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Keywords = Cryosphere

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17 pages, 3731 KiB  
Article
Lake Water Depletion Linkages with Seismic Hazards in Sikkim, India: A Case Study on Chochen Lake
by Anil Kumar Misra, Kuldeep Dutta, Rakesh Kumar Ranjan, Nishchal Wanjari and Subash Dhakal
GeoHazards 2025, 6(3), 42; https://doi.org/10.3390/geohazards6030042 (registering DOI) - 1 Aug 2025
Viewed by 87
Abstract
After the 2011 earthquake, lake water depletion has become a widespread issue in Sikkim, especially in regions classified as high to very high seismic zones, where many lakes have turned into seasonal water bodies. This study investigates Chochen Lake in the Barapathing area [...] Read more.
After the 2011 earthquake, lake water depletion has become a widespread issue in Sikkim, especially in regions classified as high to very high seismic zones, where many lakes have turned into seasonal water bodies. This study investigates Chochen Lake in the Barapathing area of Sikkim’s Pakyong district, which is facing severe water seepage and instability. The problem, intensified by the 2011 seismic event and ongoing local construction, is examined through subsurface fracture mapping using Vertical Electrical Sounding (VES) and profiling techniques. A statistical factor method, applied to interpret VES data, helped identify fracture patterns beneath the lake. Results from two sites (VES-1 and VES-2) reveal significant variations in weathered and semi-weathered soil layers, indicating fractures at depths of 17–50 m (VES-1) and 20–55 m (VES-2). Higher fracture density near VES-1 suggests increased settlement risk and ground displacement compared to VES-2. Contrasting resistivity values emphasize the greater instability in this zone and the need for cautious construction practices. The findings highlight the role of seismic-induced fractures in ongoing water depletion and underscore the importance of continuous dewatering to stabilize the swampy terrain. Full article
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14 pages, 1859 KiB  
Article
Into the Blue: An ERC Synergy Grant Resolving Past Arctic Greenhouse Climate States
by Jochen Knies, Gerrit Lohmann, Stijn De Schepper, Monica Winsborrow, Juliane Müller, Mohamed M. Ezat and Petra M. Langebroek
Challenges 2025, 16(3), 36; https://doi.org/10.3390/challe16030036 - 30 Jul 2025
Viewed by 206
Abstract
The Arctic Ocean is turning blue. Abrupt Arctic warming and amplification is driving rapid sea ice decline and irreversible deglaciation of Greenland. The already emerging, substantial consequences for the planet and society are intensifying and yet, model-based projections lack validatory consensus. To date, [...] Read more.
The Arctic Ocean is turning blue. Abrupt Arctic warming and amplification is driving rapid sea ice decline and irreversible deglaciation of Greenland. The already emerging, substantial consequences for the planet and society are intensifying and yet, model-based projections lack validatory consensus. To date, we cannot anticipate how a blue Arctic will respond to and amplify an increasingly warmer future climate, nor how it will impact the wider planet and society. Climate projections are inconclusive as we critically lack key Arctic geological archives that preserved the answers. This “Arctic Challenge” of global significance can only be addressed by investigating the processes, consequences, and impacts of past “greenhouse” (warmer-than-present) climate states. To address this challenge, the ERC Synergy Grant project Into the Blue (i2B) is undertaking a program of research focused on retrieving new Arctic geological archives of past warmth and key breakthroughs in climate model performance to deliver a ground-breaking, synergistic framework to answer the central question: “Why and what were the global ramifications of a “blue” (ice-free) Arctic during past warmer-than-present climates?” Here, we present the proposed research plan that will be conducted as part of this program. Into the Blue will quantify cryosphere (sea ice and land ice) change in a warmer world that will form the scientific basis for understanding the dynamics of Arctic cryosphere and ocean changes to enable the quantitative assessment of the impact of Arctic change on ocean biosphere, climate extremes, and society that will underpin future cryosphere-inclusive IPCC assessments. Full article
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18 pages, 3347 KiB  
Article
Assessment of Machine Learning-Driven Retrievals of Arctic Sea Ice Thickness from L-Band Radiometry Remote Sensing
by Ferran Hernández-Macià, Gemma Sanjuan Gomez, Carolina Gabarró and Maria José Escorihuela
Computers 2025, 14(8), 305; https://doi.org/10.3390/computers14080305 - 28 Jul 2025
Viewed by 204
Abstract
This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are [...] Read more.
