This study aimed to examine the effect of plot size on soil loss under natural rainfall in a humid region. Six experimental plots with areas of 3 to 360 m 2 were established on a hillslope, north of Iran. The soil losses were measured for 14 events. The results showed that absolute soil loss (ASL, g) increased with increasing plot size, whereas specific soil loss (SSL, g m −2 ) decreased, both following a nonlinear pattern. Based on ASL data, soil erosion occurred primarily as sheet erosion in plots 3–22 m long, whereas clear signs of rill formation were observed when plot length increased from 30 to 60 m, which was also confirmed by field observations. While the decreasing trend of SSL was very gradual from plot length of 3 to 10 m and of 30 to 60 m, it decreased sharply by increasing plot length from 10 to 22 meters. Scaling the SSL by two power equations showed that the power varies from −0.58 to −0.29 with a positive correlation to rainfall depth. Both equations did not fit to high SSLs well, showing under-estimation for small plots and over-estimation for big plots. The results revealed a clear scaling relationship between plot length and soil loss variables. Soil loss rates tended to decrease with increasing plot length, indicating that sediment delivery efficiency declines as the flow path becomes longer. These findings highlight the important role of scale effects and sediment redistribution processes in controlling soil erosion on forested hillslopes.
Globally, specific regions are predominantly characterised by particular tree taxa according to their ecological attributes, and this dominance is reflected in modern pollen records. In Anatolia, pine ( Pinus ) and oak ( Quercus ), which have historical and cultural significance, are similarly represented as the two most important taxa in modern pollen assemblages. Based on the largest compilation of modern pollen records currently available (1968–2025), this study analyses the representativeness of arboreal pollen (AP) and the two dominant tree genera ( Pinus and Quercus ) across Anatolia. Regional variation among the sampled sites was also examined, and the relative abundances of deciduous (Qd) and evergreen (Qe) oak taxa were specifically assessed. Results show that AP exceeds non-arboreal pollen (NAP) in 82% of the samples (total 539 samples), with an average AP value of 75%. Pinus is consistently dominant, exceeding 50% in 202 samples, whereas Quercus generally remains below 10% (339 samples). The combined contribution of Pinus and Quercus to AP (QPinAP ratio) averages 74.1% and exceeds 50% in 436 samples, clearly demonstrating the contribution of the two dominant taxa within AP. Clear regional differences are observed in the representation of deciduous versus evergreen oak. The resulting data provide a comprehensive and regionally differentiated contribution to the reconstruction of past forest dynamics in Anatolia through the evaluation of AP ratios and the pollen representation of these two genera.
Quantifying soil erosion in vulnerable tropical drylands is critical for desertification management but remains methodologically challenging due to the spatial limitations of field-based methods. This study introduces a novel framework integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) with unsupervised statistical clustering to quantify erosion rates and validate spatial heterogeneity in the La Tatacoa dry forest, Colombia. Using 123 Sentinel-1 radar images (from 2018 to 2023), we generated high-resolution displacement velocity maps, which were then integrated with Normalized Difference Vegetation Index (NDVI) data from Sentinel-2 and geomorphometric attributes derived from the ALOS PALSAR Digital Elevation Model. Study-wide mean erosion rates ranged from 0.43 to 0.54 mm/year; however, these averages masked significant localized instability. Time-series K-means clustering of 100 randomized sampling points identified four distinct deformation regimes, statistically confirming that high-velocity zones are structural realities rather than measurement noise. Specifically, Cluster 4 identified critical hotspots where erosion rates reached 30 mm/year exceeding the United States Department of Agriculture (USDA) soil tolerance threshold (0.93 mm/year) by a factor of 32. These hotspots are systematically confined to dissected terrain with slopes > 9 ° and NDVI < 0.15 . By rigorously distinguishing genuine deformation regimes from background variability, this framework provides a scalable, evidence-based tool for prioritizing conservation interventions in data-limited dryland ecosystems.
