Progress in Physical Geography: Earth and Environment

Understanding the spatial distribution of sinkholes in karst terrains is essential for evaluating groundwater resources, hydrological processes, and mitigating hazards such as land subsidence. Due to the rapidly evolving dynamic nature of sinkholes, automating sinkhole mapping is essential for maintaining accurate inventories. Additionally, human activities modify natural depressions and landforms, which complicates the task of distinguishing sinkholes from other types of depressions. This study introduces four novel morphometric parameters—berm-drop, width-depth ratio, maximum flow accumulation, and flow accumulation-to-area ratio—to improve automated sinkhole mapping, particularly in the Anthropocene, where human impacts on the landscape are pervasive. These four new parameters are integrated with eleven literature-based morphometric parameters into a Random Forest Model (RFM) to automate sinkhole mapping in the karst-dominated landscape of the Mark Twain National Forest, located in the Ozark Plateau of southeast Missouri, USA. The results demonstrate that the RFM offers great ability—about 95% overall accuracy—to distinguish sinkholes and non-sinkholes particularly human-modified depressions, such ponds. The berm-drop parameter plays a notable role in this distinction. This capability is particularly valuable in the study area, which is dominated by agricultural activities featuring a large number of ponds. The RFM identified over 4000 sinkholes across the entire study area, far surpassing the existing inventory of just 271 sinkholes maintained by the Missouri Department of Natural Resources. The model was particularly effective in detecting sinkholes of moderate to larger depths (depth ≥0.6 m), about 99% accurate in identifying sinkholes. Further, the RFM identified sinkholes of all sizes (area) accurately. This is particularly important, as larger and deeper sinkholes present heightened risks, including potential for substantial property damage and greater subsidence hazards. Thus, this research underscores the value of integrating these novel parameters for more accurate, large-scale sinkhole mapping, which is vital for informed land-use planning, hazard mitigation, and groundwater protection in karst regions.

The piedmont glacial landforms on the northern foothills of the Central Himalayas provide critical records of the Last Glacial Maximum (LGM) glaciation, yet the glacier–environment interactions in the Yarlung Zangbo River source region (SYZR) remain poorly constrained. Integrating field surveys, remote sensing, a previously established chronological and glacier reconstruction framework, and a coupled Precipitation-Temperature (P-T) model, this study systematically reconstructs the LGM paleoclimate and investigates its climatic-topographic drivers. Key findings include the following: (1) 20 piedmont glaciers covered 4000 km² during the LGM, 9.6 times of modern extent, with the largest lobes in Jiemayangzong and Kubiqu basins; (2) glacier scales show strong negative correlations with basal elevation (5000–5100 m) and slope (<10°), while resistant leucogranite bedrock preserved high divides by attenuating the “glacial buzzsaw” effect; (3) the P-T modeling reveals an Equilibrium-Line Altitudes (ELA) depression of 357 ± 169 m, a temperature decrease of 1.6 to 4.6°C, and a precipitation reduction of 10% to 28% during the LGM compared to modern levels. Topographic gaps that funneled moisture created significant spatial heterogeneity in glacier morphological characteristics. This study quantitatively resolves the LGM climate sensitivity of Himalayan piedmont glaciers, highlighting synergistic controls of regional climate forcing with local topography, lithology, and moisture availability.

