Aymane Ahajjam

Active layer thickness (ALT) is a key indicator of permafrost response to environmental change. However, its long-term variation across regions can be driven by multiple geospatial factors whose relative contributions remain difficult to disentangle. In this work, we propose a framework combining two processes: long-term trajectory classification and causal inference. Accordingly, we introduce the Active Layer Dynamics Index (ALDI), categorizing ALT time series into six classes of direction and rate of change: Rapid and Gradual Thickening, Rapid and Gradual Thinning, Transitional and No Trend. ALDI relies on nonparametric trend detection, reversal identification, and bootstrap uncertainty quantification. We then conduct causal inference using site fixed-effects and first-difference estimators to examine how environmental drivers vary across ALDI classes. Applied to 129 CALM network sites (1990–2024), ALDI identifies a 3.5:1 thickening-to-thinning ratio across the pan-Arctic and substantial regional heterogeneity. In terms of causality, thermal forcing dominates at thickening sites but is absent in thinning and transitional regimes, where hydrological and disturbance processes prevail. These class-dependent relationships were not detectable in pooled analyses, underscoring the need for trajectory-aware analysis in permafrost research.

Active layer thickness (ALT) is a key indicator of permafrost response to environmental change. However, its long-term variation across regions can be driven by multiple geospatial factors whose relative contributions remain difficult to disentangle. In this work, we propose a framework combining two processes: long-term trajectory classification and causal inference. Accordingly, we introduce the Active Layer Dynamics Index (ALDI), categorizing ALT time series into six classes of direction and rate of change: Rapid and Gradual Thickening, Rapid and Gradual Thinning, Transitional and No Trend. ALDI relies on nonparametric trend detection, reversal identification, and bootstrap uncertainty quantification. We then conduct causal inference using site fixed-effects and first-difference estimators to examine how environmental drivers vary across ALDI classes. Applied to 129 CALM network sites (1990–2024), ALDI identifies a 3.5:1 thickening-to-thinning ratio across the pan-Arctic and substantial regional heterogeneity. In terms of causality, thermal forcing dominates at thickening sites but is absent in thinning and transitional regimes, where hydrological and disturbance processes prevail. These class-dependent relationships were not detectable in pooled analyses, underscoring the need for trajectory-aware analysis in permafrost research.

We present the Alaska Coupled Observations of Land-atmosphere Dynamics (Alaska-COLD) dataset, comprising hourly air and soil temperature measurements from 12 sites across Alaska's north-central regions (64°–70°N). Each site measures air temperature and soil temperatures at four depths (0–0.7 m). Seven sites provide 714–726 days of continuous records (Summer 2023–2025) capturing multiple freeze-thaw (FT) cycles. We classify FT phenology into thawing, thawed, freezing, and frozen phases using surface temperature observations, enabling phase-specific analysis of thermal dynamics. We further derive FT metrics, including n-factors, degree-day totals, and damping ratios, to characterize surface energy exchange and subsurface thermal attenuation. The records reveal strong thermal damping with depth: surface-soil diurnal amplitudes range from 0.7–4.3°C, while the deepest sensors show only 0.004–0.047°C variation across the mean daily cycle. At the annual scale, ground-temperature ranges decrease from 11–25°C near the surface to 3–5°C at 45–75 cm depth, compared with air temperature ranges of 36–45°C. Thawed periods range from 39 to 92 days annually, and air-ground coupling vary strongly by FT phase, with R2 increasing from 0.02 during thawing to 0.69 during thawed conditions. A concise comparison with colocated ERA5-Land estimates shows stronger agreement for air temperature than for soil temperatures, highlighting the value of Alaska-COLD for resolving site-level ground thermal dynamics in permafrost environments. Alaska-COLD is accessible via: https://doi.org/10.5281/zenodo.17980271.

Active layer thickness (ALT) is a key indicator of permafrost response to environmental change. However, its long-term variation across regions can be driven by multiple geospatial factors whose relative contributions remain difficult to disentangle. In this work, we propose a framework combining two processes: long-term trajectory classification and causal inference. Accordingly, we introduce the Active Layer Dynamics Index (ALDI), categorizing ALT time series into six classes of direction and rate of change: Rapid and Gradual Thickening, Rapid and Gradual Thinning, Transitional and No Trend. ALDI relies on nonparametric trend detection, reversal identification, and bootstrap uncertainty quantification. We then conduct causal inference using site fixed-effects and first-difference estimators to examine how environmental drivers vary across ALDI classes. Applied to 129 CALM network sites (1990–2024), ALDI identifies a 3.5:1 thickening-to-thinning ratio across the pan-Arctic and substantial regional heterogeneity. In terms of causality, thermal forcing dominates at thickening sites but is absent in thinning and transitional regimes, where hydrological and disturbance processes prevail. These class-dependent relationships were not detectable in pooled analyses, underscoring the need for trajectory-aware analysis in permafrost research.

We present the Alaska Coupled Observations of Land-atmosphere Dynamics (Alaska-COLD) dataset, comprising hourly air and soil temperature measurements from 12 sites across Alaska's north-central regions (64°–70°N). Each site measures air temperature and soil temperatures at four depths (0–0.7 m). Seven sites provide 714–726 days of continuous records (Summer 2023–2025) capturing multiple freeze-thaw (FT) cycles. We classify FT phenology into thawing, thawed, freezing, and frozen phases using surface temperature observations, enabling phase-specific analysis of thermal dynamics. We further derive FT metrics, including n-factors, degree-day totals, and damping ratios, to characterize surface energy exchange and subsurface thermal attenuation. The records reveal strong thermal damping with depth: surface-soil diurnal amplitudes range from 0.7–4.3°C, while the deepest sensors show only 0.004–0.047°C variation across the mean daily cycle. At the annual scale, ground-temperature ranges decrease from 11–25°C near the surface to 3–5°C at 45–75 cm depth, compared with air temperature ranges of 36–45°C. Thawed periods range from 39 to 92 days annually, and air-ground coupling vary strongly by FT phase, with R2 increasing from 0.02 during thawing to 0.69 during thawed conditions. A concise comparison with colocated ERA5-Land estimates shows stronger agreement for air temperature than for soil temperatures, highlighting the value of Alaska-COLD for resolving site-level ground thermal dynamics in permafrost environments. Alaska-COLD is accessible via: https://doi.org/10.5281/zenodo.17980271.