Gully erosion is an important mechanism of landscape change in tropical mountain environments, yet the interactions among hydro-climatic forcing, soil erodibility, hydrological connectivity, and inherited landscape conditions remain insufficiently understood. This study investigates gully erosion susceptibility in the upper catchments of the Western Ghats, India, with particular emphasis on the role of geomorphic memory under monsoon-dominated climatic conditions. A Random Forest (RF) based Gully Erosion Susceptibility Index (GESI) was developed using topographic, hydrological, climatic, soil, and geomorphic variables, including slope, plan and profile curvature, stream power index, slope length and steepness, stream head density, rainfall erosivity, soil erodibility, and distance to palaeo-landslide initiation locations. The RF model achieved strong predictive performance (accuracy = 0.82; AUC = 0.87), indicating reliable discrimination between gully and non-gully locations. GESI values range from 0 to 0.98 (mean ≈0.30), with approximately 36% of the study area classified as having moderate to very high susceptibility. High-susceptibility zones are concentrated in areas characterized by elevated rainfall erosivity (>10,000 MJ mm ha −1 h −1 yr −1 ), high soil erodibility, and efficient drainage connectivity. Variable importance analysis identifies soil erodibility and rainfall erosivity as the dominant controls, while topographic and hydrological factors regulate runoff concentration and sediment transfer pathways. Rainfall–erosivity analysis reveals strong exponential relationships during peak monsoon months, demonstrating that relatively small increases in rainfall produce disproportionately large increases in erosive energy and promote the exceedance of critical erosion thresholds. August exhibits the highest erosivity values and the strongest rainfall–erosivity relationship, indicating that this period represents the principal window of erosive activity and gully initiation under peak monsoonal conditions. The contribution of distance to palaeo-landslide initiation locations further indicates that inherited geomorphic disturbances influence present-day susceptibility, supporting the importance of geomorphic memory in shaping erosion responses. The results suggest that gully erosion in the upper Western Ghats is governed by the interaction of monsoon-driven hydro-climatic forcing, soil susceptibility, hydrological connectivity, and geomorphic inheritance rather than by any single environmental factor. More broadly, the study highlights how legacy effects and contemporary climatic forcing interact to regulate erosion processes in humid tropical mountain catchments and provides a framework for investigating gully development under increasing rainfall extremes.
This study examines the interactions among drought, heatwaves, and wildfires in Türkiye using compound and cascading hazard frameworks. The aim is to assess how these hazards co-occur or sequentially evolve over time and to quantify their relationships with observed wildfire events. The analysis is based on daily ERA5 reanalysis data covering the period 1970–2025. Compound hazards were identified as the simultaneous exceedance of multiple hazard thresholds on the same day, whereas cascading hazards were defined as sequential hazard processes occurring within specified time windows prior to wildfire events. Fire-day matching results show that 14.55% of the 1,863 recorded wildfires are associated with same-day compound conditions, indicating a limited role of simultaneous hazard interactions. In contrast, when antecedent cascading processes are evaluated using predefined temporal windows, their explanatory capacity increases progressively from 5.05% (7-day) to 22.28% (30-day), highlighting the importance of cumulative preconditioning over multi-week timescales. Across all temporal windows, the heatwave-wildfire (HW→WF) sequence emerges as the dominant cascading pathway, while other configurations contribute only marginally. The discriminatory ability of cascading indicators was evaluated using Receiver Operating Characteristic (ROC) and precision-recall analyses. For the HW→WF indicator, the area under the ROC curve (AUC) increases from 0.67 (7-day) to 0.74 (30-day), indicating improved performance with longer antecedent periods. In contrast, the DR→HW→WF indicator exhibits consistently lower performance, with AUC values ranging between 0.56 and 0.58 across all time windows. Precision-recall results show a similar pattern, although overall AUPR values remain low. Overall performance remains moderate, reflecting the rarity and spatial heterogeneity of wildfire events. The results indicate that wildfire occurrence in Türkiye is more strongly linked to antecedent climatic processes than to same-day conditions alone. However, these relationships represent proxy-based statistical associations and temporal sequencing rather than direct physical causality for individual fire events. The findings highlight the importance of integrating compound and cascading hazard perspectives into wildfire risk assessment and early warning systems. By emphasizing the cumulative and sequential nature of drought-heatwave-wildfire interactions, this study provides a transferable framework for understanding increasingly complex wildfire risks under Mediterranean-type climate conditions.
