuous and easily accessible view of the activity for the public. Here, we describe the methodology and equipment used for the livestream cameras in Hawaii and discuss the benefits and challenges of livestream video monitoring. We show how the nature of livestream video has fundamentally changed volcano monitoring, providing an “up-close” view to the world that was previously limited to observatory field staff. The livestream video feeds, coupled with the historical summit lava fountains, have provided an unprecedented level of engagement with the public, both local and worldwide.

Hawaii↗
USGS2026

Requirements, capabilities, and analysis for Earth observations—From user needs to Earth observation solutions

Introduction At the core of the Nation’s land imaging enterprise, the Requirements, Capabilities, and Analysis for Earth Observations (RCA-EO) project serves as the backbone for understanding user needs and shaping the future of Earth observation (EO) systems, products, and technology development. Understanding user needs and continuously tracking observing system capabilities is essential for EO because it ensures that the U.S. Government investments remain aligned with the real-world decisions, applications, and societal benefits they are intended to support. The RCA-EO project collects data users’ land imaging needs, focusing on what needs to be measured rather than specific technology. Collecting this data has resulted in a comprehensive understanding of civil land imaging user needs across scientific and operational applications. Over the past 10 years, the RCA-EO project has interviewed hundreds of subject matter experts, yielding more than a thousand user needs. The RCA-EO project’s analytical tools, models, and methods help inform the development of land imaging systems and products, determine appropriate data purchases, and prioritize partnerships for access to data from missions outside of the USGS. Our tools and databases support decisions with credible, needs-based information.

Fact Sheet↗
USGS2026

Methods for estimating selected low-flow frequency and mean annual flow statistics at gaged and ungaged locations on streams in Georgia, North Carolina, and South Carolina

The U.S. Geological Survey, in cooperation with the Georgia Department of Natural Resources (Environmental Protection Division), North Carolina Department of Environmental Quality (Division of Water Resources), North Carolina Department of Public Safety (Office of Recovery and Resiliency), and South Carolina Department of Environmental Services, updated low-flow frequency, mean annual flow, and flow-duration statistics at 843 streamgages in and near Georgia, North Carolina, and South Carolina. The low-flow frequency statistics are annual minimum 1-day average flow with a 10-year recurrence interval (1Q10), annual minimum 7-day average flow for 2- and 10-year recurrence intervals (7Q2 and 7Q10, respectively), and annual minimum 30-day average flow with 2- and 3-year recurrence intervals (30Q2 and 30Q3, respectively). Monthly 1Q10 and 7Q10, and W7Q10 flow statistics for the winter period (November–March) also are presented. By using data from 604 of the streamgages on streams with streamflows that are not substantially affected by regulation or diversion and are not tidally influenced, regional regression equations were developed to predict flow statistics with prediction intervals at ungaged locations on streams with those same criteria. The regional regression analysis included data from 132 streamgages from adjacent States Alabama, Florida, Tennessee, and Virginia. The final regional regression equations include variables such as drainage area, streamflow variability, precipitation, percentage of impervious area, and percentage of the basin in various ecoregions. The low-flow statistics for the streamgages analyzed and the regional regression equations will be integrated into the U.S. Geological Survey StreamStats application ( https://www.usgs.gov/streamstats ) for Georgia, North Carolina, and South Carolina. StreamStats generates basin characteristics needed to compute low-flow frequency statistics for ungaged locations. A trend analysis of annual minimum 7-day average flows was done for 78 streamgages with at least 30 years of continuous record. Trends were evaluated for 30-, 50‑, 70-, and 90-year periods, ending in climate year 2021, and independence and short- and long-term persistence assumptions were considered. For all trend analysis assumptions, most streamgages did not exhibit significant trends in annual minimum 7-day average flows. Trends in annual precipitation and air temperature were similarly evaluated for the period 1895–2021 to assess the variability of climate for Georgia, North Carolina, and South Carolina.

Georgia, North Carolina, South Carolina↗
USGS2026

Comparing DESIS hyperspectral and Landsat 10 simulated superspectral data for crop type classification in California's Central Valley

