Climate change is reshaping the agro-ecological suitability of crop-growing regions, with significant implications for non-staple, high-value commodities often cultivated in climatically sensitive and economically vulnerable areas where data coverage is limited. Here we present SPADE (Suitability Projections using Agro-ecological Dynamics and ECOCROP), a model designed to project changes in agro-ecological suitability over time, producing suitability maps and time series of change in suitable area across decades and Shared Socioeconomic Pathways. Developed as a lightweight Python package built on top of the SpatiaFi platform and the Google Earth Engine API, SPADE combines FAO ECOCROP parameters across a wide range of crops with global, high-resolution datasets on land use-land cover, soil pH, elevation, projected changes in Köppen-Geiger climate classifications, and projected urban expansion. This model can be applied to specialty crops such as vanilla, saffron, and vetiver to assess suitability dynamics across growing regions through 2080 for SSP2 and SSP5. Model outputs quantify the combined effects of biophysical constraints and socio-environmental pressures on the spatial redistribution of crop suitability, with implications for crop migration, market dynamics, and land-use transitions. The framework offers a scalable and reproducible approach for assessing climate-driven shifts in agro-ecological potential, particularly for underrepresented crops, supporting targeted adaptation strategies.

Soil erosion poses a significant threat to agricultural productivity and water resource sustainability, particularly in monsoon-dominated semi-arid basins. This study estimates soil erosion in a sub-basin of the Upper Krishna River (UKR) using the Revised Universal Soil Loss Equation (RUSLE) within the Soil and Water Assessment Tool (SWAT) model. The Indian Meteorological Department (IMD) historical climate data from 1991 to 2021 was used for the calibration and validation of the SWAT model, and the annual average soil loss was found to be 105 tons/ha. Future projections were evaluated using climate data from two General Circulation Models (GCMs), CanESM5 and EC-Earth3, under SSP2 and SSP5 scenarios, to assess changes in annual average soil erosion until mid-century (2049). The results of the study indicate an increased average annual precipitation, which affects the soil erosivity and results in increased soil erosion. Under SSP2-4.5, projected soil erosion is 127.45 tons/ha for CanESM5 and 93.22 tons/ha for EC-Earth3, whereas under SSP5-8.5, the values rise to 208.69 tons/ha and 122.58 tons/ha, respectively. The results of the study highlight the impact of climate change on the soil erosion dynamics and identify the erosion hotspots in the study area to help policymakers and planners for sustainable land management practices.

Reflectance spectroscopy has become a promising, non-destructive technique for capturing plant functional traits. Existing leaf spectral trait prediction models are trained on peak-growing season data, however, it is uncertain whether such models can fully capture trait temporal dynamics over the phenological cycle in seasonal environments. We monitored changes in traits and leaf spectra through the season and generated 7,515 fresh leaf spectra for 7 temperate species. We used partial least squares regression (PLSR) to develop three categories of trait models for leaf phenology based on trimmed spectra (400-2400 nm). We grouped the trait prediction models into complete leaf phenology and season-specific models, and assessed their performance on measured leaf traits. Our assessment of the predicted traits show that models trained with complete leaf phenology data have high accuracy for leaf mass per area (LMA) and equivalent water thickness (EWT) with R2 = 0.85 - 0.93 and %RMSE = 4.48 - 4.77, intermediate accuracy for nitrogen (N) with R2 = 0.64 and %RMSE = 10.69 - 10.71, and low accuracy for carbon (C) with R2 = 0.08 - 0.26 and %RMSE = 17.83 - 19.92. Season-specific models had low accuracy for LMA and EWT, with R² = 0.03-0.25 and %RMSE = 16.79-464.4, while C and N had poor accuracy, with R² = 0.00-0.14 and %RMSE = 22.59-89.88. Temporal variation in leaf phenology at intra- and interspecific levels was observed in traits and spectra. We show that not accounting for temporal variation in leaf phenology can bias our understanding of plant function. This study provides a path forward to mitigate potential biases in predicting functional traits using reflectance spectroscopy and machine learning.