Microplastic (MP) pollution is a major environmental concern, as particles are transported through interconnected hydrological pathways such as domestic wastewater systems, rivers, and coastal waters, where they accumulate in sediments and pose risks to aquatic ecosystems. Developing effective and affordable approaches to intercept microplastics before they enter natural water bodies is therefore essential. In this study, a magnetically recoverable Fe 3 O 4 /carbon black (Fe 3 O 4 /CB) nanocomposite was surface-modified using oleic acid and stearic acid to enhance its affinity for hydrophobic polyethylene (PE) microplastics. The modified nanocomposite showed a clear improvement in plastic removal compared to the unmodified Fe 3 O 4 /CB material, achieving near-complete removal under optimized conditions. Under optimized conditions (15 mg dosage, 50 r/min, 60 minutes), the modified nanocomposite removed 197.2 mg of PE from 200 mg in synthetic wastewater, corresponding to a removal efficiency of 98.6%. Application to real washing machine effluent further demonstrated its effectiveness, achieving a removal efficiency of 94.2%. The enhanced performance is attributed to increased surface hydrophobicity and the synergistic interaction between Fe 3 O 4 and carbon black. Overall, these findings highlight the potential of surface-modified magnetic nanocomposites as practical intervention tools at critical hydrological control points, such as wastewater discharge pathways, to reduce downstream transport of microplastics into freshwater and coastal environments.
Ecuador lies within the Northern Andes of South America, a mountain chain driven by tectonic processes. Several studies have documented sediment mass transfer between the Andean chain and the Amazon Basin. It has been estimated that up to 95% of the sediment discharged into the Atlantic Ocean by the Amazon River originates from the Andean Range. Within this geological framework, the Ecuadorian Government constructed the Coca Codo Sinclair Hydroelectric Plant (CCSHP), a run-of-river dam, in the Coca River Watershed (CRW). However, in February 2020, the San Rafael Waterfall, located at 19.1 km downstream of the diversion dam, collapsed catastrophically, triggering a rapid headward erosion process that has damaged strategic infrastructure and continues threatening watershed stability. This study assesses how human activity has impacted hydrosedimentary dynamics, accelerating natural erosion processes and altering sediment transport and deposition patterns, with significant consequences for fluvial geomorphology and infrastructure. A multidisciplinary approach integrating hydrology, geomorphology and geology was applied. Hydrological modeling indicates that legacy sediments from the 1987 earthquake and 2002 volcanic eruption increased erosion rates by approximately 28% in the undammed Salado watershed. However, dam construction accounts for approximately 42% of the intensification of erosion observed between 2008 and 2016 in the Coca River reach between the diversion dam and the former San Rafael waterfall, mainly due to disrupted sediment connectivity and the loss of bed armoring. Historically, the Coca River experienced periodic natural damming associated with volcanic eruptions and landslides, responding through erosional adjustment. In contrast, hydroelectric infrastructure permanently altered its discharge and sediment regimes, producing profound geomorphological changes: sediment aggradation formed an unexpected mid-catchment delta upstream of the diversion dam, while the release of sediment-starved waters downstream triggered severe degradation with erosion advancing 15.5 km over a 5-year period through July 2025.
Citizen science approaches are increasingly used to support data generation and public engagement across the environmental sciences, yet their wider pedagogical value within physical geography remains underexamined. To explore the value of mapping-based citizen science for both glaciological investigation and geographical learning, we developed GlacierMap , an interactive online tool enabling participants to map glacier change in Peru’s Cordillera Blanca over a 34-year period. GlacierMap was designed not only to assess the feasibility and value of crowd-sourced glacier mapping, but also as a vehicle to support experiential learning of the trends in, and impacts of, tropical mountain glacier retreat. Participants mapped glacier change from Landsat imagery for two different years, 1984 and 2018, with analysis of glacier extents mapped by both ‘expert’ and ‘non-expert’ participant groups supporting assessment of the value of GlacierMap as a data generation activity. Participant responses to pre- and post-mapping surveys were also analysed to understand the benefits, challenges, and limitations of citizen science for the purposes of crowd-sourcing glacier inventory data, dissemination, and geographical learning. We found that while many of the individual mapping contributions were a good fit to the Randolph Glacier Inventory, there was considerable variability in accuracy, for both experts and non-experts, likely due to challenges of image interpretation, levels of engagement with the task, and a need for more targeted pre-task training. Crucially, however, participants consistently reported improved understanding of glacier change and its downstream impacts, with teachers communicating the value of the tool for classroom use. These findings demonstrate that mapping-based citizen science can meaningfully enhance climate and spatial literacy, highlighting the potential for tools like GlacierMap to strengthen connections between physical geography research, schools, and wider publics.
