TerraMosaic Daily Digest: Feb 13, 2026
Daily Summary
This digest highlights 179 selected papers with an emphasis on how extreme rainfall and coupled hydro‑mechanical processes translate into clustered slope failures and cascading hazards. Detailed case studies resolve progressive saturation and suction loss, lithologic controls, and the emergence of landslide–debris‑flow chains, while seismic and LiDAR analyses increasingly reconstruct multi‑episode failures with time-resolved kinematics.
Across modeling and engineering studies, the field is moving from static mapping toward decision-ready workflows: sampling-aware susceptibility assessment, decorrelation‑resilient deformation monitoring, and probabilistic fragility or early‑warning frameworks for tunnels, dams, and buildings. Mitigation papers converge on pathway-focused interventions—drainage–support coupling, recycled and bio‑based reinforcements, and targeted protective designs—that directly manage pore-pressure and deformation evolution.
Key Trends
- Hydro‑mechanical state variables are replacing rainfall totals as the organizing lens: Multiple studies show failure is governed by time‑dependent saturation, matric suction, and drainage performance—sometimes without positive pore‑pressure—highlighting why thresholds must be process‑anchored rather than purely empirical.
- Event reconstruction is becoming multi-sensor and time-resolved: Broadband seismic signals, high‑resolution LiDAR, and targeted field monitoring are now combined to recover rupture timing, multi‑stage kinematics, and runout evolution—closing the gap between post‑event mapping and near‑real‑time understanding.
- Susceptibility assessment is being actively debiased: Work on non‑landslide sampling, clustering-based filtering, and reduced subjectivity points to a shift from “better algorithms” to “better evidence,” improving model stability and interpretability across heterogeneous terrains.
- Monitoring pipelines are designed for challenging conditions and operational cadence: Advances in InSAR time‑series strategies and in‑situ IoT sensing emphasize robustness to decorrelation and delivery of actionable metrics (e.g., automated factor of safety or deformation updates) rather than static deformation snapshots.
- Mitigation is trending toward hybrid, low-cost, and mechanism-targeted designs: From drainage–support coordination in reactivated slides to combined recycled material–vegetation systems and site‑specific rockfall protection, interventions increasingly target the governing failure pathway instead of generic stabilization.
Selected Papers
This digest features 179 selected papers from 750 deduplicated papers analyzed (out of 2255 raw papers scanned). Each paper has been evaluated for its relevance to landslide and broader geohazard research and includes links to the original publications.
1. Characteristics and initiation mechanism of clustered landslides triggered by an extreme rainfall in Wuping County, Fujian Province, China
Core Problem: Understanding the characteristics, spatial distribution, and specific initiation mechanisms of a large cluster of shallow landslides triggered by an extreme rainfall event, and how lithology influences their morphology and cascading hazards.
Key Innovation: Comprehensive investigation combining remote sensing, field surveys, hydrological monitoring, and numerical simulations to reveal that progressive saturation reduced matric suction and shear strength (without positive pore-water pressure) causing failure at the residual/weathered layer interface, and identifying lithological controls on landslide morphology and the frequency of landslide-debris flow hazard chains.
2. The catastrophic 2025 Junlian landslide in Sichuan, China: insights revealed by seismic signals
Core Problem: Lack of direct observations during the failure of high-speed, long-runout landslides limits understanding of their dynamic evolution.
Key Innovation: Integrated broadband seismic records with high-resolution airborne LiDAR data to reconstruct the complete dynamics of a catastrophic landslide, revealing two major failure episodes and four kinematic stages, demonstrating the potential of seismic observations for near-real-time quantitative resolution of landslide dynamics.
3. Formation mechanism and stability assessment of the Attabad landslide-dammed lake along the China-Pakistan Karakoram Highway using remote sensing technology
Core Problem: Investigating the formation mechanism and stability of the Attabad landslide-dammed lake, which poses risks to infrastructure and requires prevention/mitigation strategies.
Key Innovation: Utilized multi-temporal high-resolution satellite imagery to analyze the formation mechanism based on geological conditions and causative factors, assessed the stability of the landslide-dammed lake by tracking deformation and lake area changes, and identified new geological hazard warning zones and the potential for successive landslides.
4. Landslide susceptibility assessment under non-landslide sampling strategy based on a clustering algorithm
Core Problem: Improving the spatial representativeness of non-landslide samples remains a major challenge for enhancing the stability and predictive accuracy of landslide susceptibility assessment (LSA) models.
Key Innovation: Development of a novel non-landslide sampling strategy using a density-based spatial clustering of applications with noise (DBSCAN) algorithm to eliminate spatially anomalous samples, combined with the synthetic minority oversampling technique (SMOTE) for dataset balancing. This strategy significantly improves LSA model accuracy and robustness, with AUC values for SVM and LR models improving by 46.98% and 35.71% respectively.
5. Analysis of reactivation mechanisms and deformation patterns in a colluvial landslide: the role of drainage and supporting structures
Core Problem: Understanding the complex reactivation mechanisms and deformation evolution of colluvial landslides, particularly when influenced by intense rainfall, drainage system failure, and the performance of supporting structures, is essential for effective mitigation.
Key Innovation: Detailed investigation of a reactivated colluvial landslide using field data, monitoring, and numerical simulation. It identifies two interconnected cyclic reactivation mechanisms: rainfall-induced strength reduction and groundwater accumulation behind anti-slide piles due to drainage blockage. The study proposes an integrated 'drainage-supporting coupling' strategy for effective mitigation, emphasizing spatiotemporal coordination.
6. Evaluation of protective measures against rockfall hazard with 3D analysis (Mardin Castle, Türkiye)
Core Problem: The need to evaluate rockfall hazard and design effective protective measures for a crowded area near Mardin Castle, which has experienced recent damaging rockfall events.
Key Innovation: Performed a comprehensive 3D rockfall analysis combining field work, UAV imagery, and numerical modeling to evaluate rockfall hazard and propose reinforced embankments, assessing their performance for protection.
7. Optimization of various machine learning concepts to evaluate landslide susceptibility: XGBoost, k-NN and MLP using PSO algorithm
Core Problem: The urgent need to improve the performance of machine learning algorithms for accurate landslide susceptibility prediction and effective hazard mitigation.
Key Innovation: Evaluated and optimized kNN, MLP, and XGBoost models using Particle Swarm Optimization (PSO) for landslide susceptibility mapping, demonstrating that PSO-XGBoost achieves superior predictive capability and robustness in handling complex spatial patterns.
8. Reducing subjectivity in landslide susceptibility mapping method: an integrated methodology
Core Problem: Traditional landslide susceptibility assessment methods, such as the Slope Stability Susceptibility Evaluation Parameter (SSEP), are often constrained by subjective judgments, leading to less objective and reliable mapping.
Key Innovation: Developed and validated a novel integrated approach combining the SSEP framework with Geographic Information System (GIS)-based bi-variate statistical analysis to enhance objectivity and reliability in landslide susceptibility mapping, achieving high predictive accuracy.
9. Fragility assessment of shield tunnels under water and soil gushing hazards
Core Problem: The frequent occurrence of water–soil gushing poses severe risks to the safety of shield tunnels, necessitating a detailed probabilistic fragility analysis to understand tunnel responses and collapse risks under such hazards.
Key Innovation: Proposed a machine learning-based probabilistic fragility assessment framework for shield tunnels under water–soil gushing hazards, integrating MPM-FEM coupling models, Latin Hypercube Sampling for uncertainty, and a Multi-Layer Perceptron model to predict tunnel responses and generate fragility curves.
10. Stability of Slopes Reinforced with a Combined Waste Tyre and Vetiver System
Core Problem: Inadequate stabilization of landslide-prone areas in Indonesia due to insufficient funding and increasing landslide frequency, necessitating alternative and sustainable reinforcement materials.