Determining statistical significance of changes in soil organic carbon (SOC) stocks at specified probabilities of exceedance is challenging when using landscape-scale datasets due to high natural variability, even over short distances. Using 100 resampled sites from the Australian National Soil Carbon Monitoring Network, we assessed SOC stock changes via design-based statistical inference (probabilistic) methods. Each site represents a 25 x 25 m area within a paddock (with the same soil type and land management), where 10 soil cores were randomly selected using a 5 x 5 m sub-grid. Samples were collected at 0–10, 10–20, and 20–30 cm depths and summed to estimate SOC stocks for 0–30 cm. Maintaining protocol and laboratory consistency with baseline data from ~10 years ago allowed for robust comparisons. Soil organic carbon stocks were adjusted to equivalent soil mass before analysis. The mean change in SOC between surveys was calculated, along with the sampling variance within each 25 x 25 m support area. The variance of SOC change, Var(ΔSOC), is given by Var₁ + Var₂ – 2Cov₁₂, where Var₁ and Var₂ represent the sampling variances of SOC stock for the first and second sampling times, respectively, and Cov₁₂ is the covariance term. As sampling events were independent, Cov₁₂ was assumed to be zero. To ensure conservative stock estimation, SOC stock changes were evaluated at exceedance probabilities of 50%, 60%, 70%, 80%, and 90%, corresponding to quantiles of the student’s t-distribution (α = 0.50, 0.40, 0.30, 0.20, and 0.10). The quantile function (inverse cumulative distribution) was used to scale the mean change, incorporating uncertainty into estimates. We present initial results and key lessons from applying this method across diverse Australian agricultural landscapes.

Dispersion imaging is a crucial step in multichannel analysis of surface wave surveys for estimating shear-wave velocity profiles. Traditional wavefield transformation methods such as the f-k, τ-p, and phase-shift techniques assume planar wavefronts and often require preprocessing steps to suppress noise and compensate for geometric attenuation. One common step is trace normalization or amplitude balancing, applied in either the time or frequency domain to ensure equal weighting across sensors. In contrast, cylindrical beamforming inherently accounts for geometric spreading by applying square root of distance-based shading weights to the frequency-domain wavefield, making additional normalization unnecessary. Field data comparisons show that using phase-shift or f-k methods with trace normalization versus using cylindrical beamforming without normalization can lead to a significant shift in the relative energy distribution between the fundamental and higher modes. This variation is not intrinsic to the transformation technique itself but results directly from the differing preprocessing strategies applied. Such effects are particularly evident in heterogeneous subsurface conditions where multiple modes propagate with varying amplitudes. These observations underscore the need for careful consideration of preprocessing choices, as they directly influence mode sensitivity and ultimately impact the reliability of surface wave dispersion analysis.

The variability of the Somalian Jet (SJ) over the western Indian Ocean (WIO) plays a crucial role in determining Indian summer monsoon rainfall (ISMR). This jet drives the Somalian Current along the Somali coast in the western Arabian Sea (WAS) and is associated with several mesoscale eddies, including the prominent Great Whirl. These eddies show a distinct sea surface temperature (SST) patters where cold SSTs are surrounded by relatively warm waters, with temperature differences exceeding 3°C and corresponding sensible heat flux variations over 10 W/m² within spatial scales of just 200 km. The air-sea interaction processes in WAS having these sharp mesoscale gradients are studied using the ICOADS ship observations. The focus is on understanding how the variability in these interactions influences the SJ, which in turn effects the ISMR.  Clear distinctions are observed in meridional profiles along 57°E for variables such as SST, 10-meter meridional wind speed, 10-meter air temperature, sea-air temperature difference, and surface layer stability when comparing surplus and deficient monsoon year composites. These differences highlight the interannual variability of air-sea interactions in WAS.To understand the intraseasonal variability, a time-series analysis was conducted for a normal monsoon year. Previous studies emphasized the role of potential vorticity (PV) of SJ in the Arabian Sea in driving the intra-seasonal variability of ISMR. In this study, pronounced positive peaks of PV tendency in 5-day averaged time series of meridional temperature gradients and zonal wind stress were observed across the latitudes of Somalian eddies, particularly at the beginning of active monsoon cycle over the Monsoon Core Zone. These variables also contribute to positive PV anomalies that influence the strength and structure of the SJ. Such features were not present at other latitudes with weak mesoscale variability in SST and other related fields.This study thus emphasizes the significance of understanding air-sea interactions in the western Arabian Sea, for improved understanding of ISMR at interannual and intraseasonal timescales.