This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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18 pages, 3600 KiB  
Article
Long-Term Snow Cover Change in the Qilian Mountains (1986–2024): A High-Resolution Landsat-Based Analysis
by Enwei Huang, Guofeng Zhu, Yuhao Wang, Rui Li, Yuxin Miao, Xiaoyu Qi, Qingyang Wang, Yinying Jiao, Qinqin Wang and Ling Zhao
Remote Sens. 2025, 17(14), 2497; https://doi.org/10.3390/rs17142497 - 18 Jul 2025
Viewed by 452
Abstract
Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation [...] Read more.
Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation area in western China. This study presents the first high-resolution historical snow cover product developed specifically for the QLM, utilizing a multi-level snow classification algorithm tailored to the complex topography of the region. By employing Landsat satellite data from 1986–2024, we constructed a comprehensive 39-year snow cover dataset at a resolution of 30 m. A dual adaptive cloud masking strategy and spatial interpolation techniques were employed to effectively address cloud contamination and data gaps prevalent in mountainous regions. The spatiotemporal characteristics and driving mechanisms of snow cover changes in the QLM were systematically analyzed using Sen–Theil trend analysis and Mann–Kendall tests. The results reveal the following: (1) The mean annual snow cover extent in the QLM was 15.73% during 1986–2024, exhibiting a slight declining trend (−0.046% yr−1), though statistically insignificant (p = 0.215); (2) The snowline showed significant upward migration, with mean elevation and minimum elevation rising at rates of 3.98 m yr−1 and 2.81 m yr−1, respectively; (3) Elevation-dependent variations were observed, with significant snow cover decline in high-altitude (>5000 m) and low-altitude (2000–3500 m) regions, while mid-altitude areas remained relatively stable; (4) Comparison with MODIS data demonstrated good correlation (r = 0.828) but revealed systematic differences (RMSE = 12.88%), with MODIS showing underestimation in mountainous environments (Bias: −8.06%). This study elucidates the complex response mechanisms of the QLM snow system under global warming, providing scientific evidence for regional water resource management and climate change adaptation strategies. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Snow and Ice Monitoring)
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21 pages, 4829 KiB  
Article
Quantification of MODIS Land Surface Temperature Downscaled by Machine Learning Algorithms
by Qi Su, Xiangchen Meng, Lin Sun and Zhongqiang Guo
Remote Sens. 2025, 17(14), 2350; https://doi.org/10.3390/rs17142350 - 9 Jul 2025
Viewed by 381
Abstract
Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 [...] Read more.
Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. Comparative analyses demonstrated that integrating multiple vegetation indices (e.g., NDVI, SAVI) with terrain factors yielded superior accuracy compared to factors utilizing land surface reflectance or excessive variable combinations. While slope and aspect parameters marginally improved accuracy in mountainous regions, including them degraded performance in flat terrain. Notably, land surface reflectance proved to be ineffective in snow/ice-covered areas, highlighting the need for specialized treatment in cryospheric environments. This work provides a reference for LST downscaling, with significant implications for environmental monitoring and urban heat island investigations. Full article
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36 pages, 7227 KiB  
Review
Formation of Low-Centered Ice-Wedge Polygons and Their Orthogonal Systems: A Review
by Yuri Shur, Benjamin M. Jones, M. Torre Jorgenson, Mikhail Z. Kanevskiy, Anna Liljedahl, Donald A. Walker, Melissa K. Ward Jones, Daniel Fortier and Alexander Vasiliev
Geosciences 2025, 15(7), 249; https://doi.org/10.3390/geosciences15070249 - 2 Jul 2025
Viewed by 821
Abstract
Ice wedges, which are ubiquitous in permafrost areas, play a significant role in the evolution of permafrost landscapes, influencing the topography and hydrology of these regions. In this paper, we combine a detailed multi-generational, interdisciplinary, and international literature review along with our own [...] Read more.