Key Innovation: Proposed and tested a combined waste tyre and vetiver (WTV) system for slope stabilization through full-scale field models and numerical simulations, demonstrating that WTV significantly increases slope stability, reduces deformation, and maintains lower pore water pressure during rainfall compared to waste tyre-only systems, due to increased soil cohesion from vetiver roots.
11. Investigation of post-rainfall dynamic behavior of anti-dip layered rock slope through shaking table tests
Core Problem: Understanding the complex, progressive failure mechanisms of anti-dip layered rock slopes subjected to coupled rainfall and seismic loading, and identifying reliable instability indicators.
Key Innovation: Performed large-scale shaking table tests to reveal a three-stage failure process (rainfall weakening, moderate shaking cracking, strong shaking collapse/toppling) and identified key instability indicators: acceleration amplification, localized pore-pressure build-up, and spectral softening.
12. A hybrid SSA-CNN-SVM model for seismic-induced sand liquefaction discrimination
Core Problem: Accurate and rational discrimination and prediction of seismic-induced sand liquefaction states are crucial for geohazard mitigation, but existing models may lack optimal performance.
Key Innovation: Proposes a novel hybrid SSA-CNN-SVM model that integrates Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Networks (CNN) with Support Vector Machines (SVM) for superior performance in seismic-induced sand liquefaction discrimination, achieving 88.33% accuracy.
13. Seismoacoustic Analysis of Possible Vent‐Drying Sequence During Phreatomagmatic Activity on 13 July 2021 at Semisopochnoi Island, Alaska
Core Problem: Interpreting the source processes of pulse-like infrasound during phreatomagmatic volcanic activity, particularly the interaction with water and the influence of topography on signals, is challenging for volcano monitoring.
Key Innovation: By analyzing temporal variations in pulse-like infrasound, seismoacoustic amplitudes, and spectral characteristics, this study suggests a vent-drying sequence during phreatomagmatic activity. Numerical simulations also highlight the significant topographic effect on infrasound waveforms, improving the interpretation of volcanic sounds and monitoring.
14. SBAS-InSAR for sustainable development goals (SDGs)—the impact of extreme precipitation on the foothill's stability of the South Taihang Mountains, China
Core Problem: Understanding the mechanisms of surface deformation and geological safety challenges in foothill regions induced by extreme rainfall events, particularly in the context of sustainable development goals.
Key Innovation: Application of SBAS-InSAR to monitor and analyze surface deformation in response to extreme precipitation, linking it to regional geological safety and sustainable development goals.
15. Failure mechanism and damage assessment of talus slopes with varied gradation under seismic loading
Core Problem: Understanding the seismic stability and failure mechanisms of talus slopes with varying particle gradation, and developing a damage assessment method for these slopes.
Key Innovation: Identified two distinct failure modes (shallow and large-scale) and a consistent three-stage failure process for large-scale failures. Demonstrated how particle gradation fundamentally controls sliding through mesostructural evolution and proposed a seismic damage assessment method considering actual seismic loading, slope scale, and material composition.
16. Preliminary investigation of rainfall-induced landslides and related damages by the 2025 extreme monsoon in the Northwestern Himalayan region
Core Problem: Understanding the factors contributing to slope instability and infrastructure damage caused by extreme monsoonal rainfall in the Northwestern Himalayan region.
Key Innovation: Systematically discussed the combined effect of extreme rainfall and human-induced factors/infrastructure expansion on fragile slopes, turning natural phenomena into large-scale disasters, based on multi-source data and field investigation.
17. Landslide distribution controlled by faults: A case study in Nam Pam commune, Northwest Vietnam
Core Problem: Unclear causes of complex geomorphic characteristics and landslide occurrences in the Nam Pam commune, specifically the connection between landslides and faults.
Key Innovation: Investigated the connection between landslides and faults using DEM-derived geomorphology, field observations, geophysical profiles, and soil mineralogy, identifying two main fault systems and demonstrating their control on landslide distribution through triangular facets, distance to faults, fault density, and the presence of weak minerals.
18. Structural material evaluation of 400 collapsed RC buildings after February 6, 2023 Kahramanmaraş double earthquakes in Türkiye
Core Problem: The widespread collapse of reinforced concrete (RC) buildings during the Kahramanmaraş earthquakes highlighted critical deficiencies in building material quality and compliance with earthquake codes.
Key Innovation: Evaluated structural material quality and code compliance in 400 collapsed RC buildings post-earthquake through field observations and laboratory studies, identifying factors like low material quality, poor workmanship, and insufficient compaction as key contributors to collapse, informing strategies for improved building resilience.
19. Strategic master plan for coastal city risk mitigation: a case study of Kuala Nerus and Kuala Terengganu, Malaysia
Core Problem: Coastal regions worldwide, including Kuala Terengganu and Kuala Nerus (KTKN), face heightened vulnerability to multiple natural hazards like erosion, flooding, and sea level rise, necessitating comprehensive multi-hazard assessment for effective spatial planning.
Key Innovation: Developed a comprehensive multi-hazard assessment and land-use suitability framework for a Coastal City Spatial Master Plan (CCSMP) in KTKN, integrating Deep Learning models, coastal hazard models, Delphi-AHP, and GIS to generate Multi-Hazard Numerical Spatial Data (MHNSD) for resilient urban planning.
20. Machine learning prediction of post-earthquake debris flows incorporating hydrological variables
Core Problem: Most existing post-earthquake debris flow prediction models rely primarily on rainfall data, leading to frequent false positives and neglecting the complex nonlinear relationships involving hydrological conditions.
Key Innovation: Enhanced post-earthquake debris flow prediction accuracy by incorporating hydrological variables (soil water content, peak discharge) into machine learning models (Random Forest outperformed Logistic Regression), demonstrating their critical importance over conventional rainfall features.
21. Paleolandslides, paleoshorelines and lacustrine sediment deformation structures in the midstream Tashkurgan River, Chinese Pamir and implications on regional seismic risk
Core Problem: Understanding the regional landscape evolution, paleoclimatic/neotectonic conditions, and paleoseismicity in the Tashkurgan region is crucial for assessing current seismic and landslide risk to critical infrastructure.
Key Innovation: Identified and dated three large paleolandslides and analyzed seismic-origin soft-sediment deformation structures in lacustrine sediments along the Tashkurgan River, providing critical constraints for regional seismic and landslide risk assessment, linking specific paleolandslides to fault activity.
22. UAV-SfM coupled PBD simulation of graded rockfall fragmentation and air resistance: validation at Bijiashan, Chongqing
Core Problem: Existing numerical simulations of rockfalls often rely on idealized assumptions that fail to account for real-world phenomena like collision-induced fragmentation and air resistance, compromising simulation reliability and prediction accuracy.
Key Innovation: Development of a high-precision rockfall dynamics simulation method using Unreal Engine 5, integrating UAV-SfM data and a position-based dynamics (PBD) algorithm with a graded fragmentation mechanism. This approach effectively simulates the complete dynamic behavior of unstable rock masses, including fragmentation and air resistance, providing accurate 3D coordinates, velocity, kinetic energy, and movement trajectories, validated against historical deposition patterns.
23. Deformation and failure mechanisms of shallow landslides in the Qinghai-Tibet Plateau, China: a case study of Zhaxuecun landslide
Core Problem: Understanding the distribution, deformation, and failure mechanisms of widespread shallow landslides in the Qinghai-Tibet Plateau, which are often triggered by human activities and environmental factors, is critical for hazard mitigation.