As Villanova University students navigate campus life, ghost stories tied to specific buildings, paths, and rituals circulate as grassroots spatial narratives. This article argues that these stories involving haunted halls, underground tunnels, and ritualized practices surrounding seals, arches, and fountains, function as forms of Radical Placemaking, through which students collectively reinterpret, appropriate, and sometimes resist the university’s officially sanctioned spatial order. Drawing on 162 student testimonies collected in 2019, translated into Spanish, and analyzed using topic modeling, co-occurrence mapping, and GIS visualization, the study demonstrates how vernacular stories encode lived experiences, informal knowledge, and alternative claims to campus space. Nine thematic clusters emerge, organized into three narrative domains: supernatural encounters anchored to institutional buildings (including Alumni Hall’s Civil War history, the St. Mary’s nun legend, and Tolentine Hall hauntings), ritual and tradition practices that reinscribe or subvert formal authority (the Corr Chapel arch, the Driscoll Hall seal ritual, and student ceremonies), and hidden-space narratives that imagine infrastructures beyond official visibility (such as underground tunnels linking campus buildings). Analysis of narrative transmission reveals uneven power relations: institutional channels circulate curated traditions aligned with university identity. Peer networks and personal experiences generate counter-mappings that privilege exploration, embodiment, and affect. Villanova’s ghost stories constitute spatial perceptions that enable students to assert belonging, contest institutional narratives, and produce place through collective storytelling within an evolving and hierarchically governed campus landscape.

Accurately predicting Active Layer Thickness (ALT) is essential for assessing permafrost stability and mitigating risks to Arctic infrastructure, ecosystems, and communities. Traditional physics-based models struggle with generalization due to sparse in-situ observations and complex environmental drivers. This study introduces a data-driven framework for ALT prediction in the Circum-Arctic combining field measurements, satellite and reanalysis data, and machine learning (ML) techniques. Maximum ALT for a given year is paired with geospatial inputs from earlier years, enabling prediction at four horizons: Same year, +1, +2, and +5 years. A robust multi-stage feature selection strategy is then applied to identify the most informative and temporally stable ALT drivers. To predict ALT, a weighted ensemble of three ML models (Categorical Boosting, Extremely Randomized Trees, and Bootstrap Aggregating) is trained separately for each horizon. The framework is evaluated using data from 115 sites across the Circumpolar Active Layer Monitoring network. Results show that the proposed approach consistently outperforms multiple baseline models across all horizons, with R^2 above 0.8 and RMSE under 25 cm. Future work will be focused on conducting ALT trend analyses to develop a multiclass index of permafrost degradation and regeneration at the Circum-Arctic scale.

For scientists, policymakers, decision makers, local communities and other stakeholders working in Alaska’s rapidly changing permafrost regions, accessible and timely insights derived from geospatial data are critical. This paper introduces a web-based Analysis and decision Support system for Alaska Permafrost (ASAP), designed to predict and visualize various permafrost-related phenomena, including wildfire risks and soil temperatures in Alaska. Utilizing a three-tier Service-Oriented Architecture, ASAP integrates real-time data acquisition, sophisticated high-speed data processing, and multiple machine learning models to deliver accurate and actionable insights. The system’s modular architecture enhances scalability, security, and user experience by seamlessly incorporating geospatial and meteorological data from both external and internal sources. The webapp’s automated processes streamline data selection, providing users with an intuitive interface that offers detailed visualizations and predictions. By bridging data science and environmental monitoring, ASAP offers a powerful tool for proactive decision-making in the face of accelerating Arctic transformation.

This paper describes a high performance, distributed artificial intelligence based approach for Arctic focused multivariate geospatial feature prediction to enhance weather model accuracy and computational efficiency through distributed dataset preprocessing. The proposed approach was tested using an Alaska wide time series air temperature dataset spanning 25 years. The workflow partitions the dataset into spatio-temporally independent chunks, which are preprocessed and scored locally. Feature scores from each chunk are then aggregated using a weighted voting average, ensuring that features consistently important across multiple regions are prioritized while maintaining global consistency. This distributed preprocessing offers benefits such as reduced computation time, enhanced model accuracy, improved generalization of AI models, and a more robust representation and understanding of complex geospatial data. Eight feature selection techniques covering filter, wrapper, and embedded methods were evaluated to identify the most relevant predictors, and their computational performance compared across distributed partitions. Four AI models were selected from linear, nonlinear, time-series machine learning, and non-time-series deep learning categories to predict air temperature and trained using databases of diverse regions from Alaska. The impact of the eight different feature selection techniques on model performance was evaluated using MSE, sMAPE, KGE, MPE (%), and prediction time. Results show that distributed preprocessing not only substantially reduces preprocessing time but also produces equivalent or better predictive performance compared to serial preprocessing. By capturing localized features and applying weighted aggregation, the workflow ensures global feature consistency while preserving regional significance. This leads to improved model generalization across heterogeneous Arctic regions and more accurate representation of spatially diverse geophysical patterns. Methods such as Kendall+DT and Pearson+MI exhibit superior scalability with increasing dataset size and computational resources, confirming their effectiveness for large-scale Arctic datasets.