Ice wedges, which are ubiquitous in permafrost areas, play a significant role in the evolution of permafrost landscapes, influencing the topography and hydrology of these regions. In this paper, we combine a detailed multi-generational, interdisciplinary, and international literature review along with our own field experiences to explore the development of low-centered ice-wedge polygons and their orthogonal networks. Low-centered polygons, a type of ice-wedge polygonal ground characterized by elevated rims and lowered wet central basins, are critical indicators of permafrost conditions. The formation of these features has been subject to numerous inconsistencies and debates since their initial description in the 1800s. The development of elevated rims is attributed to different processes, such as soil bulging due to ice-wedge growth, differential frost heave, and the accumulation of vegetation and peat. The transition of low-centered polygons to flat-centered, driven by processes like peat accumulation, aggradational ice formation, and frost heave in polygon centers, has been generally overlooked. Low-centered polygons occur in deltas, on floodplains, and in drained-lake basins. There, they are often arranged in orthogonal networks that comprise a complex system. The prevailing explanation of their formation does not match with several field studies that practically remain unnoticed or ignored. By analyzing controversial subjects, such as the degradational or aggradational nature of low-centered polygons and the formation of orthogonal ice-wedge networks, this paper aims to clarify misconceptions and present a cohesive overview of lowland terrain ice-wedge dynamics. The findings emphasize the critical role of ice wedges in shaping Arctic permafrost landscapes and their vulnerability to ongoing climatic and landscape changes. Full article
(This article belongs to the Section Cryosphere)
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20 pages, 3812 KiB  
Article
Rising Net Shortwave Radiation and Land Surface Temperature Drive Snow Cover Phenology Shifts Across the Mongolian Plateau During the 2000–2022 Hydrological Years
by Xiaona Chen and Shiqiu Lin
Remote Sens. 2025, 17(13), 2221; https://doi.org/10.3390/rs17132221 - 28 Jun 2025
Viewed by 339
Abstract
Snow cover phenology (SCP) serves as a critical regulator of hydrological cycles and ecosystem stability across the Mongolian Plateau (MP). Despite its importance, the spatiotemporal patterns of SCP and their climatic drivers remain poorly quantified, constrained by persistent gaps in satellite snow cover [...] Read more.
Snow cover phenology (SCP) serves as a critical regulator of hydrological cycles and ecosystem stability across the Mongolian Plateau (MP). Despite its importance, the spatiotemporal patterns of SCP and their climatic drivers remain poorly quantified, constrained by persistent gaps in satellite snow cover observations. Leveraging a high-resolution (500 m) daily gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover dataset combined with reanalysis climate datasets, we systematically quantified SCP dynamics and identified the dominant controls during the 2000–2022 hydrological years using trend analysis and ridge regression. Our results reveal a significant divergence in SCP parameters: snow end dates (De) advanced markedly across the entire plateau (0.29 days yr−1, p < 0.01), accounting for 90.39% of SCP anomalies. In contrast, snow onset date (Do) exhibited unnoticeable changes, explaining 9.58% of SCP changes. Attribution analysis demonstrates that 47.72% of De variability stems from increased net shortwave radiation (+0.38 Wm−2 yr−1) and rising temperatures (+0.06 °C yr−1) during the melting season, with net shortwave radiation exerting stronger control (R2 = 0.73) than temperature (R2 = 0.63). This study establishes the first continuous, high-resolution SCP climatology for the MP, providing mechanistic insights into cryosphere–atmosphere interactions that inform adaptive water resource strategies for climate-vulnerable arid ecosystems in this region. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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20 pages, 3416 KiB  
Article
Deflection Prediction of Highway Bridges Using Wireless Sensor Networks and Enhanced iTransformer Model
by Cong Mu, Chen Chang, Jiuyuan Huo and Jiguang Yang
Buildings 2025, 15(13), 2176; https://doi.org/10.3390/buildings15132176 - 22 Jun 2025
Viewed by 366
Abstract
As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the [...] Read more.