Wetlands in the Yellow River Basin face the composite pressures of climate change and human disturbances. However, analyzes of the major riverine wetlands remain far less than those of the headwater and estuarine regions in the Yellow River Basin. Based on the Google Earth Engine platform, we combined a sample migration strategy and Random Forest classification to produce an annual 30 m riverine wetland dataset (1986-2022) for the Yellow River Basin (excluding the headwater and estuarine regions). We then analyzed the spatiotemporal change and driving factors of water bodies, mudflats, and marshes. Results show that long-term wetland maps exhibit high reliability, with overall accuracy above 90%, and an average Kappa coefficient exceeding 0.88. The expansion of water bodies and mudflats failed to offset marsh degradation, leading to a net riverine wetland loss of 1326.30 km2, with the total wetland area in 2022 decreasing to 95% of its 1986 level. Increasing temperature and runoff at the Lanzhou station are associated with marsh core loss and fragmentation, accompanied by partial conversion to water bodies and mudflats, thereby threatening wetland biodiversity and carbon sequestration capacity. Water body expansion and increased connectivity are primarily associated with runoff at the Lanzhou station, average temperature, and precipitation. Landscape change of mudflats in the midstream and downstream is associated with average temperature, sediment discharge at the Huayuankou station, runoff at the Toudaoguai station, and regional human activities. This study provides critical data support and spatiotemporal evidence for riverine wetland conservation and restoration in the Yellow River Basin.
The development of varied landforms in the Dehradun valley is the result of tectonic activity and sculptured climatically driven surface processes. Stretching for about 35 km between the Tons and Song river valleys, the Main Boundary Thrust (MBT) in the Dehradun valley is defined by geomorphological attributes that suggest neotectonics along this active geological structure. The MBT and its splay thrusts are arcuate in nature as evident from deformation on the landforms, where north dipping discontinuous fault scarps are developed along the MBT. The conglomerates of the Upper Siwalik rocks in the immediate footwall of the MBT are highly sheared, having dips ranging from steeply dipping to vertical. Three fault traces in the hanging wall of the MBT are observed that stretch for about 1.5 km and are aligned in parallel. The fault traces are represented by linearly arranged paleo-sag ponds, and the fault zones are marked by highly sheared bedrock. Fault traces are also observed along the Main Boundary Fault in the Kalawa-Kalsi area, where the fault plane dips north. Structurally controlled drainages are observed along the trace of the MBT, Santaurgarh Thrust, Santaurgarh Anticline, and Nagsidh Anticline; where numerous paleochannels, incised channels, and radial drainage are observed. Wide wind gaps are formed, some measuring more than 1.5 km in width. Present findings address the adjustment of the fluvial system with the ongoing tectonic activity.