This poster has been presented at AGU fall meeting 2025  Abstract ID: 1918944 Session: GP33B: Frontiers in Electromagnetic (EM) Geophysics II Poster Date & Time: Wednesday, 17 December 2025, 14:15 – 17:45 CST1In-person Location: New Orleans Convention Center, Halls E-G (Poster Hall)Deep learning methods show promise in geophysical inversion. We investigate the magnetotelluric (MT) inverse problem using a deep unsupervised inversion algorithm guided by physics, beginning with 1D MT inversion. Physics-guided neural network (PGNN) incorporates physical information, e.g., Partial differential equations, in the loss function and thus reduces the data dependence and achieves physical consistency. Goyes-Peñafiel (2025) introduced a Multilayer Perceptron (MLP) model, featuring an auto-differentiable solver, which they applied to predict media resistivity ranging from 1 to 103 Ωm. However, real-world geology (e.g., a granite bedrock containing highly conductive thin graphite layers) exhibits a far wider range of resistivity, from milli (e.g., 10-3 Ωm for graphite) to mega (e.g., 106 Ωm for dry granite). To handle strong resistivity contrasts in field data, we first explore various activation functions and loss functions; then we propose a multi-head architecture to predict the conductance, thickness and depth of the thin conductive layer, with known bedrock resistivity; next, we investigate the performance of various regularization methods (smoothness, total variation, minimum support, minimum gradient support). We analyze MCMC dropout and deep ensemble for uncertainty quantification. We then apply the algorithm to two field datasets where highly conductive graphite layers are present. We also demonstrate how to use a non-differentiable forward solver with externally computed gradients, allowing us to leverage legacy modeling codes without extensive modification. Additionally, we propose a Convolutional Neural Network (CNN) model and compare its performance against the MLP model.   In conclusion, PGNNs facilitate straightforward inversion of various parameter types when automatic differentiation is employed. However, the effectiveness of neural networks is inherently limited by the fidelity of the underlying physical model represented in the data. Additionally, hyperparameter selection must be tailored to the specific problem, and the quality of uncertainty quantification largely hinges on both the model architecture and the informational content of the input data.

Coral reefs provide vital ecosystem services, including natural coastal protection through wave attenuation. While this function is well recognized, comprehensive global assessments using satellite-based observations remain limited. This study leverages three decades of along-track satellite altimetry data from the European Space Agency’s Sea State Climate Change Initiative (CCI) to evaluate the influence of coral reefs on significant wave height (SWH) across diverse coastal regions.

For over 30 years, satellite altimetry has been a vital tool for monitoring ocean and inland water surfaces. Since 1992, at least two missions have operated concurrently, beginning with TOPEX/Poseidon and ERS-1, and now including around 10 active satellites such as ICESat-2, Sentinel-6A, and SWOT. While most mission data are freely available, they differ in format, processing level, and reference systems (e.g., ellipsoid or time), complicating multi-mission applications. Additionally, generating high-quality, ready-to-use scientific products often requires specialized expertise.   To address these challenges, DGFI-TUM has developed the Open Altimeter Database (OpenADB), which enables consistent data management and integration. OpenADB comprises the internal Multi-Version Altimetry (MVA) repository and a user-friendly web portal. It offers access to along-track products such as sea surface heights and ocean tides, along with detailed information on satellite missions, configurations, and data characteristics. All products are freely accessible via https://openadb.dgfi.tum.de after registration.   A key focus of OpenADB is the coastal zone. Over two decades of data have been reprocessed using algorithms and geophysical corrections tailored to enhance sea level retrievals near coasts without compromising open-ocean standards. The coastal dataset, based on ALES and ALES+SAR retrackers, is the largest of its kind. It also underpins the EOT20 tidal model, which delivers superior performance in shelf and coastal regions, which are typically challenging for tidal estimation. The EOT20 model is also available as an independent product. Furthermore, all missions are cross-calibrated, allowing users to seamlessly combine data from different satellites for regional-scale sea level analysis.

The Pacific-Japan (PJ) pattern, a major mode of East Asian summer monsoon variability, features a tri-polar or dipolar structure, centered over southern Japan with opposing anomalies in the western North Pacific (WNP) and Sea of Okhotsk. Since it exhibits both interannual and decadal variability, capturing its phase transitions—especially the decadal shift from tripole to dipole—is crucial for understanding East Asian summer climate. This study evaluated the performance of three large ensemble models—CESM2-LE, MIROC6, and CanESM5—in reproducing the PJ pattern’s spatiotemporal characteristics, using 20CRv3 reanalysis data as a reference, with particular attention to their ability to capture its decadal variability. To robustly assess this, a multi-model and multi-ensemble framework was employed, allowing for comparisons across different models and ensemble members. Initially, performance of the climatology of three key East Asian summer monsoon (EASM) components was assessed. It was found that precipitation was substantially underestimated over the WNP, a key convective region driving meridional teleconnections essential for the PJ pattern development. This bias motivated the use of both precipitation and the zonal-wind–Coriolis parameter product (U*f) to more robustly evaluate the PJ pattern. Based on these variables, the PJ pattern was defined as the leading mode from empirical orthogonal function (EOF) analysis. The analysis showed that climate models tend to simulate excessively strong variance in the first mode of EOF, implying potentially weaker representation of lower modes of EASM and its decadal variability. This feature was further confirmed by power spectrum analysis of PC1, which revealed that most ensemble members were dominated by interannual variability. These results highlight the models’ capacity to simulate the PJ pattern yet limitations in simulating its complex feature and further decadal variability.