Key Innovation: A comprehensive case study of the Zhaxuecun landslide integrating field investigations, in-situ tests, laboratory experiments, and numerical simulations. It identifies the combined influence of highway excavation, freeze-thaw cycles, and snowmelt, detailing the degradation of mechanical properties under saturated conditions, and proposes a four-stage evolution model for shallow landslides in the region.
24. A root pullout failure-based shear strength model for root reinforced loess
Core Problem: Existing shear strength models for root-reinforced soil, primarily based on the Wu model, assume root tension failure, leading to overestimation of shear strength because roots often fail by pullout during shearing.
Key Innovation: Proposed a modified shear strength prediction model based on root pullout failure, incorporating root-soil friction and soil suction, which demonstrated superior accuracy, particularly for larger-diameter roots, compared to existing models.
25. Influence of spheroidal and blade-shaped coarse particles on the shear evolution and spatial deformation of sliding zone soil
Core Problem: Limited understanding of the shear spatial deformation of Coarse Grained-Sliding Zone (CG-SZ) soil and a weak correlation between experimental and simulation results, despite its substantial impact on landslide deformation.
Key Innovation: Investigated the influence of spheroidal and blade-shaped coarse particles on the shear evolution and spatial deformation of CG-SZ soil using PIV and DEM, revealing differences in shear band thickness and interlocking effects, and classifying the shearing process into four distinct stages.
26. Formation mechanisms and modes of karst collapse columns based on multi-information exploration technology: a case study of Fengpei coalfield, North China
Core Problem: Karst collapse columns (KCCs) are widely developed geological structures in North China Coalfields and are primary factors contributing to mine water inrush hazards, requiring better understanding of their formation mechanisms and effective treatment methods.
Key Innovation: Established a multi-information exploration technology to characterize KCCs, proposed four formation modes (Karst strong runoff zone, Fold structure, Fault structure, High-temperature corrosive water control), and developed a low-disturbance grouting treatment method to effectively eliminate their impact on coal seam mining.
27. Influence of topographic factors on the distribution of herbaceous vegetation and mechanical root properties of the Xijitan giant landslide in the upper reaches of the Yellow River, northwestern China
Core Problem: The ecosystem characteristics of giant landslide areas in the upper Yellow River are poorly understood, particularly how locally dominant herbaceous species and their root mechanical properties influence the shallow stability of these landslides under varying topographic factors.
Key Innovation: Demonstrated a clear link between slope aspect, herbaceous community distribution, and the mechanical properties of plant roots on a giant landslide, showing that habitat differences caused by slope aspect drive changes in root mechanical behavior, providing theoretical support for ecological restoration and shallow landslide risk mitigation.
28. A New Stress–Brittleness–Energy Index for Estimating the Rockburst Tendency in Deep Tunnel Considering Pre- and Post-failure Stages of Rock Mass
Core Problem: Current rockburst tendency assessment indexes neglect either the pre-failure or post-failure stages of rock mass, leading to incomplete evaluations, especially under true triaxial stress conditions in deep tunnels.
Key Innovation: Proposed a new Stress–Brittleness–Energy (SBE) index that integrates in situ stress and both pre- and post-failure rock mass characteristics under true triaxial conditions, validated at engineering and laboratory scales, providing a reliable tool for predicting rockburst tendency and guiding tunnel support design.
29. Experimental Investigation of the Influence of Adjacent Joint Planes on Spalling Failure and the Fracture Process in a D-Shaped Tunnel
Core Problem: Understanding the joint-induced failure mechanism, spalling, and rockburst events in deep tunnels, particularly the influence of joint inclination and lateral stress, which present critical challenges in underground engineering.
Key Innovation: Systematically revealing the influence of jointed inclination and lateral stress on tunnel failure behavior, categorizing the spalling process into four distinct stages, demonstrating how jointed cracks induce earlier but less severe failure on one sidewall while delaying and intensifying failure on the intact sidewall, and quantifying fracture mechanisms using AE and DIC.
30. Real-Time AE − DIC Analysis and DEM Modeling of Structure-Controlled Rockburst Evolution in Hard Rock Tunnels
Core Problem: Understanding the governing mechanisms of structure-controlled rockbursts in deep hard-rock tunnels to improve prediction and mitigation strategies.
Key Innovation: Integrated real-time acoustic emission–digital image correlation (AE–DIC) physical model tests with discrete element method (DEM) numerical simulations (using LERR and FAI) to analyze multi-scale mechanical responses, crack evolution, energy dissipation, and principal-stress reorientation, providing a theoretical basis for predicting and mitigating rockbursts.
31. Osmotically Driven Solute Transport in Tailings Dams: Modeling and Application to the Sarcheshmeh Tailings Facility, Iran
Core Problem: Existing hydraulic models for tailings dams fail to account for the significant influence of dissolved salts (osmotic effects) on soil hydraulic properties, leading to underestimation of contaminant transport rates and leakage risks.
Key Innovation: Developed and applied a numerical model that embeds osmotic-dependent fluid-retention and unsaturated hydraulic conductivity models to enhance fluid flow predictions in tailings dams. Demonstrated that dissolved ions significantly increase contaminant transport rates (e.g., 17-30%) due to enhanced hydraulic conductivity, and showed the effectiveness of GCLs in reducing transport, providing a more accurate assessment of leakage risk.
32. Experimental and numerical study on real-time early warning of secondary collapse in semi-ruined RC frame structures under aftershocks
Core Problem: Semi-ruined RC frame structures are highly vulnerable to secondary collapse during aftershocks, posing severe risks, but a reliable real-time early-warning framework to assess this risk and guide emergency response is lacking.
Key Innovation: Developed an integrated early-warning framework combining scaled shaking table experiments, high-fidelity FEM–FDEM simulations, and a novel dynamic indicator, Cumulative Absolute Vertical Velocity (CAVV), to predict secondary collapse in semi-ruined RC structures under aftershocks. Established an effective CAVV threshold and a real-time decision algorithm with adaptive threshold updating, providing a scientific basis for post-earthquake emergency response.
33. Development of magnitude estimation relations from P-wave onset parameters for earthquake early warning applications in Iran
Core Problem: Reliable and regionally calibrated magnitude estimation is essential for effective earthquake early warning systems, particularly in regions like Iran, requiring robust relations based on P-wave onset parameters.
Key Innovation: Develops and validates new empirical magnitude estimation relations for Iran based on P-wave onset parameters (frequency, amplitude, energy), identifying amplitude- and energy-related parameters (especially Pd) as providing more stable and accurate performance for earthquake early warning.
34. A review of ground motion correlation modelling for regional seismic risk analysis
Core Problem: Ignoring inter and intra-site correlations of ground-motion intensity measures can lead to significant misrepresentation of losses and ground motion fields in regional seismic hazard and risk assessments.
Key Innovation: Provides a detailed review of over 45 ground-motion correlation models developed over two decades, summarizing the current state of modelling and offering guidance on model selection to improve the accuracy of hazard and risk assessments in spatially distributed systems.
35. Assessing the Transferability of Post-Disaster Building Damage Assessment using Synthetic Aperture Radar and Machine Learning
Core Problem: SAR ML models for post-disaster building damage assessment need to generalize to new disaster events and differing contexts for practical disaster response, but their transferability with medium-resolution SAR has been untested.
Key Innovation: Evaluating the transferability of medium-resolution SAR ML models (using Sentinel-1 coherence and intensity) for mapping damage across 11 diverse disaster events, revealing substantial performance decrease under global transferability but better performance for disaster-type specific models (e.g., fire-specific).
36. Short-term impact of 2016 floods on the demographic change and housing prices in the state of Louisiana
Core Problem: Understanding the short-term shifts in racial composition, household income, and housing prices following major flood events to inform disaster recovery and housing policy.