Eutrophication is a nutrient-driven shift in trophic status characterized by the appearance of green or blue green algae and declining water quality. Non-point source (NPS) pollutants are diffuse pollutants originating from multiple sources like agriculture runoff, urban waste, animal or human activities, etc. are major drivers of trophic transitions and algal proliferation. However, their seasonal influences on nutrient balance and trophic state remain understudied. This study provides an integrated framework combining water quality parameters, Carlson trophic state index (CTSI), Redfield ratio, and normalized differentiated chlorophyll index (NDCI) to understand the effect of NPS on nutrient loading and trophic transition across three major wetlands in Guwahati city, Deepor Beel, Dighalipukhuri, and Jorpukhuri during the post-monsoon season. Water quality parameters including temperature, pH, EC, TDS, DO, chlorophyll-a, NO3-, TKN, TN, TP, PO43-, and COD were analyzed from sampling points vulnerable towards NPS pollution across the wetlands. Results showed severe eutrophication intensity with CTSI values ranging from 113 to 115 in Deepor Beel, 110 to 114 in Dighalipukhuri, and 108 to 114 in Jorpukhuri. Redfield ratio of all the three wetlands indicated strong nitrogen dominance (TN/TP >16), with Deepor Beel showing highest spatial heterogeneity, while Dighalipukhuri and Jorpukhuri remain consistently phosphorus-limited across all points. Overall eutrophication intensity is highest in November across sites, with multiple hotspots exceeding TN/TP >50. NDCI mapping validated these findings, showing elevated chl-a concentrations along the pollution-prone margins of Deepor Beel. Across all wetlands, lower DO coincided with elevated chlorophyll-a, phytoplankton driven turbidity (Tchl-a > Tsd) and warmer post-monsoon temperatures are consistent markers of high eutrophication.
The Environmental Kuznets Curve (EKC) provides a theoretical framework for exploring the evolution of human-nature relationships. However, existing EKC-based studies often oversimplify human systems and natural systems, and few studies have conducted a multidimensional analysis of EKC characteristics. To address these limitations, this study conceptualizes the human-nature relationship as the interaction between human activity intensity and environmental quality, quantified using the Human Activity Index (HAI) and the Ecological Quality Index (EQI), respectively. Using panel data from 368 Chinese cities during 2000-2020, we construct composite indicators to characterize the multidimensional states of human and natural systems and apply the EKC framework to analyze their coupled dynamics. Building on the conventional EKC model, we further develop a set of EKC characteristic metrics to systematically describe transition timing, HAI-EQI interactions at the turning point, and dynamic patterns before and after the turning point. Cities exhibiting EKC-type relationships are subsequently classified into distinct human-nature transition pathways, and key driving factors and their heterogeneous effects are examined. The results show that although HAI and EQI increased in most cities, only 147 cities exhibited clear EKC-type trajectories, which can be further grouped into five distinct clusters based on EKC characteristics. Urban expansion, slope, greenspace, forest resilience, and elevation emerged as dominant drivers with heterogeneous influences on EKC dynamics. By extending EKC analysis from simplified indicators to a multidimensional and process-oriented framework, this study advances EKC interpretation beyond pattern identification and provides policy-relevant insights for differentiated urban sustainability strategies and coordinated environmental governance in China.
In recent years, bark beetle outbreaks have become a significant threat to forest ecosystems in Central Europe, a trend exacerbated by climate change, creating favourable environmental conditions for pest proliferation. This has increased the interest in remote-sensing-based detection of bark beetle infestations. The objective of this study is to detect and reconstruct forest disturbances in a mountainous area situated in the Upper Puster Valley (South Tyrol, Italy) and quantify spatio-temporal patterns, with a special focus on bark-beetle-induced damages. The study area was severely impacted by the storm VAIA in autumn 2018 as well as widespread snow breakage in subsequent winters, causing large amounts of residual wood in the forests and providing brood material for bark beetles. Combined with warm and dry summer conditions, populations grew significantly. Using a Sentinel-2 satellite time series spanning from 2018 to 2024, we applied a parametric bi-temporal change detection approach based on linear regression. Validation of infestation data from 2021 to 2023 with a reference dataset yielded detection rates ranging from 52% to 92%. The analysis of elevation influence on damage patterns revealed significant differences between abiotic and biotic disturbances. The interannual clustering of forest disturbances was analysed based on bivariate join count statistics. The results indicate a strong spatial correlation between years for both disturbance types. The highest correlations were found for bark beetle infestations, clustering highly around previous disturbances, underlining the importance of understanding interannual spatial connectivity and implementing timely targeted management strategies to limit the spread of outbreaks.