In the midst of frequent and multiple global extreme hydrological events (e.g., drought), numerous studies have focused on the effects of drought on soil microbial community composition, while few paying attention to the effects of post-drought rehydration on soil microbial communities and composition. This study used field-controlled experiments to compare the response changes of soil bacterial and fungal communities to rehydration processes after drought events of different intensities, analyzing the complexity of soil microbial community structure and their interspecies dispersion. Soil bacteria showed minimal changes in OTU number, phylum-level composition, α-diversity indices (Chao1 and Shannon), and community structure, soil bacteria exhibited smaller changes during post-drought rehydration. In contrast to soil bacterial communities, the post-drought rehydration process had a more significant impact on the composition and structure of soil fungal communities. Furthermore, the post-drought rehydration process after a drought had no dominant phyla of soil bacteria and fungi. Proteobacteria, Actinobacteriota, and Calditrichota remained the dominant phyla of soil bacteria. The three most prevalent phyla of soil bacteria were still Ascomycota, Mortierellomycota, and Basidiomycota. According to correlation network diagrams, post-drought rehydration had a more substantial and detrimental effect on the stability of both soil bacterial and fungal networks; however, soil fungal networks demonstrated greater stability than soil bacteria. In addition to offering significant scientific value for identifying drought-resistant microorganisms, this study enhances research on the mechanisms by which post-drought rehydration impacts soil microbial communities. It also reveals the ecological characteristics of soil microbial communities, and their recovery strategies under abiotic stress.

Lumped basin representations are a cornerstone of hydrologic modeling, supporting applications ranging from regional prediction in ungauged basins (PUBs) to conceptual and semi-distributed frameworks. They also play a critical role in regional parameter calibration and in training “universal” deep-learning models for river-flow forecasting. Traditionally, lumped descriptors summarize the average characteristics of a watershed — such as its shape, mean slope, mean elevation, or the proportion of hydrologically relevant land-cover types. However, these approaches overlook both spatial heterogeneity and the spatial organization of distributed basin characteristics, which can significantly influence hydrologic response. In fact, distinct spatial configurations can yield similar average values yet produce markedly different surface runoff behaviors. We introduce a novel methodological framework for deriving a new class of lumped metrics based on hydrologic connectivity, that are able to capture differences in the spatial arrangement of surface characteristics. These metrics facilitate more meaningful comparisons across basins and offer a hydrologically informed approach to integrating heterogeneous spatial data into complex deep-learning models, enhancing both reliability and interpretability. We generate a CONUS-wide dataset of connectivity-based descriptors and demonstrate its utility by training an ensemble of long-short term memory (LSTM) models for hydrologic forecasting, benchmarking their performance against the same model trained with traditional basin descriptors. We further examine model sensitivity to individual input variables to assess how the LSTM ensemble leverages the information contained in distinct connectivity-based basin metrics.

