Yingkui Li

Mountain glaciers are sensitive indicators of climate change and provide essential freshwater resources for downstream ecosystems and human societies. Although glacier area changes have been extensively investigated in the Greater Caucasus, no long-term assessments of regional glacier ice volume reconstruction have been made. This study presents the first comprehensive reconstruction of glacier ice volume changes across the entire Greater Caucasus over the past 65 years, integrating glacier inventories at five observational periods (1960, 1986, 2000, 2014, and 2025) with the automated PalaeoIce reconstruction framework. Total glacier ice volume declined from 88.08 ± 4.5 km³ in 1960 to 42.66 ± 1.1 km³ in 2025, corresponding to an overall loss of more than half of the Caucasus glacier volume (51.6%) since 1960. Ice volume loss accelerated markedly after 2000, with the most rapid decline (27% or −2.45 yr⁻¹) occurring between 2014 and 2025. Spatial patterns in ice volume change show pronounced regional variability. Relative ice loss over the past 65 years was greatest among glaciers in Azerbaijan, which experienced a 98.4% reduction in ice volume. In comparison, glaciers in Georgia and Russia lost 52.6% and 50.9% of their ice volume, respectively, over the same period. Sectoral analysis further reveals contrasting spatial responses across the Greater Caucasus, with the eastern sector experiencing the greatest proportional ice loss of 71.6% over the 65-year study period, followed by the western sector with 60.4% and the central sector with 47.3%. Glacier thickness also declined across the Greater Caucasus over the 65-year study period. Mean glacier thickness decreased from 28.2 ± 1.41 m in 1960 to 17.4 ± 0.75 m in 2025, while mean maximum thickness declined from 88.7 ± 4.44 m to 54.1 ± 2.33 m over the same period. These findings provide region-wide quantification of long-term glacier ice volume and thickness evolution in the Greater Caucasus and establish an important baseline for assessing glacier response to ongoing climate change, future water availability, natural hazards, and regional cryospheric evolution.

Glaciers in Wind River Range, Wyoming, have experienced substantial reductions in surface area and volume in recent decades, yet existing inventories contain temporal gaps and often mix glaciers with seasonal snowpack. We mapped and classified glacier outlines using high-resolution satellite imagery in 2006 in Google Earth Pro (version 7.3.7.1327) and compared them against inventories from 1963–1974, 2015, and 2020. A classification approach was applied to distinguish glaciers from snowpack based on Welch’s t-test of significant difference in elevation changes within and surrounding mapped outlines based on four digital elevation models from 2000 to 2019. Results show an 11% decrease in mapped glacier/snowpack features from 1963–1974 to 2006 (476 to 423), with a 21% reduction in mapped outlines classified as glaciers (314 to 247). Total glacier area declined from 48.35 km² in 1963–1974 to 34.06 km² in 2006, while volume decreased from 0.998 km³ to 0.701 km³. From 1963–1974 to 2015, area and volume losses reached 47% and 49%, respectively, consistent with accelerated regional retreat. An unusually low reduction observed from 2015 to 2020 is likely caused by methodological inconsistencies between existing inventories. This study underscores the need for standardized mapping protocols and robust classification between glaciers and snowpack to accurately document glacier changes and assess their impact on water resources.

Palaeoglacier reconstructions based on geomorphological evidence and present‐day topography are essential for deriving equilibrium‐line altitude (ELA) estimates and making robust palaeoclimatic approximations. Current tools to generate 3D glacier reconstructions and calculate ELAs are dependent on proprietary GIS software platforms and associated licences, limiting accessibility. This study presents Q‐GlaRe+, an open‐source QGIS plugin that implements established flowline‐based algorithms and removes proprietary software barriers whilst maintaining scientific accuracy. The toolset incorporates automated workflow logging that ensures reproducibility of glacier reconstructions. Validation against five Swiss Alpine glaciers with known glacier bed topography demonstrates ice thickness estimates consistent with published modelling benchmarks. ELA estimates closely match existing implementations, confirming comparable performance. By eliminating proprietary software dependencies whilst maintaining scientific accuracy, Q‐GlaRe+ supports a broader community use of glacier reconstruction tools and facilitates open data sharing and reproducible research practices.

Energy Geographies has a unique position at the intersection of geospatial and social science, and it now faces a defining methodological development with the rapid rise in Geospatial Artificial Intelligence (GeoAI). This paper examines where GeoAI has and has not been applied within energy research through two bibliometric analyses using the Dimensions database. The first establishes an updated picture of energy geographies scholarship from 2020 to 2026, mapping the field’s current priorities and geographic distribution as a baseline for evaluating GeoAI’s role. The second conducts a bibliometric analysis of GeoAI-specific energy publications from 2020 to 2026, which reveals significant GeoAI Application Gaps: a heavy concentration in energy extraction and production research and in renewable energy siting and grid optimization, while energy transition, justice, and the energy problems of underrepresented regions remain substantially underserved. GeoAI energy research is also more geographically concentrated than the broader field, dominated by a small number of countries, raising questions about the applicability of these tools to the energy challenges facing the rest of the world. We argue that this gap reflects a pattern of problem selection as much as technological limitation, and that energy geographers are well positioned to redirect the development of this new field. We outline three directions for future research: developing Explainable GeoAI to ensure transparency and accountability, expanding geographic coverage to address data biases that favor a small set of well-resourced countries, and confronting the computational energy paradox of carbon-intensive AI applied to sustainability-oriented research.