Key Innovation: Providing a short-term analysis of demographic and housing price shifts after the 2016 Louisiana floods, quantifying immediate changes using a novel population monitoring framework, and empirically linking flood-induced demographic displacement to housing price changes.
37. Hazard and vulnerability analysis of NaTech disasters induced by hydrological events to support probabilistic safety assessment in natural gas pipelines
Core Problem: A critical gap exists in accounting for the complex interplay between hydrological conditions (flooding) and inherent uncertainties in natural gas pipeline integrity, which can lead to NaTech disasters.
Key Innovation: A systematic probabilistic approach to analyze the hazards and vulnerability of flood-NaTech events in natural gas pipelines, integrating hydrological modeling with technical pipeline analysis and structuring uncertainty scenarios to support probabilistic safety assessment.
38. Flood monitoring: An innovative application of multisource image fusion and transfer learning
Core Problem: The increasing frequency of extreme weather events necessitates advanced methods for flood disaster monitoring, but challenges exist in robust water body mapping using multi-temporal optical imagery due to radiometric discrepancies and temporal drift.
Key Innovation: Proposed a cross-sensor framework for robust water body mapping using multi-temporal optical imagery, integrating relative radiometric normalization, spatiotemporally invariant feature extraction, a PSO-RF classifier, and cross-sensor sample transfer strategies, achieving significant classification accuracy improvements for flood monitoring.
39. Instrumented unsaturated slope monitoring with IoT-based factor of safety calculations
Core Problem: There is a need for real-time, high-resolution, and cost-effective monitoring and assessment of slope stability, especially under varying climatic conditions, to enable proactive landslide risk management.
Key Innovation: Implemented an IoT-based warning system for slope safety monitoring that combines advanced moisture sensors and osmotic tensiometers with cloud-integrated data for real-time Factor of Safety (FoS) assessment. Developed a linear regression model for continuous, automated FoS estimation from in situ moisture data, demonstrating a scalable framework for proactive slope stability assessment.
40. Salinity Variations in the Venice Lagoon (Italy) Induced by Safeguard Structures: A Challenging Trade‐Off Between Urban and Ecosystem Protection in the Face of Climate Change
Core Problem: The complex and often unintended ecological and environmental consequences arising from the combined operation of multiple hard-engineering flood defense solutions (spillways and floodgates) within the same estuary, particularly in the context of climate change and sea-level rise, posing a trade-off between urban and ecosystem protection.
Key Innovation: Using numerical modeling informed by field data to evaluate the combined impact of a river spillway and floodgate system on Venice Lagoon hydrodynamics and salinity, showing that floodgate closures reduce salinity by limiting tidal propagation and increasing freshwater inflow, with future sea-level rise predicting more frequent and longer closures, further widening areas affected by salinity changes.
41. Collapse–subsidence retrogressive thaw slumps in degrading permafrost: multi-source insights from the Northeastern Qinghai–Tibet Plateau
Core Problem: Poor characterization of Collapse–subsidence Retrogressive Thaw Slumps (CS-RTSs) which pose risks to ecosystems and infrastructure in degrading permafrost regions.
Key Innovation: Examined CS-RTSs using combined drilling, field monitoring, geophysical imaging, CT scanning, and thermal analysis, demonstrating how thermo–gravitational coupling and shallow ice-rich layers control slump initiation, and providing a multi-scale framework for hazard evaluation and infrastructure planning in permafrost regions.
42. “I can’t think about nature right now”: perceptions of territorial fragility and the acceptance of nature-based solutions in the Simeto Valley (Italy): a participatory approach
Core Problem: Marginal regions face interdependent environmental fragilities (including landslides, floods, and land degradation) and socio-economic decline, with local communities often lacking awareness and acceptance of Nature-Based Solutions (NBS) as viable responses.
Key Innovation: Integrated participatory action research, GIS mapping, and qualitative data to understand community perceptions of environmental fragilities and the acceptance of NBS in the Simeto Valley, highlighting the need for targeted educational and participatory initiatives to promote resilient and sustainable approaches.
43. Meteotsunamis in the Western mediterranean: a regional analysis from high-frequency sea level observations
Core Problem: Meteotsunamis pose a significant hazard (flooding, infrastructure damage) in the Western Mediterranean, but a comprehensive regional characterization beyond known hotspots is lacking.
Key Innovation: Conducted the first regional characterization of meteotsunamis in the Western Mediterranean using high-resolution tide gauge data, revealing spatial heterogeneity in impact, identifying new hotspots, and linking events to synoptic-scale atmospheric patterns and local coastal topography.
44. Increasing compound drought and heatwave events in the Horn of Africa and its effect on agriculture
Core Problem: Compound Drought-Heatwave Events (CDHEs) are increasing in the Horn of Africa (HOA), posing severe risks to water, agriculture, and ecosystems, with significant impacts on food security.
Key Innovation: Analyzed spatiotemporal patterns and agricultural impacts of CDHEs (2000–2022) in the HOA using satellite remote sensing and meteorological indices, quantifying CDHE-impacted areas and linking exposure to significant reductions in harvested area and crop production, highlighting the need for region-specific adaptation strategies.
45. A comparative assessment of data-driven flood susceptibility mapping in Nigeria
Core Problem: Increased flood risk in Nigeria necessitates accurate and high-resolution data for effective disaster risk management, particularly using open-access and scalable methods.
Key Innovation: Developed a nationwide, 30m flood susceptibility map for Nigeria using open-access data, comparing four DEMs, four hydrological methods, and three machine learning models, and generating an ensemble map validated against a catastrophic flood event.
46. Advanced Geospatial Modeling of Highly Variable Geotechnical Data for Infrastructure Resilience
Core Problem: Subsurface heterogeneity poses a significant challenge for geotechnical experts in hazard-prone regions, requiring accurate and robust geospatial modeling of geotechnical data for infrastructure resilience.
Key Innovation: Developed geotechnical soil maps (GSMs) using an improved modified Shepard-based Inverse Distance Weighting (IDW) method on Google Earth Engine, demonstrating superior accuracy and smoother transitions compared to traditional Kriging, thereby enhancing hazard mitigation and infrastructure resilience.
47. Shear strength and microstructural characteristics of loose sandy soil reinforced with vetiver grass roots and improved by microbially induced calcite precipitation
Core Problem: There is a need for sustainable and low-carbon alternatives to conventional cement-based stabilization for enhancing the shear strength of loose sandy soil, particularly for applications like slope protection and riverbank reinforcement.
Key Innovation: A combined approach using vetiver grass root reinforcement and microbially induced calcite precipitation (MICP) significantly increases the shear strength and cohesion of loose sandy soil. Microscopic observations confirm dense and uniform calcium carbonate distribution, effectively filling voids and bonding particles, demonstrating a synergistic effect for bio-mediated stabilization.
48. Internal erosion on coarse alluvial sediments: a case study of bar dam
Core Problem: Understanding the causes of sinkholes, surface deformation, and concentrated leakage observed during the impounding of the Bar Dam, which are attributed to internal erosion and suffusion in coarse alluvial sediments, is crucial for dam safety.
Key Innovation: Integration of in-situ investigation, theoretical criteria, and large-scale suffusion experiments to demonstrate that Bar Dam foundation materials are susceptible to suffusion-induced deformation and sinkhole formation. It quantifies the influence of hydraulic gradient, particle size distribution, and relative compaction on fine particle migration and internal erosion, highlighting the need for protective schemes.
49. Modeling Dynamic Fracturing Behavior of Water-Resisting Rock of Tunnel Under Seismicity Based on a Coupled Hydro-Mechanical Discrete Element Method
Core Problem: The underemphasized rupture behavior of water-resisting rock in tunnels under the coupled effect of seismicity, hydraulic pressure, and geostress, which leads to water and mud inrush hazards.