The Patagonian Icefields in the Andes Mountains represent South America's largest solid freshwater reserves and are highly sensitive indicators of regional climate variability. This study analyzes the contrasting behaviors of two emblematic Glaciers of the Southern Patagonia Icefield: Upsala Glacier, characterized by sustained retreat and Perito Moreno Glacier, historically considered relatively stable. Using Landsat 5, 7, and 8 imageries from 1997-2023, we provide a quantitative assessment of frontal displacement, area change, and retreat dynamics, offering additional insights beyond prior studies. For Upsala Glacier, we confirm and refine earlier reports of significant retreat, measuring similar to 6989 +/- 0.56 m of frontal loss, an average retreat rate of -304.55 +/- 0.78 m/year (p < 0.05), and a statistically significant acceleration of -1.9 +/- 1.11 m/year(2). Surface area declined by similar to 73.5 +/- 0.22 km(2) (similar to 9% of its 1997 extent), at an average rate of -3 +/- 0.17 km(2)/year, with continued retreat after 2023 (100 +/- 15 m in 2024 and 291.5 +/- 15 m in 2025). In contrast, Perito Moreno Glacier exhibited near-equilibrium conditions with no sustained frontal retreat for most of the study period, but since 2016 has undergone a marked transition toward sustained frontal retreat, with retreat rates reaching -55 +/- 0.78 m/year and accounting for nearly two-thirds of its total area loss since 1997 (similar to 3 +/- 0.15 km(2)), with spatially asymmetric retreat-advance dynamics. Recent observations further confirm this shift, with a pronounced retreat event in 2025 reaching similar to-385 +/- 15 m representing the largest frontal recession recorded over the study period. These contrasting Glacier responses highlight the strong influence of Glacier geometry and local dynamics on frontal behavior, emphasizing the importance of sustained satellite monitoring and the integration of complementary datasets for understanding the future evolution of Patagonian Glaciers.
The record-breaking 2023 Canadian wildfires attracted considerable attention; however, the bidirectional feedbacks between wildfires and meteorology remain unclear. Here, we use the fully coupled Weather Research and Forecasting model with Chemistry (WRF-Chem) to investigate the dynamic interactions among wildfire emissions, aerosol-radiation interactions (ARI), and meteorological responses during this extreme event. The model reasonably reproduced the key spatial and temporal characteristics of wildfire smoke aerosols. Results show that wildfire aerosols caused substantial shortwave radiative perturbations, characterized by strong surface and top-of-atmosphere cooling, with a domain-averaged 8-day mean shortwave radiative perturbation reaching -165 W m-2, and pronounced atmospheric heating of up to +140 W m-2. These effects spatially aligned with regions of elevated optical depth (AOD). The atmospheric heating, primarily driven by light-absorbing carbonaceous aerosols (CAs), enhanced atmospheric stability and suppressed turbulent mixing, while also leading to reduced cloud formation and drying of the upper troposphere. As a result of the enhanced stability, the planetary boundary layer height (PBLH) decreased locally by up to similar to 500 m, accompanied by reduced near-surface wind speeds. Importantly, increased humidity and weakened winds suppressed fire potential, suggesting that dense smoke plumes modified local meteorology in ways that inhibited further wildfire spread. These findings highlight the critical role of fully coupled models in capturing the complex wildfire-aerosol-radiation-meteorology feedback loop and emphasize the importance of representing two-way fire-weather interactions in future wildfire prediction and mitigation efforts.
Landslide susceptibility mapping faces the challenge of subjective weight assignment in Multi-Criteria Decision-Making (MCDM) approaches, while machine learning methods lack transparency in factor importance. This study presents a hybrid approach integrating Forest-Based Classification and Regression (FBCR) with MCDM techniques in Qasri District, Kurdistan, Iraq, a transitional zone between Zagros Mountains and foothills with complex topography. Using 883 mapped landslides and 15 conditioning factors from multi-source geospatial data, we developed three susceptibility models: FBCR, SAW, and TOPSIS. FBCR achieved strong performance (R2 = 0.93 training, 0.92 validation) while generating objective variable importance scores used as MCDM weights. The LS-factor was the most influential predictor (14%), followed by NDVI (11%) and land cover (10%). Validation using ROC analysis showed SAW achieving highest performance (AUC = 0.933), followed by FBCR (0.908) and TOPSIS (0.882). However, spatial distributions differed markedly: FBCR classified 64% of the area as very high susceptibility, SAW 37%, and TOPSIS 15%, reflecting their contrasting aggregation logics. Uncertainty analysis using the Assess Sensitivity to Attribute Uncertainty with a triangular simulation and uniform +/- 5/10/20% perturbations (100 iterations) revealed stability decline from 92.02% (+/- 5%) to 83.01% (+/- 10%) and 70.41% (+/- 20%), while IQR and SD of outcomes widened, pinpointing stable core hotspots whereas marginal hillslopes were most sensitive. Overall, FBCR produced a high-confidence susceptibility map with interpretable drivers while SAW yielded the sharpest class stratification from the same factors and weights. Crucially, embedding simulation-based uncertainty within the same GIS environment transformed a deterministic map into a confidence-aware product suitable for planning and risk management.