Peatland development is affected by changing climates, disturbances such as fire, and autogenic processes. Peatlands may be susceptible to periods of peat loss or reduced accumulation resulting in hiatuses in the peat stratigraphy. At eight minerotrophic peatlands in isolated topographic depressions in western Washington State, peat accumulation rates were reconstructed using radiocarbon-dated continuous peat cores. Age-depth models, fit to 7 to 11 radiocarbon dates and known tephra ages per peatland, were built using a Bayesian approach that constrains the range of potential peat accumulation rates and specifies potential hiatus events at abrupt changes in organic soil composition, tephras, and abrupt changes in the bulk-density profile. The among-site mean long-term rate of organic matter accumulation varied from 30 to 40 g/m²/year during the Late Holocene but was much lower, 18–22 g/m²/year, between 9500 and 6500 year before present. Fifteen modeled hiatuses encompassed 22% of the period in the age depths models and were more common during 9500 to 6500 year before present. During this period, hiatuses suggest that 50% of the potential peat accumulation is missing. Compared to nearby pollen and charcoal records, the hiatal intervals were likely the result of peat loss from a lower water table, fire, or burial by 4–7 cm of Mazama tephra. Sites with small watershed:peatland area ratios had the largest hiatuses and small peatlands in kettle depressions had the smallest hiatuses, consistent with water-table sensitivity to climate change. Charcoal and pollen stratigraphy from hummocks at three sites indicate that fires during the last century occurred near the peat core sites with little impact on organic matter accumulation. The variability in peat accumulation, among sites and over time, suggests site-dependent climatic sensitivity of these biologically and hydrologically significant sites.

Remote sensing land use analyses are an indispensable method for geography, environmental science, and earth system science. Against the backdrop of the increasing climate and environmental crisis, these disciplines and their methods are gaining enormous importance in environmental policy. However, they often have a weak epistemological basis. While they are frequently viewed as technical and objective results, they are in fact the result of socio-technical practices in which the values, decisions, and practices of the creator are inextricably interwoven. This misinterpretation can lead environmental managers to place too much trust in remote sensing’s claims about the world. In two recent articles, I have both demonstrated and theoretically framed the importance of practices and values in land use mapping and shown that remote sensing often fails in three respects. First, it overlooks the importance of epistemological foundations; second, it overlooks the importance of reflexive research practice; and third, it lacks a critical technoethics. An important but still missing step in this argument is to use a concrete research example to reveal what these deficiencies look like and to make practical suggestions as to how they can be remedied. In this article, I thus deconstruct and critique one of my own published classification approaches and outline a better set of research practices for future remote sensing.

PhenoCam networks are useful for unattended monitoring of ecological dynamics at daily time-steps. Despite extensive networks of PhenoCams in many regions of the world, especially in North America and Europe, there are none in High Mountain Asia (HMA). Standard PhenoCams also require power and internet infrastructures that are not readily available in remote mountain areas such as HMA. Here, we assessed for the first time, the use and effectiveness of low-cost trailcams for high altitude phenological and temperature logging within the Himalayan alpine zone (>4000 m.a.s.l.). The ethos of this approach was to work with local community members in Himalayan villages above the treeline to maintain the PhenoCam systems for year-round operation. Results from this study show variations in phenology between two dominant plant communities - Juniperus spp. and Rhododendron spp. – in two high-altitude villages Dole (4038 m.a.s.l.) and Machermo (4392 m.a.s.l.) in the Khumbu region of Nepal. Juniperus spp. was found to have a longer growing season at higher altitudes (Machermo) compared to Dole. Seasonal temperature variations were demonstrated through temperature data extracted from the trailcam images. This study fills the current gap in our understanding of seasonal variations in phenophase of dwarf plants in the Himalayan alpine zone, but more importantly provides a proof-of-concept for a low-cost, low-carbon and community-maintained methodology to obtain long-term in-situ observations in the remote mountainous regions. While some missions encountered challenges due to user errors, this study underscores the potential for implementing citizen science initiatives in high-altitude Himalayan ecosystems, and demonstrates pathways for doing so.