Dams are crucial structures for efficient water resource management. Previous studies have presented the interlinkages between seasonal dam deformation and hydrometeorological factors. However, the spatiotemporal changes in this relationship have not been widely quantified. We combined InSAR-based dam deformation, rainfall (RF), reservoir water levels (RWL) time series, and quantified Colwell’s seasonality across six dams in California. We also applied continuous wavelet (CWT) and cross-wavelet (XWT) transform to examine seasonal periods and coupling mechanisms between dam deformation and hydrometeorological variables. We find that the studied dams undergo seasonal deformations that are closely linked to RF and RWL. The Colwell indices (range 0-1), Predictability (P: 0.16-0.42), and Contingency (M: 0.15-0.42) both fall within the significance threshold (p<0.05), indicating strong predictability and seasonal dam deformation patterns. In contrast, the index Constancy (C: 0-0.08) shows no statistical significance across any of the dams, confirming these structures do not exhibit consistent deformation patterns over time but rather respond dynamically to seasonal variations. The Pardee (PAR) Gravity Dam (38.25N, -120.85W) has the highest index values for P and M, as it also indicates consistent annual deformation patterns over time. The Melones (NML) Embankment Dam (37.95N, -120.52W) has the lowest index values for P and M, as its yearly pattern varied during 2016-23. The seasonal period of deformation is around 1 year in CWT spectra for all the dams except the NML dam. It has CWT significant spectra dominated within a 6-month to 1-year range. XWT indicates that RF and RWL exhibit different interlinking with dam deformation for each dam. The dam deformation exhibits a lag response time of up to ¼ of a year, characterized by a 90° phase relation with the RWL as it rises and falls. RF has an anti-phase relation and no direct influence on the dam deformation. There is a strong possibility of having an influence of other factors like atmospheric loading and geological influence. This highlights the need for further analysis to examine the influence of these factors on seasonal dam deformation. Our findings enable improved dam safety monitoring protocols and support climate-resilient infrastructure management.

Understanding sea ice evolution in Antarctica is critical for deciphering climate dynamics in polar regions and icy planets over multiple spatio-temporal scales. Satellite-derived ice condition models, primarily ice concentration, are frequently assimilated into coupled sea ice-ocean modelling systems. Accurate ice concentration measurements are crucial to the accuracy of climate models, as the percentage of ice coverage and form of ice greatly affect ocean surface albedo and heat exchange. However, the gap between the ice condition model and dynamics of the sea ice landscape in-situ is known to be significant. We present our in-situ ice observation data gathered during the NBP2501 expedition that provides high temporal and spatial resolution categorization of ice thickness, concentration, form and stage of development through observational analysis of the transition from open ocean to close pack ice in the Ross Sea, Antarctica from Feb. 18 to Apr. 6 2025. We document relative ice concentration based on analyzing the complexity in the variability of ice thicknesses, forms, and stages of development. These ice categorizations are used to train a machine learning algorithm to assess the ice condition, and compared to existing satellite imagery, and microwave satellite-based ice concentration. This analysis is used to establish an unprejudicial ice condition index that considers relative thickness, form, concentration and stage of development. In collaboration with ice forecasting company Drift+Noise, these ice condition index data could augment the ARTIST Sea Ice Concentration algorithm developed by Kaleschke et al (2001) and later refined in Spreen et al (2008), with the goal of providing a more comprehensive ice concentration that includes ice form, thickness, and stage of development. We also demonstrate the application of this data to inform operational decision making for scientific expeditions. Each operation conducted on the NBP2501 cruise is analyzed in the context of this index with a description of the operational challenges and adaptive strategies. Our high-resolution categorization of ice conditions aims to lessen the temporal and spatial gap between satellite-derived models and ground truth reality and provide a more robust framework for scientific operational decision making.

To support decision-making processes aimed at mitigating flood impacts and groundwater depletion, this study aims to quantify the potential recharge effects of riparian restoration of floodplain functions. We hypothesize that since 1985, groundwater recharge in the Northern Rio Grande Basin has declined due to reductions in flood-irrigated agriculture, and that current recharge suitability in semi-arid basins is significantly associated with soil type/hydraulic conductivity, land use/land cover, precipitation, slope, and hydro-geomorphic opportunity. Using similar basins with known recharge, these parameters will be used to predict recharge potential, and in the Santa Cruz Basin, the top predicted suitability zones will align with geomorphic parameters known to support recharge (e.g., floodplains and irrigated lands), more often than random distribution would predict. Recharge estimation is approached in two stages. First, reductions in recharge since 1985 are estimated from observed declines in flood-irrigated agriculture. To reconstruct historical irrigation patterns, we developed a training dataset through interviews with local farmers, identifying irrigated and non-irrigated parcels across diverse land uses. Annual NDVI values (2005–2024) were extracted from Landsat 5, 7, 8, and 9 imagery using Google Earth Engine (GEE), and sensor-specific NDVI thresholds were derived to improve classification consistency. These thresholds were then applied to classify irrigated land annually from 1985 to 2024. The preliminary results revealed a peak in irrigated area during the 1990s, followed by a decline in the 2000s, and partial recovery in the 2010s. Second, potential recharge from restoration is quantified by applying recharge rates reported in donor basins to Santa Cruz subareas identified through spatial suitability analysis. ArcGIS-based Weighted Overlay Analysis and Spatially Weighted Regression are used to link soil, land cover, precipitation, slope, and hydro-geomorphic opportunity with recharge suitability. Mann–Kendall tests and hydrological datasets are employed to validate temporal trends. This approach yields a spatial framework that (i) quantifies recharge decline linked to reduced irrigation since 1985, and (ii) identifies priority areas where restoration of floodplain connectivity and managed flooding could enhance recharge. The findings provide a transferable method to assess recharge decline and restoration potential in semi-arid basins facing water scarcity and land-use change.