We propose more-than-human (MTH) geomorphologies as a conceptual reframing for undertaking socially situated, geographical and holistic landscape science. With increasing momentum in the environmental social sciences, more-than-human approaches offer less anthropocentric ways of perceiving landscapes and associated knowledge production. Here, the history of process conceptualisations in geomorphology, the study of the development and resultant landscape, is revisited from its geologic origins to its contemporary geographic framings. Historical and process geomorphologies have been separated throughout much of this history. Process philosophy, originated by Whitehead in 1925 but considered by geomorphologists much later, potentially offers a broader consideration of landscapes as relational and socially produced. This relational perspective shares many connections with Indigenous perspectives. MTH geomorphologies are presented as a logical extension to this history through its consideration of non-human agency. We ground our approach using a case study from New Zealand, showing how MTH approaches expand earlier approaches to landscape analysis. We discuss ways in which MTH geomorphologies could be facilitated and conducted in practice, emphasising the place-based and holistic nature of the method that makes for the best use of the best knowledge in each situation. We suggest MTH geomorphologies as a way to move beyond 'who speaks for nature', to allowing nature to speak for itself through more attentive practices that listen and understand landscapes.
The glaciers on northern slope of the Western Himalayas play an irreplaceable role in maintaining water resource balance in northwestern South Asia, regulating river runoff stability, and providing ecological barrier functions. Under the context of global warming, the accelerated melting of glaciers in this region exhibits significant differences from those in the Eastern and Central Himalayas and has led to hydrological reorganization and disaster risk increases, seriously threatening regional ecological security and social stability. Therefore, there is an urgent need to strengthen the monitoring and research on the dynamics and driving mechanisms of glaciers on the northern slope of the Western Himalayas. Based on Landsat TM/ETM+/OLI series remote sensing, this study uses the ratio threshold method to extract glacier boundaries for different periods on northern slope of the Western Himalayas from 1990 to 2024, systematically analyzing the temporal and spatial characteristics of glacier changes and their influencing factors. Results are as follows: (1) The study area glaciers exhibit a pronounced and accelerating retreat trend, with an average annual retreat rate of 0.65%/a during the study period. (2) Glacier area increases with elevation and then decreases, showing a unimodal pattern. The most pronounced glacier retreat occurs between 5400 and 5600 m. Mid-slope regions with gradients of 25 degrees-30 degrees represent the primary zones of glacier recession. (3) Some glaciers in study area are covered by supraglacial debris, and the debris-covered glaciers (accounting for 18.6% of the total glacier area) exhibit greater stability. This indicates that supraglacial debris can, to some extent, suppress glacier ablation in the study area. Valley glaciers are widely distributed, and the elevation at which glaciers develop is a key factor influencing the rate of area change among different glacier types. (4) Based on the analysis of glacier-climate responses with a 10-year lag, rising air temperature (0.015 degrees C/a) and decreasing precipitation (-7.04 mm/a) are the primary climatic drivers of accelerated glacier retreat in the study area. Among these factors, anomalously high summer temperatures and the substantial reduction in solid precipitation exert particularly pronounced impacts on glacier ablation. This study reveals the temporal and spatial changes of glaciers on northern slope of the Western Himalayas and their climate-driven mechanisms, aiming to provide a scientific basis for adaptive management strategies and to enhance the security of regional water resources.