Glacier surges and associated ice-dammed lake outburst floods pose significant hazards in the Karakoram, where fragmented historical records show unclear trends in surge behaviour and flood frequency. Therefore, this study integrates historical archives, remote sensing data, digital elevation models (DEMs), surface velocity time series (ITS_LIVE), and ground observations to analyse the surge dynamics and associated glacial lake outburst flood (GLOF) history of the Kumdan group glaciers (Chong Kumdan, Kichik Kumdan, and Aktash). Historical records cross-verified with original sources and imagery confirm 16 GLOFs since 1835, predominantly from Chong Kumdan and Kichik Kumdan, with none from Aktash, despite its surges. Chong Kumdan exhibits long active phases (7–10 years) and an ∼77-year surge cycle for its primary tributary ‘a’, with asynchronous surges in its tributary ‘b’ and the main trunk. It generated the most devastating floods, including significant events in 1835, 1926, and 1929, but the last GLOF occurred in 1934. The recent surge (2001–2010) caused the terminus to advance ∼100 m short of the Shyok River, which was therefore not dammed. Kichik Kumdan shows shorter active phases (∼2 years) with cycle lengths decreasing from 33 years (1833–1866) to 27 years (1970–1997). Its last significant GLOF occurred in 1903, with post-1970s surges failing to form stable ice dams. Aktash shows periodic surges but no GLOFs occurred due to effective subglacial drainage that prevents river blockages. We observe a decline in surge-induced GLOFs because of climate warming, which reduces mass accumulation, shortens surge cycles, and weakens ice dams. DEM analyses show recent thickening in reservoir zones of Chong Kumdan (∼22 m) and Kichik Kumdan (∼20 m) from 2015 to 2022, indicating potential future surges. These findings provide crucial insights for policymakers and local communities in managing glacier-related hazards for improved risk assessment and mitigation strategies.

Physical geography (PG) is currently experiencing a subtle revolution that reinterprets old ideas in light of new scientific, social, and environmental challenges. This paper addresses the thematic areas of climatology, biogeography, hydrology, geomorphology, and the cross-cutting role of geospatial technologies in disaster risk reduction, and speaks to geographers, environmental scientists, and policy-oriented scholars concerned with the implications of global change. Its importance lies in reframing PG not only as a technical discipline but also as a critical and integrative field, socially situated, capable of contributing to sustainability and socio-environmental justice. The key problem identified is the persistence of fragmented and technicist approaches that overlook the entanglement of natural processes with social, political, and cultural dynamics. Our central aim is to assess how different fields are incorporating integrative frameworks and to evaluate their role in shaping a renewed disciplinary identity. Climatology increasingly incorporates the social dimension of climate and the assessment of inequality through socio-economic scenarios to contextualize climate change. Biogeography integrates ecosystem services to link conservation with human well-being. Hydrology recognizes the hydro-social dimensions of water, reframing debates on justice and governance. Geomorphology engages critically with the political production of landscapes and their implications for spatial justice. Together, these developments indicate that PG does not weaken by engaging with social sciences; instead, it strengthens its explanatory capacity and enhances its societal relevance. We argue that this subtle revolution, grounded in long-standing geographical traditions, positions PG as a reflective, interdisciplinary, and socially engaged science. By overcoming the dichotomy between the physical and the social, PG can better address global environmental change, territorial conflicts, and sustainability challenges. Drawing on recent review literature and illustrative case-work, we examine the extent to which these fields are transcending technical specialization to address complex socio-environmental challenges through a critical, integrative lens.

Mountainous terrain is increasingly being measured and mapped by airplane-based LiDAR (Light Detection and Ranging) techniques, but the accuracy of these measurements in such topographically variable terrain is not well understood. For this study, we measured 179 mountain summits with differential GNSS static surveys and compared summit elevation and location measurements to those measured by LiDAR in point cloud data sets. We measured summits in 13 US states (Washington, Idaho, Montana, Utah, California, Nevada, Arizona, New Mexico, Michigan, Wisconsin, Kentucky, Colorado, and Pennsylvania) and two Canadian provinces (British Columbia and Nova Scotia). Summits included icecapped peaks, open rocky peaks, and tree-covered peaks ranging in elevation from 490 m to over 4000 m. LiDAR-point-cloud-derived summit elevations and locations were computed using four different methods: manual processing, highest ground return, highest return, and Lastools reclassification. The average one-sigma LiDAR vertical errors for each method were 0.50 m, 1.09 m, 9.83 m, and 1.96 m, respectively. Average one-sigma horizontal errors were 3.03 m, 2.41 m, 5.17 m, and 3.78 m, respectively. Errors are also presented separately for each type of summit. Error sources include sharp summits being unsampled, dense vegetation misclassified as ground, human-created structures misclassified as ground, snow/ice that can melt over time, and summit erosion over time.