As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the progression of localized damage. The accurate modeling and forecasting of deflection are thus essential for effective bridge health monitoring and intelligent maintenance. To address the limitations of traditional methods in handling multi-source data fusion and nonlinear temporal dependencies, this study proposes an enhanced iTransformer-based prediction model, termed LDAiT (LSTM Differential Attention iTransformer), which integrates Long Short-Term Memory (LSTM) networks and a differential attention mechanism for high-fidelity deflection prediction under complex working conditions. Firstly, a multi-source heterogeneous time series dataset is constructed based on wireless sensor network (WSN) technology, enabling the real-time acquisition and fusion of key structural response parameters such as deflection, strain, and temperature across critical bridge sections. Secondly, LDAiT enhances the modeling capability of long-term dependence through the introduction of LSTM and combines with the differential attention mechanism to improve the precision of response to the local dynamic changes in disturbance. Finally, experimental validation is carried out based on the measured data of Xintian Yellow River Bridge, and the results show that LDAiT outperforms the existing mainstream models in the indexes of R2, RMSE, MAE, and MAPE and has good accuracy, stability and generalization ability. The proposed approach offers a novel and effective framework for deflection forecasting in complex bridge systems and holds significant potential for practical deployment in structural health monitoring and intelligent decision-making applications. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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17 pages, 4154 KiB  
Article
Mapping Mountain Permafrost via GPR-Augmented Machine Learning in the Northeastern Qinghai–Tibet Plateau
by Yao Xiao, Guangyue Liu, Guojie Hu, Defu Zou, Ren Li, Erji Du, Tonghua Wu, Xiaodong Wu, Guohui Zhao, Yonghua Zhao and Lin Zhao
Remote Sens. 2025, 17(12), 2015; https://doi.org/10.3390/rs17122015 - 11 Jun 2025
Viewed by 708
Abstract
Accurate permafrost mapping in mountainous regions is hindered by sparse in situ observations and heterogeneous terrain. This study develops a GPR-augmented machine learning framework to map mountain permafrost in the northeastern Qinghai–Tibet Plateau. A total of 1037 presence–absence samples were compiled from boreholes, [...] Read more.
Accurate permafrost mapping in mountainous regions is hindered by sparse in situ observations and heterogeneous terrain. This study develops a GPR-augmented machine learning framework to map mountain permafrost in the northeastern Qinghai–Tibet Plateau. A total of 1037 presence–absence samples were compiled from boreholes, soil pits, 128 GPR transects collected in 2009, and 22 additional empirical points above 4700 m, covering diverse topographic and thermal conditions. Thirteen classification algorithms were evaluated using 5-fold cross-validation repeated 40 times, with LightGBM, CatBoost, XGBoost, and RF achieving top performance (F1 > 0.98). Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. Aspect-controlled permafrost boundaries were captured, with modeled lower elevation limits varying by >200 m across slope directions. SHAP analysis showed that climate and soil variables contributed nearly 80% to model outputs, with LST, FDD, BD, and TDD being dominant. Several predictors exhibited threshold or nonlinear responses, reinforcing their physical relevance. Additional experiments confirmed that integration of GPR and high-elevation constraint samples significantly improved model generalization, especially in underrepresented terrain zones. This study demonstrates that a GPR-augmented machine learning framework can support cost-effective, physically informed mapping of frozen ground in complex alpine environments. Full article
(This article belongs to the Special Issue Advanced Ground-Penetrating Radar (GPR) Technologies and Applications)
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11 pages, 989 KiB  
Editorial
Natural Ice/Snow and Humans: From Mountain to Sea
by Zhijun Li, Li Zhou, Sasan Tavakoli and Ove Tobias Gudmestad
Water 2025, 17(12), 1754; https://doi.org/10.3390/w17121754 - 11 Jun 2025
Viewed by 346
Abstract
The cryosphere constitutes a vital component of the Earth’s system [...] Full article
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25 pages, 15092 KiB  
Article
Simulation of Active Layer Thickness Based on Multi-Source Remote Sensing Data and Integrated Machine Learning Models: A Case Study of the Qinghai-Tibet Plateau
by Guoyu Wang, Shuting Niu, Dezhao Yan, Sihai Liang, Yanan Su, Wei Wang, Tao Yin, Xingliang Sun and Li Wan
Remote Sens. 2025, 17(12), 2006; https://doi.org/10.3390/rs17122006 - 10 Jun 2025
Viewed by 447
Abstract
Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the [...] Read more.
Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R2: 0.873, and MAPE: 0.104), and its inversion of QTP’s ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost. Full article
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17 pages, 1280 KiB  
Review
Rapid Change in the Greenland Ice Sheet and Implications for Planetary Sustainability: A Qualitative Assessment
by Abhik Chakraborty
Earth 2025, 6(2), 55; https://doi.org/10.3390/earth6020055 - 8 Jun 2025
Viewed by 805
Abstract
Ubiquitous and accelerating mass loss from the Greenland Ice Sheet (GrIS) has been widely reported in recent scientific studies, implying rapid changes in the Arctic cryosphere. However, while numerous studies provide accounts of glacial mass loss and consequent sea level change, a qualitative [...] Read more.
Ubiquitous and accelerating mass loss from the Greenland Ice Sheet (GrIS) has been widely reported in recent scientific studies, implying rapid changes in the Arctic cryosphere. However, while numerous studies provide accounts of glacial mass loss and consequent sea level change, a qualitative assessment of the implications is conspicuously absent. This scoping review addresses that gap by synthesizing the recent scientific literature related to cryospheric change in Greenland and its implications for key species and ecological processes; and highlights the necessity of understanding the bigger picture of how multiple ecological processes, abiotic-biotic assemblages, and cryosphere-human interactions with the environment are rapidly changing and pushing the Arctic into a possible no-analog scenario in recent geological times. It is also argued that this situation presents a novel challenge for planetary sustainability and warrants the identification of new research priorities that can generate a holistic understanding of the complexity of the Arctic cryosphere, interactions between biotic and abiotic components, and local lifeworlds—all of which are related to the well-being of the Earth itself. Full article
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24 pages, 4948 KiB  
Article
The Evolution of Runoff Processes in the Source Region of the Yangtze River Under Future Climate Change
by Nana Zhang, Peng Jiang, Bin Yang, Changhai Tan, Wence Sun, Qin Ju, Simin Qu, Kunqi Ding, Jingjing Qin and Zhongbo Yu
Atmosphere 2025, 16(6), 640; https://doi.org/10.3390/atmos16060640 - 24 May 2025
Viewed by 382
Abstract
Climate change has intensified the melting of glaciers and permafrost in high-altitude cold regions, leading to more frequent extreme hydrological events. This has caused significant variations in the spatiotemporal distribution of meltwater runoff from the headwater cryosphere, posing a major challenge to regional [...] Read more.
Climate change has intensified the melting of glaciers and permafrost in high-altitude cold regions, leading to more frequent extreme hydrological events. This has caused significant variations in the spatiotemporal distribution of meltwater runoff from the headwater cryosphere, posing a major challenge to regional water security. In this study, the HBV hydrological model was set up and driven by CMIP6 global climate model outputs to investigate the multi-scale temporal variations of runoff under different climate change scenarios in the Tuotuo River Basin (TRB) within the source region of the Yangtze River (SRYR). The results suggest that the TRB will undergo significant warming and wetting in the future, with increasing precipitation primarily occurring from May to October and a notable rise in annual temperature. Both temperature and precipitation trends intensify under more extreme climate scenarios. Under all climate scenarios, annual runoff generally exhibits an upward trend, except under the SSP1-2.6 scenario, where a slight decline in total runoff is projected for the late 21st century (2061–2090). The increase in total runoff is primarily concentrated between May and October, driven by enhanced rainfall and meltwater contributions, while snowmelt runoff also shows an increase, but accounts for a smaller percentage of the total runoff and has a smaller impact on the total runoff. Precipitation is the primary driver of annual runoff depth changes, with temperature effects varying by scenario and period. Under high emissions, intensified warming and glacier melt amplify runoff, while low emissions show stable warming with precipitation dominating runoff changes. Full article
(This article belongs to the Section Climatology)
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13 pages, 6387 KiB  
Article
Evolution of a Potentially Dangerous Glacial Lake on the Kanchenjunga Glacier, Nepal, Predictive Flood Models, and Prospective Community Response
by Alton C. Byers, Sonam Rinzin, Elizabeth Byers and Sonam Wangchuk
Water 2025, 17(10), 1457; https://doi.org/10.3390/w17101457 - 12 May 2025
Viewed by 2090
Abstract
During a research expedition to the Kanchenjunga Conservation Area (KCA), eastern Nepal, in April–June 2024, local concern was expressed about the rapid development of meltwater ponds upon the terminus of the Kanchenjunga glacier since 2020, especially in terms of the possible formation of [...] Read more.