Key Innovation: Developed a coupled hydro-mechanical discrete element method model to simulate and analyze the dynamic fracturing behavior of water-resisting rock under seismicity, revealing the profound influence of seismic loading level, frequency, and inclination on crack propagation, macrofailure planes, and fractal features.
50. Effect of Saturation State on Mudstone Mechanical Properties and the Fracture Zone Formation Around Mudstone Roadways
Core Problem: The impact of water distribution uniformity and temporal evolution on mudstone mechanical behavior and the formation of fractured zones around roadways is often ignored or underestimated in laboratory tests and related research.
Key Innovation: Demonstrating that water-distribution uniformity significantly impacts the degradation trend of mudstone mechanical parameters, leading to deviations in assessing fractured-zone radius around roadways, and providing new insights into water uniformity's impact on mudstone's mechanical response and near-field rock stability.
51. Permeability Evolution and Water Inrush Potential Fine Assessment of Fault Zone Under Mining Disturbance
Core Problem: Accurately assessing the dynamic water inrush potential of fault zones under mining disturbance, which is a major cause of underground engineering disasters.
Key Innovation: Developed an optimized Dynamic Variable Weight Cloud Model (DVWCM) by introducing an exponential variable weight function and cloud model theory to quantitatively and finely evaluate water inrush potential, showing 35.7% higher accuracy than constant weight models.
52. Seismic Site Classification, Stratigraphy Assessment and SPT-Vs Correlation for Madhubani and Adjoining Regions of Bihar Based on In-Situ Geotechnical and MASW Investigations
Core Problem: Lack of detailed local soil characterization, seismic site classification, and regional SPT-Vs correlations for the Madhubani region of Bihar, hindering accurate assessment of seismic hazard and potential for building damage and liquefaction.
Key Innovation: Conducted extensive in-situ geotechnical (boreholes) and geophysical (MASW) investigations to develop detailed subsoil stratification, a seismic site classification map, and the first regional SPT N value and shear wave velocity (Vs) correlation for the Madhubani region, enabling improved surface seismic hazard assessment.
53. In Situ Three-Dimensional Stress Determination in an Overpressured Fault Zone Using Borehole Measurements
Core Problem: Accurately determining in situ three-dimensional stresses and pore pressure variations within deep, overpressured fault zones, especially how fault zone geomechanical properties influence local stress states and slip tendency, has been challenging, often relying on numerical models or indirect interpretations.
Key Innovation: Employed an integrated field approach (DFIT, borehole imaging, acoustic logging) to directly characterize in situ stress magnitudes, orientations, and pore pressure variations within a deep fault zone. Demonstrated a marked shear stress drop, maximum horizontal stress rotation, and pore pressure dissipation within the damage zone, showing how these conditions reduce fault slip tendency and improving upon prior model-based understandings.
54. Impact of ground motion model selection on the benefit from monitoring data
Core Problem: Quantifying the benefits of monitoring data for seismic emergency management and making informed decisions is complex due to high uncertainties in seismic demand and structural capacity, and the selection of appropriate ground motion models significantly impacts the accuracy of damage probability estimations.
Key Innovation: Assessed the impact of ground motion model selection on the benefit derived from monitoring data for seismic emergency management of a bridge, utilizing information theory metrics (value of information and entropy). Demonstrated how different ground motion models influence the quantification of monitoring outcomes, thereby improving the understanding of decision-making under seismic uncertainty.
55. Building Community Resilience: Development and Validation of a School-Based Framework for Post-Disaster Child Mental Health Support
Core Problem: Disasters significantly impact children's mental health, but professional services are limited, and systematic evidence on the implementation and effectiveness of non-professional psychosocial interventions (e.g., by teachers) is lacking.
Key Innovation: Conducting a systematic evidence synthesis to develop and validate the School-Based Community Resilience Implementation Model (SB-CRIM), an evidence-based framework for integrating psychosocial support into educational systems and disaster preparedness, addressing implementation barriers.
56. Optimization of isolation valve operation and identification of critical components for enhancing the resilience of water distribution systems
Core Problem: Enhancing the resilience of water distribution systems (WDSs) by optimizing isolation valve operations and identifying critical components to minimize service performance losses during pipe failures.
Key Innovation: Proposing a framework that converts WDS topology into a segment-valve model, establishes quantitative metrics for hydraulic and water quality losses, develops an optimization model for isolation valve closure (IVC) schemes, and introduces a criticality assessment framework to identify key segments and valves, demonstrating significant reduction in performance loss and enhanced resilience.
57. Coastal changes in the low-latitude western North Pacific region
Core Problem: The need to understand the complex responses of coastal landforms (deltas, estuaries, barrier-lagoon systems) in the low-latitude western North Pacific to Holocene environmental changes, particularly sea-level fluctuations and sediment supply, to predict future coastal evolution.
Key Innovation: A comprehensive analysis of regional sea-level change, monsoon rainfall, and geomorphologic records, revealing distinct phases of coastal evolution (rapid sea-level rise, reduced rise, near-zero accommodation, seaward progradation) and predicting that future rapid sea-level rise may halt land growth and cause coastal retrogradation.
58. Dynamic inheritance-enhanced TomoSAR imaging with dual-task deep learning for urban applications
Core Problem: Traditional TomoSAR methods are inefficient for rapid, long-term urban infrastructure deformation monitoring due to repetitive computations and complex inversion algorithms.
Key Innovation: Proposes a dynamic inheritance-enhanced TomoSAR Imaging method using a dual-task deep learning model (DT-TomoSARNet) to efficiently screen and inherit historical scatterer parameters, significantly reducing processing time while maintaining high accuracy for urban deformation monitoring.
59. Vibration mitigation for underground tunnels using jet-grouted barriers subjected to impact pile driving
Core Problem: Impact pile driving generates ground vibrations that can damage nearby underground tunnels, and conventional mitigation measures have limitations in performance and risk.
Key Innovation: Investigated the use of jet-grouted (JG) barriers for vibration mitigation, demonstrating its effectiveness in reducing vibrations transmitted to tunnels, with performance comparable to or exceeding other barriers when optimized.
60. Rainfall and snowmelt-runoff erosivity: Evaluating their relationship with seasonal suspended sediment dynamics in a mountainous watershed
Core Problem: Lack of understanding regarding the seasonal dynamics of total rainfall-runoff erosivity (TR) and its relationship with suspended sediment yield (SSY) in mountainous regions, especially the significant contribution of snowmelt-runoff erosivity (SR).
Key Innovation: Estimated daily snow accumulation, snowmelt, and SR, and calculated semi-monthly R, SR, TR, and SSY, revealing that SR contributes significantly to TR (30-75%) and that TR and SSY exhibit similar seasonal trends with a timing offset influenced by broader hydrological processes.
61. Accelerating urban flood prediction using a dual-stream Transformer‑CNN model with spatiotemporal feature fusion and uncertainty quantification
Core Problem: The increasing frequency of urban floods demands rapid and reliable prediction models, but existing deep learning approaches often suffer from limited training data diversity and neglect predictive uncertainty, reducing their reliability for urban flood risk management.
Key Innovation: Developed a novel dual-stream Transformer-CNN hybrid model with Monte Carlo dropout for rapid and uncertainty-quantified urban pluvial flood prediction, demonstrating high accuracy and significant speedup compared to physics-based models, thus benefiting flood early warning.
62. A multiple-interdependency risk assessment model with time-varying reasoning capabilities for long-distance water diversion projects
Core Problem: Existing operational risk assessment methods for long-distance water diversion projects often neglect the complex interdependencies between multiple risk factors and their dynamic evolution, limiting real-time risk reflection.