To advance crop type mapping in support of global food and water security, this study compared three spectral configurations: (A) the full 60-band DLR Earth Sensing Imaging Spectrometer (DESIS) hyperspectral narrowband (HNB) dataset, (B) a 14-band subset of DESIS-derived HNBs aligned with the planned Landsat 10 (formerly Landsat Next) spectral configuration (400–1000 nm), and (C) DESIS-based simulations of Landsat 10 superspectral broadbands. The analysis was conducted in California’s Central Valley, hereafter referred to as “the Central Valley”, during the peak growing month of August. DESIS imagery from August 2021, 2022, and 2023 was used sequentially for model development, testing, and independent validation. Over these three years, DESIS provided extensive hyperspectral coverage of much of the 4 million hectares in the Central Valley’s. Analyses were performed on Google Earth Engine using two pixel-based supervised classifiers, Random Forest (RF) and Support Vector Machine (SVM), to differentiate three major crop classes: row crops, grapes and tree crops, and winter wheat/fallow/other. The highest overall accuracy (86%) was achieved using SVM in combination with either the full DESIS hyperspectral dataset or the 14 DESIS narrowbands corresponding to Landsat 10. This finding aligns with earlier studies showing a small number of strategically positioned narrowbands can be optimal for crop type classification. Use of the narrowband datasets resulted in substantially higher accuracy (overall accuracy of 86%) compared to the simulated Landsat 10 broadbands (overall accuracy of 75%), supporting previous studies highlighting the utility of narrowbands. Despite the high accuracy using August imagery, the study indicates more granular crop type classification will require multi-temporal observations spanning the full phenological cycle (June–October), especially for a large number of crop classes. Acquiring task-based hyperspectral imagery over such large areas throughout the growing season remains operationally challenging. In contrast, Landsat 10 superspectral imagery could provide routine coverage across seasons and years that is practical and scalable for future large area crop type mapping and agricultural monitoring.

California↗
USGS2026

Correcting a systematic bias in an ocean drilling project site 882 alkenone sea surface temperature record

Reconstructions of sea surface temperature (SST) in the geologic record are fundamental to our understanding of Earth's climate history and the evaluation of Earth's climate sensitivity to greenhouse gas forcing. SSTs are reconstructed with a variety of methods, including alkenone biomarker lipids produced by certain coccolithophore algae. One such alkenone SST reconstruction from the subpolar northwest Pacific Ocean Drilling Program (ODP) Site 882 (50.21° N, 167.35° E, 3244 m water depth) has played a large role in shaping the paleoclimate science community's view of global climate warmth during the Late Pliocene (3.6–2.6 million years ago) and the subsequent cooling that characterized the intensification of Northern Hemisphere Glaciation (Haug, 1995; Haug et al., 2005; Martínez-Garcia et al., 2010). First, we have found that the values reported in the PANGAEA archive for this ODP Site 882 alkenone dataset were inaccurately reported as U K' 37 values when they are instead U K 37 ( https://doi.org/10.1594/PANGAEA.315092 , Haug and Sarnthein, 2005). This error in the archived data table resulted in the incorporation of inaccurate absolute SST estimates by several studies that applied U K' 37 calibrations to this ODP Site 882 dataset (e.g., Brennan et al., 2022; Clark et al., 2024, 2025; Tierney et al., 2019, 2025b). Second, using other published data from ODP Site 882 (Studer et al., 2012) and nearby Site 883 (51.11° N, 167.46° E, 2384 m water depth; Herbert et al., 2016; Novak et al., 2024), we show that the original Haug (1995) alkenone SST record at ODP Site 882 systematically reports an amplified range of absolute SST values compared to the more recently generated data. This observation is consistent with the known concentration-dependent biases of the gas chromatography chemical ionization mass spectrometry (GC-CI-MS) analytical method used by the original ODP Site 882 study (Chaler et al., 2000, 2003; Haug, 1995; Hefter, 2008; Rosell-Mele et al., 1995). These concentration-dependent analytical biases complicate applying a uniform correction to the entire Haug (1995) dataset. However, we are able to leverage the published datasets to propose a correction and quantification of uncertainty for a subset of the Haug (1995) dataset measured at similar on-column analyte abundance. For these samples, we find an average analytical uncertainty equivalent to ±2.05 °C , which is greater than and in addition to the typical ±1.4 °C 1 σ prediction uncertainty of the U K' 37 sea surface temperature proxy. We then discuss the implications of the corrected dataset for our understanding of late Neogene and Quaternary climate in the Kuroshio Extension region.

Climate of the Past↗
USGS2026

A Lake Charr pangenome reveals highly conserved Ohnologs as drivers of phenotypic diversity