Inland wetland vegetation plays a critical role in regulating hydrological and ecological processes, supporting natural flood control, improving water quality, and sustaining ecosystem functions essential to land-use resilience and biodiversity. Yet, accurate classification of their vegetation communities remains challenging due to high spectral similarity, vegetation structure, and variable sensor characteristics. This study presents the development of a foundational deep learning model designed to classify inland wetland vegetation communities in Central Florida, USA, using high-resolution WorldView-2 (WV-2) imagery. We trained a two-stage convolutional neural network (CNN) on WV-2 imagery collected over wetland management areas of the Upper St. Johns River Basin (USJRB) in Central Florida, overseen by the St. Johns River Water Management District, using labeled vegetation patches derived from field-verified expert interpretation. The first stage model distinguishes among broad structural vegetation types, which are then further classified into finer community-level categories by a set of specialized submodels. The model leverages learned spatial features and targeted spectral indices to address persistent classification challenges, including spectral similarity and shadow-induced confusion. 

General circulation models (GCMs) exhibit systemic precipitation biases over much of South America during its wet season (December to March), undermining their utility in projecting future mean and extreme precipitation shifts and Amazon carbon cycle feedbacks in response to rising greenhouse gases. Though these biases, including a dry bias over the Amazon, have persisted across generations of model development, their origins are an ongoing topic of debate. Here, we explore whether the underestimation of Andes peak heights—and thus their orographic blocking effect—due to overly-smooth resolved topographic boundary conditions in GCMs underlies the biases. We performed a suite of idealized experiments forced by elevated topography over the Andes (and Central America) to better capture orographic blocking with the NASA GISS ModelE version 2.1 (E2.1) and 3 (E3). In the E2.1 experiments conducted with a fully-coupled atmosphere-ocean, we find precipitation over the central Amazon and South Atlantic Convergence Zone (SACZ) is increased relative to a control simulation forced with the default topography, reflecting a reduction of the model’s precipitation biases in these regions. The former results from enhanced blocking of low-level tropical easterly winds and thus moisture convergence east of the mountains, while the latter is driven by shifting stationary wave patterns that enhance moisture convergence in the SACZ. Yet, the wet bias in the eastern Amazon is worsened in the elevated topography experiment due to an intensification of the ITCZ bias in the southern tropical Atlantic. Analysis of analogous experiments run with E2.1 and E3 in atmosphere-only mode and prescribed sea surface temperatures reveals that the high pressure stationary wave pattern generated in the south Atlantic relied on coupled atmosphere-ocean mechanisms. Coupled processes are shown to be important to generating stationary wave patterns arising from orographic blocking and sea surface temperature interactions, enhancing moisture convergence and thus reducing model biases over the central Amazon and SACZ, though this is balanced by increased model biases influenced by the ITCZ.

Numerous cargo and tanker vessels every year transit the Bering Sea, and with decreasing sea ice in the Arctic, increasing levels of travel through the Bering Strait to new shipping routes is a contemporary reality. Increased shipping in the region not only intensifies the impact to remote communities but also causes concern for the safety of vessel operators and the environment. Analyzing existing policy within the Bering Sea is useful to determine policy effectiveness as maritime activity is amplified in the region. To examine changes in traffic patterns from 2015 to 2022, and therefore the effectiveness of existing policy, hot spot analysis (Getis-Ord Gi*) was conducted on the Bering Strait ‘Areas to be Avoided’ (ATBAs), implemented in 2018 as a part of the International Maritime Organization’s Polar Code. Additionally, hot spot analysis (Getis-Ord Gi*) was conducted on vessels transiting the region every year (8 of 8 years), frequently (4–7 years), or rarely (1–3 years) to investigate aspects of maritime activity that can impact adherence to the ATBA policy. The analysis conducted on vessel traffic in the Bering Strait from 2015 through 2022 shows that the ATBA policy influenced all vessel types. Determining the effectiveness of current policy in maritime activity in the Bering Strait will be necessary for future policymaking to protect this unique region of Alaska, as well as for the communities who have inhabited it before recorded history.