Key Innovation: Proposed a multiple-interdependency risk assessment model with time-varying reasoning capabilities (TVR-MIRA) for long-distance water diversion projects, integrating DEMATEL-ISM and a dynamic Bayesian network to quantify interdependencies and dynamic risk evolution, identifying floods and earthquakes as strong causal factors.
63. Model-free data-driven computational mechanics for deformation analysis of fractured rock masses
Core Problem: Accurately capturing the deformation and stress distribution of fractured rock masses under external loads remains a key scientific challenge due to their inherent heterogeneity, discontinuity, and anisotropy, hindering reliable mechanical response prediction.
Key Innovation: A model-free data-driven computational mechanics method was proposed for deformation analysis of fractured rock masses, which bypasses traditional constitutive modeling by directly incorporating experimental data, offering a unified and practical numerical approach for analyzing their nonlinear mechanical behavior.
64. Effects of hydrate occurrence habits on the acoustic propagation of hydrate-bearing sediment
Core Problem: Understanding the attenuation mechanisms of elastic waves in hydrate-bearing sediments (HBS) and how hydrate occurrence habits affect their acoustic propagation is crucial for interpreting field data and assessing HBS stability.
Key Innovation: This study investigated acoustic propagation in HBS, demonstrating that hydrate presence increases acoustic impedance and enhances wave energy transmission, and revealed that pore-filling hydrates induce stronger attenuation than cementing hydrates, providing insights into hydrate occurrence habits and their implications for HBS stability.
65. Pore‐Scale Transition Behavior of Digital Carbonate Rock Dissolution During CO2 Geo‐Sequestration
Core Problem: Predicting CO2 plume migration and ensuring long-term storage stability during geological carbon sequestration requires understanding the mechanisms controlling calcite dissolution under varying flow and structural conditions, particularly with explicit resolution of fracture-matrix interactions and computational scalability.
Key Innovation: A novel pore-scale numerical framework integrating the volumetric lattice Boltzmann method with GPU-CUDA parallel computing to efficiently simulate reactive flow in both fracture-free and fracture-matrix systems, revealing how injection velocity and temperature govern dissolution morphology and efficiency, and identifying distinct dissolution regimes.
66. A Novel Elastic Viscoplastic Clay–Structure Interface Model
Core Problem: Traditional elastoplastic interface models fail to adequately capture the unique mechanical characteristics of normally consolidated clay-structure interfaces, specifically the shear rate-dependent strength decrease and softening under slow shear.
Key Innovation: Development of a novel elastic viscoplastic model that incorporates overstress theory, failure plane transfer, and the critical state framework to accurately simulate the shear rate and normal stress effects on clay-structure interfaces, validated against experimental data.
67. Experimental study on local scour characteristics and protective effects of spoiler structures for monopile foundations of offshore wind turbines
Core Problem: Local scour is a critical factor threatening the service safety of monopile foundations for offshore wind turbines, necessitating effective protection strategies.
Key Innovation: This study proposes a novel Staggered Block Spoiler (SBS) structure and experimentally investigates its protective effects against scour, providing an optimal design threshold and a revised scour depth prediction formula for monopile foundations.
68. Dynamic assessment of compound flooding through a risk index approach
Core Problem: Traditional models for assessing flood risk in estuarine and coastal zones overlook rapidly evolving, non-linear interactions among drivers of compound flooding, leading to underestimation of severity.
Key Innovation: Developed the Compound Dynamic Risk Index (CDRI) which integrates daily-resolution probabilistic dependence modeling with a curvature-based diagnostic to capture second-order temporal dynamics, enabling early detection of shifts in hazard trajectory and functioning as an anticipatory indicator for emerging compound floods, with high predictive accuracy using deep learning algorithms.
69. Revolutionizing urban flooding predictions: a segmented deep learning model with fine-tuning update capabilities
Core Problem: Urban flooding, intensified by climate change and urbanization, poses significant threats and high costs, requiring more accurate and adaptable forecasting methods.
Key Innovation: Introduced a segmented deep learning model with fine-tuning capabilities for urban flooding forecasting, which effectively adapts to real-time data and dynamic urban conditions, surpassing traditional methods in accuracy and supporting proactive flood management.
70. Improving characterization of drought monitoring in the Yellow River Basin based on a high-resolution drought index
Core Problem: GRACE/GRACE-FO data for Terrestrial Water Storage Anomaly (TWSA) monitoring suffers from substantial uncertainties and coarse resolution, limiting precise drought characterization.
Key Innovation: Developed a high-resolution drought index (HDSI) by fusing multi-source GRACE solutions using the Bayesian Three-Cornered Hat (BTCH) method and downscaling TWSA products with GWR/RF models, providing improved data for water resource management and drought early warning.
71. Verifying excessive rainfall outlooks across three hurricanes of varying rainfall impact
Core Problem: Hurricane warning messaging often underemphasizes rainfall-induced flooding risk, and there is a need to verify the accuracy of hurricane rainfall forecasts to improve hazard communication.
Key Innovation: Investigated and verified Excessive Rainfall Outlooks (EROs) against a flash flood proxy for three Atlantic hurricanes using fractional coverage calculations and skill scores (FBS, FSS), demonstrating improved skill with new ERO definitions and providing a framework for wider forecast verification.
72. Flood risk assessment at electrical substations using a risk matrix coupled with a hydrodynamic model
Core Problem: Existing flood risk assessment methods often lack facility-specific vulnerability analysis for critical infrastructure like substations, hindering the development of targeted flood resilience strategies.
Key Innovation: A methodological framework for assessing flood risk at substations is presented, coupling a risk matrix with a hydrodynamic model and AHP, providing an actionable 'Flood Mitigation Priority Action List' for power grid managers.
73. Hydrological response of wildfire affected Mediterranean watersheds
Core Problem: Limited long-term experimental investigations exist on the post-fire hydrological response in small Mediterranean catchments, despite wildfires leading to increased hydraulic and hydrogeological risk.
Key Innovation: Long-term (6-year) monitoring of four wildfire-affected catchments revealed an initial increase in runoff coefficients followed by rapid recovery within the first post-fire year and full hydrological restoration after 5 years, primarily due to vegetation recovery.
74. Impact of El Niño Southern Oscillation and Indian ocean dipole on wildfire across the Indian forests
Core Problem: A comprehensive understanding of how atmospheric conditions and large-scale coupled ocean-atmospheric interactions (ENSO, IOD) influence the spread of forest fires across diverse Indian forested regions is needed.
Key Innovation: Analyzed the impact of antecedent precipitation, temperature, wind speed, vapor pressure deficit, ENSO, IOD, and western disturbances on wildfire spread across six climatic zones in India, providing insights to improve fire danger rating systems and generate alerts for disaster mitigation.
75. Seismic resilience assessment of a mountain tunnel considering terrain effects under near-fault and far-field earthquakes
Core Problem: There is a lack of comprehensive assessment regarding the seismic resilience of mountain tunnels, particularly considering terrain features and the distinct impacts of near-fault and far-field earthquakes.
Key Innovation: Developed a framework for assessing the seismic resilience of mountain tunnels, incorporating terrain features, IDA, fragility curves, and functionality restoration functions, revealing that near-fault pulse-like ground motions significantly reduce tunnel resilience.
76. Experimental study on mechanical properties of clay reinforced by fiber and phosphogypsum slag-based cementitious materials
Core Problem: Clay's strong water sensitivity and low strength make it highly susceptible to undesirable phenomena in engineering applications, necessitating effective and sustainable methods for soil property enhancement.
Key Innovation: Developed a novel green material (phosphogypsum slag-based cementitious material combined with basalt fiber) to solidify clay, demonstrating significant improvements in compressive strength, energy absorption, and permeability, providing theoretical and technical guidance for subgrade improvement and slope protection.