During a research expedition to the Kanchenjunga Conservation Area (KCA), eastern Nepal, in April–June 2024, local concern was expressed about the rapid development of meltwater ponds upon the terminus of the Kanchenjunga glacier since 2020, especially in terms of the possible formation of a large and potentially dangerous glacial lake. Our resultant study of the issue included informal interviews with local informants, comparison of time series satellite composite images acquired by Sentinel-2 Multispectral Instrument, and modeling of different lake development, outburst flood scenarios, and prospective downstream impacts. Assuming that the future glacial lake will be formed by the merging of present-day supraglacial ponds, filling the low-gradient area beneath the present-day glacier terminal complex, we estimated the potential volume of a Kanchenjunga proglacial lake to be 33 × 106 m3. Potential mass movement-triggered outburst floods would travel downstream distances of almost 120 km even under the small magnitude scenario, and under the worst-case scenario would reach the Indo-Gangetic Plain and cross the border into India, exposing up to 90 buildings and 44 bridges. In response, we suggest that the lower Kanchenjunga glacier region be regularly monitored by both local communities and Kathmandu-based research entities over the next decade. The development of user-friendly early warning systems, hazard mapping and zoning programs, cryospheric hazards awareness building programs, and construction of locally appropriate flood mitigation measures are recommended. Finally, the continued development and refinement of the models presented here could provide governments and remote communities with a set of inexpensive and reliable tools capable of providing the basic information needed for communities to make informed decisions regarding hazard mitigation, adaptive, and/or preventive measures related to changing glaciers. Full article
(This article belongs to the Special Issue Study of Hydrological Mechanisms: Floods and Landslides)
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20 pages, 11450 KiB  
Article
Glacier Recession and Climate Change in Chitral, Eastern Hindu Kush Mountains of Pakistan, Between 1992 and 2022
by Zahir Ahmad, Farhana Altaf, Ulrich Kamp, Fazlur Rahman and Sher Muhammad Malik
Geosciences 2025, 15(5), 167; https://doi.org/10.3390/geosciences15050167 - 7 May 2025
Viewed by 1238
Abstract
Mountain regions are particularly sensitive and vulnerable to the impacts of climate change. Over the past three decades, mountain temperatures have risen significantly faster than those in lowland areas. The Hindu Kush–Karakoram–Himalaya region, often referred to as the “water tower of Asia”, is [...] Read more.
Mountain regions are particularly sensitive and vulnerable to the impacts of climate change. Over the past three decades, mountain temperatures have risen significantly faster than those in lowland areas. The Hindu Kush–Karakoram–Himalaya region, often referred to as the “water tower of Asia”, is the largest freshwater source outside the polar regions. However, it is currently undergoing cryospheric degradation as a result of climatic change. In this study, the Normalized Difference Glacier Index (NDGI) was calculated using Landsat and Sentinel satellite images. The results revealed that glaciers in Chitral, located in the Eastern Hindu Kush Mountains of Pakistan, lost 816 km2 (31%) of their total area between 1992 and 2022. On average, 27 km2 of glacier area was lost annually, with recession accelerating between 1997 and 2002 and again after 2007. Satellite analyses also indicated a significant increase in both maximum (+7.3 °C) and minimum (+3.6 °C) land surface temperatures between 1992 and 2022. Climate data analyses using the Mann–Kendall test, Theil–Sen Slope method, and the Autoregressive Integrated Moving Average (ARIMA) model showed a clear increase in air temperatures from 1967 to 2022, particularly during the summer months (June, July, and August). This warming trend is expected to continue until at least 2042. Over the same period, annual precipitation decreased, primarily due to reduced snowfall in winter. However, rainfall may have slightly increased during the summer months, further accelerating glacial melting. Additionally, the snowmelt season began consistently earlier. While initial glacier melting may temporarily boost water resources, it also poses risks to communities and economies, particularly through more frequent and larger floods. Over time, the remaining smaller glaciers will contribute only a fraction of the former runoff, leading to potential water stress. As such, monitoring glaciers, climate change, and runoff patterns is critical for sustainable water management and strengthening resilience in the region. Full article
(This article belongs to the Section Cryosphere)
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