Glacial landforms record valuable information about past climates, but quantitative reconstructions depend on robust estimates of glacier characteristics, particularly the equilibrium-line altitude. Traditional approaches rely on global mean or median values of equilibrium-line altitude ratios (Accumulation Area Ratio, Toe-to-Headwall Altitude Ratio, and Area-Altitude Balance Ratio) to calculate equilibrium-line altitude. However, these ratios vary across glacier types and climatic regimes, meaning that use of a single value can introduce substantial errors. Here we present empirically-constrained global estimates of equilibrium-line altitude ratios for approximately 215,000 glaciers derived from numerical modeling. We find that the Accumulation Area Ratio provides the most reliable approximation of the equilibrium-line altitude, particularly when adjusted for glacier type. Explicitly accounting for climatic setting, glacier size, slope and aspect when selecting equilibrium-line altitude ratios further improves accuracy of past climate reconstructions. Our work provides insights into global glacier characteristics and establishes a stronger basis for reconstructing past climates.

Numerical palaeoglacier modelling is being applied to mountain glacier systems worldwide to investigate past ice dynamics, climatic controls, and glacier–climate interactions. At present, there is no overview or consensus framework for evaluating the performance of these models, nor agreed standards for determining whether simulated glacier distribution and geometry are plausibly reconstructed through time.To assess how palaeoglacier models in mountains are being validated and evaluated, we conducted a systematic review of 94 coupled mass-balance–ice-dynamics palaeoglacier models worldwide. For each study, we recorded validation approaches (visual, quantitative, and/or statistical), the glaciological attributes evaluated (extent, area, ice thickness, mass balance, ice flow, glacier distribution), the validation datasets used (direct geomorphological evidence versus previous model outputs), and the spatial structure of validation (point-, line-, polygon-, or grid-based). We also synthesised validation workflows to document the datasets, metrics, and decision criteria employed.Our results show that the methodological design used to validate model outputs vary considerably between studies. We found that 80% of reviewed studies incorporate visual evaluation of model outputs to some extent, and 43% rely exclusively on subjective visual interpretation. A further 4% of studies do not perform any form of model validation or evaluation. Most studies validate glacier length (89%), whereas 48% of cases validate only one individual model output. Geomorphic reconstructions are used as validation datasets in only 27% of studies, indicating that most validation workflows rely on mapped glacial landforms. Because glacier length and glacial landforms dominate validation strategies, point- and line-based features constitute the majority of spatial validation data, with polygon- or grid-based approaches remaining comparatively rare.We observe that the interpretation of model performance often remains subjective and reliant on the judgement of a limited number of research authors, which hinders reproducibility and intercomparison across studies. This reliance on subjective visual interpretation reflects persistent challenges in palaeoglacier model validation that are not being solved by existing tools and workflows designed to measure model-data fit. Our review indicates that uneven geomorphic evidence coverage, positional uncertainty between mapped landforms and simulated ice margins, resolution mismatches between geomorphic data and model outputs, chronological dating uncertainties, parameter uncertainty, and ‘equifinality’ (i.e., where multiple parameter combinations can yield similarly plausible glacier geometries) within glacier models collectively hinder robust quantitative validation.Therefore, we propose a probabilistic, equifinality-aware validation framework that integrates geomorphically based reconstructions, multiple model outputs (e.g., ice extent, ice thickness), temporal steps (e.g., LGM and present-day), and performance metrics (e.g., overestimation, underestimation). Our approach evaluates ice cover as a probability field derived from an ensemble of acceptable simulations, explicitly acknowledging parameter non-uniqueness of equifinal modelling outputs. This approach identifies spatial patterns of robust agreement and persistent uncertainty, avoids subjective selection of a best-fit simulation, and enables domain-wide validation that captures spatial and temporal heterogeneity in glacier behaviour, thereby providing a more transparent and reproducible basis for evaluating palaeoglacier model–data fit.

This study advances our understanding of Quaternary glaciation in the southeastern Tibetan Plateau by integrating geomorphic evidence with numerical ice sheet modeling to constrain paleoclimate and ice dynamics. We focus on the Haizishan Plateau, a key region with well-preserved landforms (cirques, U-shaped valleys, moraines) and cosmogenic nuclide chronology indicating major glaciations during MIS 2 and MIS 6. Using these geomorphic and chronological constraints, we first reconstructed the Last Glacial Maximum (LGM) ice margin and then applied the PaleoIce flowline model to estimate paleo-ice thickness and surface elevation. These independent geomorphic reconstructions were then compared to the results of three-dimensional paleo-ice sheet simulations performed with the Parallel Ice Sheet Model (PISM), which were driven by inferred paleoclimate conditions.The PISM simulations indicate an LGM characterized by a temperature depression of ~4°C and precipitation levels 60–70% of modern values. The model successfully replicates the regional ice cap evolution, showing limited glacial extent during MIS 3 and restricted advances in MIS 4. Critically, the simulated ice configuration aligns well with the geomorphically reconstructed evidence, including glacial lineation patterns, landform zonation, and overall extent, validating the model's performance. Our findings demonstrate the power of combining geomorphic reconstruction with dynamical modeling to refine the glacial and climatic history of high-mountain regions.