77. A novel indoor physical-mechanical experimental system for the sediment of water-level-fluctuation zone during the reservoir operation period
Core Problem: Understanding the deformation behavior and shear strength characteristics of sediments in reservoir water-level fluctuation zones under complex stress and wetting-drying cycles, which is crucial for slope stability, is challenging with existing experimental methods.
Key Innovation: Development of a novel indoor physical-mechanical experimental system (PMES) that accurately replicates complex stress and wetting-drying conditions. It quantifies the significant decrease in sediment compressive modulus and shear strength parameters (cohesion, internal friction angle) and increase in porosity due to wetting-drying cycles, linking porosity evolution to strength degradation.
78. Experimental study on deformation and failure characteristics of soft and hard composite rock masses with different layer thickness ratios
Core Problem: Understanding the spatiotemporal evolution and deformation characteristics of failure in layered soft-hard composite rock masses, which exhibit significant discontinuities and anisotropy.
Key Innovation: Conducted uniaxial compression tests with 3D-DIC on soft-hard composite rock specimens with varying layer thickness ratios, quantifying the negative correlation between soft rock proportion and mechanical properties, and establishing a damage evolution equation that reflects the entire rock failure process.
79. Mechanism and critical state of internal erosion in bare model test within subtropical monsoon climates
Core Problem: The mechanisms and critical thresholds governing internal erosion in bare model tests within subtropical monsoon climates remain poorly understood, hindering the development of effective long-term prevention and control strategies for related hazards.
Key Innovation: Developed natural sample-based synthesis techniques for model test models to simulate internal erosion, elucidating patterns of deformation and failure, dynamics of elemental composition, and ecological performance, and defining the evolutionary mechanisms and critical state thresholds for internal erosion.
80. Prediction of Ground Vibrations Induced by Vibratory Probe Compaction in Liquefiable Soils Using a Semi-Empirical Approach
Core Problem: Lack of reliable attenuation models for predicting ground vibrations induced by vibratory probe compaction (VPC) in liquefiable soils, hindering design-stage assessment of risks to nearby structures.
Key Innovation: Developed a semi-empirical framework to predict the attenuation of peak vertical particle velocity (PPVv) from VPC, quantifying vibratory power and coupling it to a hybrid attenuation function, achieving high accuracy (R2 = 0.987) in predicting ground vibrations for design-stage assessment.
81. Numerical Modelling of Construction Induced Stress Release Effects on the Seismic Response of Tunnels
Core Problem: The impact of construction-induced stress release on the seismic response and structural integrity (bending moment, tensile damage) of tunnel secondary linings is not well understood or quantified, hindering effective seismic resistance design.
Key Innovation: Developed a numerical modeling approach combining static (convergence-confinement, tracing element) and dynamic (seismic input) analyses to simulate tunnel construction and seismic response. Quantified the impact of stress release coefficients, surrounding rock classes, and tunnel depths on tunnel seismic performance, providing insights for seismic resistance design.
82. Seismic behavior assessment of exterior steel fiber reinforced concrete beam-column connections utilizing nonlinear finite element analysis
Core Problem: Traditional reinforced concrete beam-column connections in seismic applications often suffer from joint shear failure and reinforcement congestion, and their complete nonlinear seismic behavior, especially with advanced materials like SFRC, is not fully captured by simpler analytical models.
Key Innovation: Developed and validated advanced nonlinear finite element models to accurately predict the complete seismic behavior (damage progression, post-peak softening, ultimate displacement capacity) of exterior SFRC beam-column connections. Demonstrated that increasing fiber volume fraction significantly enhances seismic performance by delaying joint shear failure and promoting ductile behavior, offering a solution for reducing reinforcement congestion in seismic design.
83. Morphostratigraphy and sediment provenance of a Late Holocene succession from the central Po Plain (northern Italy)
Core Problem: Understanding the Late Holocene evolution of the Po Plain and how its morphostratigraphic units and paleochannels influence the local response to seismic perturbations, specifically co-seismic liquefaction.
Key Innovation: Detailed mapping and dating of ten morphostratigraphic units, linking their formation to avulsion processes and anthropic impact, and demonstrating that Late Holocene paleochannels controlled the location of co-seismic liquefaction during the 2012 earthquake sequence.
84. Topographic primacy in rock glacier distribution revealed by GAMs and their water storage: Insights from a new polygonised inventory of the southeastern Tibetan Plateau
Core Problem: Limited understanding of rock glacier distribution drivers and their hydrological importance in the southeastern Tibetan Plateau due to historically restricted datasets and the application of a limited number of drivers.
Key Innovation: Mapped 9543 rock glaciers and used Generalized Additive Models (GAMs) to reveal that topographic variables, particularly relief, are more robust predictors of rock glacier distribution than climatic/cryospheric factors. Quantified their significant water storage capacity (0.3–0.8 times modern glaciers) and observed vegetation expansion suggesting ongoing periglacial stabilization.
85. A standardized saltwater intrusion risk index for estuaries in the United States
Core Problem: The need for a quantitative and standardized method to assess saltwater intrusion (SWI) risk in estuarine systems.
Key Innovation: Developed the standardized saltwater intrusion risk index (SSIRI) by identifying salinity and discharge as primary factors for saltwater intrusion distance (SID), fitting their differences to a Pearson Type III distribution, and demonstrating its effectiveness in tracking SWI dynamics across various estuaries and timescales.
86. Climate change-driven propagation of meteorological-hydrological-groundwater drought: insights from the Pearl River Basin, China
Core Problem: The dynamics and nonlinear responses of interconnected meteorological, hydrological, and groundwater droughts to climate change, particularly their propagation within the hydrological cycle, are poorly understood.
Key Innovation: Applied the PCR-GLOBWB 2 model driven by CMIP6 climate projections to investigate the evolution and propagation of meteorological, hydrological, and groundwater droughts in the Pearl River Basin, revealing enhanced drought frequency, shortened duration, and delayed, nonlinear propagation behaviors under different emission scenarios.
87. Studying the mechanical behaviour of an anisotropic clay rock around gallery intersections and the effect of the support stiffness
Core Problem: Gallery intersections in deep underground constructions present complex challenges due to their three-dimensional nature, excavation sequence, support installation, high in-situ stress, and anisotropic material properties, making stress and strain redistribution difficult to predict.
Key Innovation: Investigated the mechanical behavior of anisotropic clay rock around supported gallery intersections using 3D finite element analyses, modeling progressive excavation and support phasing. Demonstrated the impact of different support stiffnesses on stress distribution, plastic strain, and plastic zone generation in the surrounding rock mass.
88. Evaluating statistical learning approaches to predict suction bucket displacement due to vertical cyclic loading in sand
Core Problem: Predicting accumulated displacement of suction buckets for offshore wind turbine foundations due to vertical cyclic loading is critical for design, but traditional numerical models are time-consuming and require detailed information often unavailable at early design stages.
Key Innovation: Evaluated three non-linear statistical learning models (GAM, RF, XGBoost) to predict suction bucket displacement from centrifuge test data, with XGBoost performing best. The model captured the effects of changing stress amplitude during storm loading, offering a quicker and more realistic prediction than typical engineering practice.
89. Experimental and numerical study on dynamic responses of three-story building supported by T-BAGS base isolation and vibration control system
Core Problem: Buildings are vulnerable to severe earthquake-induced accelerations, requiring effective base isolation and vibration control systems to attenuate structural responses.
Key Innovation: The T-BAGS seismic base isolation and vibration control system was developed and evaluated, demonstrating its ability to considerably attenuate building response acceleration during severe earthquakes through a combination of base isolation and hysteretic shear deformation of sandbags.