Whole-genome duplication (WGD) is hypothesized to spur evolutionary diversification by producing genome-wide duplicate gene sets (Ohnologs) that are initially functionally redundant but can diverge markedly as the effects of relaxed selection accumulate over time. However, the underlying mechanisms remain unclear, in part because genomic studies often reconstruct Ohnolog evolution over millions of years, during which subsequent mutations can obscure deep-time signals. Investigating the relationship between Ohnolog evolution and diversification on a contemporary timescale offers clearer insights. We explore this relationship in Lake Charr ( Salvelinus namaycush ), where ∼10% of genes are retained highly conserved polyploid duplicates following the Salmonid-Specific Fourth Round WGD. Using 31 chromosome-level assemblies of Lake Charr from morphologically and ecologically diverse populations, joined into a pangenome graph, we characterized 189,555 structural variants (SVs) that were significantly less likely to affect genes retained as sequence-conserved Ohnolog pairs, nuancing the hypothesis that gene redundancy, relaxed selection, and functional diversification are intertwined. However, we found that SVs affecting such conserved Ohnologs may be potent drivers of adaptive evolution. Notably, we identified a putative 938-Kb interchromosomal translocation containing 25 genes with highly conserved Ohnologs in a paralogous (but untranslocated) genomic block. This putative translocation appears to have facilitated Ohnolog divergence in ankrd11 and hp , genes putatively linked to craniofacial and lipid metabolic diversity in sympatric Lake Superior morphs. This research reveals that conserved Ohnologs previously presumed to be redundant remain a reservoir for adaptive change.

BioRxiv↗
USGS2026

Quantifying avian resilience to habitat change to support conservation decision-making

Habitat loss and degradation are two of the main drivers of contemporary avian population declines. Wildlife managers are increasingly advocating for tools that provide decision support to set priorities for restoration or conservation efficiently. We demonstrate how to derive two species-specific management and resilience metrics: the greatest management impact point (GMIP) and the ecological resilience threshold (RT). The GMIP indicates the amount of environmental change in a landscape where habitat improvements are expected to have the greatest impact on species’ occurrence. The RT represents the amount of environmental change in a landscape that a species can tolerate before the steepest change in occupancy is expected to begin. We estimate species-specific metrics using the amount of uncharacteristic exotic vegetation as an index of environmental change, and demonstrate how multispecies patterns may suggest potential management strategies. We estimated occupancy models using 5 years of multispecies avian detection data from the Integrated Monitoring in Bird Conservation Regions program in the State of Utah, USA (hereafter, Utah). Based on the estimated relationship between species’ occupancy and amounts of uncharacteristic exotic vegetation, we derive RT and GMIP scores for 61 species breeding in Utah. We found wide interspecific variation in resilience to amounts of exotic vegetation, with species generally clustering at extreme values. Our results demonstrate that birds in Utah appear more resilient to amounts of uncharacteristic exotic vegetation at coarser spatial resolution, showing greater variance and lower average RTs at finer spatial resolution. Species that are not fully resilient to the range of uncharacteristic exotic vegetation observed in this study are expected to respond most strongly, on average, to management actions in landscapes with high levels of exotic vegetation; however, early detection and rapid response is likely the most effective strategy. Quantified across many species, these metrics can be used to identify and prioritize landscapes where current environmental conditions could be maintained to avoid the greatest species’ declines, or which maximize expected biodiversity returns on investment in environmental restoration. Managers can consider either focal species’ resilience for tailored conservation planning or summarize species resilience to create efficient management plans that maximize outcomes for multiple species.

Utah↗
USGS2026

Site-specific amplifications in Northwestern Turkiye: A generic approach

In this study, we derive generic site amplification functions applicable to Northwestern Türkiye and follow guidance on site classifications established by the United States National Earthquake Hazards Reduction Program (NEHRP). We employ the one-dimensional (1-D) equivalent linear ground response analysis method and focus on recordings from a select suite of free-field strong motion (SM) stations. These particular SM stations are associated with a comprehensive geotechnical site conditions dataset comprising 76 shear-wave velocity profiles. Despite their varying resolutions and depths, this diversity allows for a more representative analysis across different site conditions, enhancing the robustness of our findings. For analyses, we utilize a robust set of global ground motion records, selected from both within and outside of Türkiye, to serve as strong and weak input bedrock motions. Site amplification is determined by propagating these bedrock records through 76 site-specific Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) station profiles and calculating the spectral acceleration ratio between the ground surface and the input motion. Our analyses reveal substantial disparities between the amplification functions derived from 1-D site response analyses using strong and weak input motions. These disparities are central to our analysis and inform the subsequent comparison with site amplification functions reported in the literature. We compare the generic site amplification functions derived in this study with those reported in the literature for regions with similar geological and seismic conditions to Northwestern Türkiye. For example, site amplification functions for NEHRP Site Classes C and D have been documented for Greece and the United States utilizing frequency-dependent methods such as the quarter-wavelength approximation. Our findings reveal that the generic site amplification functions derived in this study exhibit differences in peak amplitudes and predominant frequencies compared to those in the literature, reflecting regional variations in geotechnical and seismic properties. This study represents an initial effort to formulate generic site amplification functions for Türkiye, with a particular emphasis on the Northwestern region. A thorough literature review indicated no prior studies have proposed such functions specifically for this area.

Northwestern Turkiye↗