90. An adaptive UDEC calibration tool for modelling intact rocks based on machine learning
Core Problem: Determining accurate modeling parameters for intact rocks in UDEC is a fundamental issue, and existing machine learning attempts have been limited in compatibility across experimental inputs or ML models.
Key Innovation: UPCal, an open-source adaptive machine learning-based UDEC calibration tool, was developed to automatically derive valid UDEC modeling parameters for intact rocks, offering compatibility across experimental inputs and ML models, thereby enhancing the credibility of rock mass engineering simulations.
91. Landward Acceleration of Tropical Cyclones Making Landfall Along the South China Coast
Core Problem: Accurate disaster preparedness for tropical cyclones (TCs) is hindered by a limited understanding of short-term changes in their translation speed components (normal and parallel to the coast) as they approach landfall.
Key Innovation: This study reveals a significant pre-landfall acceleration of TCs along the South China coast, primarily contributed by the normal component of the translation vector, driven by land-induced asymmetric flow and convection. These findings strengthen the understanding of TC motion dynamics for improved disaster prevention and mitigation.
92. Experimental Study on the Behavior of Bucket Foundations under Sustained Uplift Loading
Core Problem: Uncertainty in current design approaches for bucket foundations under tension regarding the duration over which tensile capacity from negative excess pore pressure can be sustained.
Key Innovation: An experimental study demonstrating that sustained uplift loads on bucket foundations in clay are resisted by skirt friction and negative excess pore pressure, providing dimensionless data and algebraic equations to estimate vertical displacement and safe load duration.
93. In-Season Sugarcane Mapping in the U.S. and Brazil Using Time-Invariant Phenological Features
Core Problem: Traditional time-series mapping of sugarcane faces challenges due to limited training data across distant producers and heterogeneous growth seasons.
Key Innovation: Developed an automated phenology-based transfer learning workflow using time-invariant phenological features derived from Sentinel-2 data and historical U.S. training data to produce accurate in-season sugarcane maps for both the U.S. and Brazil, demonstrating transferability and improved accuracy over existing products.
94. Evolution characterization and attribution analysis of hydrological drought in Ganjiang River based on hydrological model and deep learning coupling
Core Problem: Better prevention and control of hydrological drought in the Ganjiang River Basin requires understanding its evolution characteristics and attribution.
Key Innovation: Coupled the ABCD hydrological model with seven deep learning algorithms (e.g., ABCD-random forest) to improve runoff simulation accuracy, applied the standardized runoff index to describe hydrological drought, and quantitatively assessed the effects of human interventions and climate change on hydrological drought, providing insights for water resource management and hydrological disaster prevention.
95. Spatiotemporal drought variability in Gansu Province based on reconstructed land surface temperature
Core Problem: Increasing drought risks exacerbated by climate change in arid-semiarid regions require a comprehensive understanding of drought characteristics.
Key Innovation: Proposed a novel framework integrating Temperature Vegetation Drought Index (TVDI) derived from reconstructed Land Surface Temperature (LST) with Standardized Precipitation Index (SPI) to investigate spatiotemporal drought patterns and their correlations with climatic factors and agricultural impacts in Gansu Province, providing scientific support for drought resilience strategies and water resource optimization.
96. Coal, soil, and survival: unraveling the agricultural consequences of mining area in India
Core Problem: Coal mining leads to significant environmental degradation, including soil fertility loss, water resource depletion, and vegetation cover reduction, impacting agricultural sustainability.
Key Innovation: Integrated multivariate statistics with RS–GIS products to quantify the agricultural consequences of mining on soil health in the Raniganj block, India, and proposed SDG-aligned reclamation strategies (phytoremediation, hydroponics, aquaponics) to balance economic growth with environmental stewardship.
97. Freeze-thaw response and macro-micro evolution of saline soil stabilized with lime and fly Ash
Core Problem: Saline subgrade soils in cold regions suffer severe deterioration from freeze-thaw cycles, and sustainable, low-carbon stabilization methods are needed.
Key Innovation: Developed a sustainable stabilization method for saline soils using lime and fly ash, demonstrating significant improvements in unconfined compressive strength, enhanced frost resistance, and self-healing capabilities under freeze-thaw cycles, supported by macro-micro structural analysis.
98. Tectonic activity and seismicity impacts on the sediment yield of Iranian basins
Core Problem: Understanding how tectonic activity and seismicity influence the sediment yield in Iranian basins.
Key Innovation: Investigating the relationship between tectonic activity, seismicity, and sediment yield in Iranian basins.
99. Slope grade difference of cropland spatial pattern in China
Core Problem: Existing studies on cropland spatial patterns predominantly overlook the significant influence of slope gradient, leading to a lack of understanding of cropland distribution on different slopes and its implications for protection and potential geohazard risk.
Key Innovation: Proposed a slope location quotient (SLQ) index to reflect the relative advantage of cropland distribution, revealing that the inverse correlation between cropland area and slope remained stable, but the mean absolute variation of SLQ increased with slope gradient, particularly for slopes >15°, with a distinct southward-increasing pattern, recommending reversing this trend for cropland protection.
100. An efficient active learning-based method for reliability analysis of geotechnical structures using sparse polynomial chaos expansion
Core Problem: Reliability analysis of computationally expensive geotechnical problems requires efficient metamodeling techniques that can handle uncertainty and provide accurate predictions with minimal computational cost.
Key Innovation: Introduced a novel active learning algorithm combining sparse polynomial chaos expansions (PCEs) with Monte Carlo simulation for reliability analysis, utilizing Sparse Bayesian Learning (SBL) and an active learning strategy to achieve high accuracy and computational efficiency for geotechnical reliability analysis.
101. A semi-analytical method for high retaining wall earth pressure in concrete face rockfill dam
Core Problem: Lack of an accurate quantitative method for calculating earth pressure on high retaining walls in concrete face rockfill dams, which is crucial for dam safety and optimizing face slab stress distribution.
Key Innovation: Proposed a novel semi-analytical method, based on Rankine’s theory and incorporating parameters from a PSO-Kriging surrogate model, for accurately calculating earth pressure on high retaining walls in concrete face rockfill dams, validated by numerical simulation.
102. Damage Evolution and Energy Dissipation Mechanisms in Marble Subjected to Cyclic Stress-Path Disturbances
Core Problem: Inadequate understanding of the anisotropic deformation, failure mechanisms, and damage evolution of deep rock masses (marble) under complex true-triaxial cyclic loading, which is critical for predicting stability during excavation.
Key Innovation: Investigated the damage evolution and energy dissipation mechanisms of marble under true-triaxial cyclic loading, identifying critical precursors to instability (AE b-value drop, Felicity effect) and proposing a comprehensive damage model for stability prediction in deep underground engineering.
103. Closed-Form Solution for Circular Openings in Confining Pressure-Sensitive Strain-Softening Rock Mass
Core Problem: Predicting the irreversible damage, significant deformation, and potential instability of engineering structures (circular openings) in deep underground rock masses under excavation, especially considering confining pressure-sensitive strain-softening behavior.
Key Innovation: Developed a confining pressure-sensitive strain-softening model and derived a closed-form solution for circular openings, providing a reliable theoretical foundation for predicting plastic zone, stress, and displacement distributions, and optimizing support design in deep tunnels.
104. Engineering Properties of Lava Rocks from S. Miguel Island: Insights from Laboratory Testing and New Empirical Correlations
Core Problem: Lack of comprehensive characterization and predictive models for the mechanical behavior of widespread lava rocks, particularly in the Azores, despite their importance as geotechnical materials.
Key Innovation: Providing a comprehensive mechanical database for Azorean lavas, identifying open porosity as the principal control on mechanical behavior, and deriving statistically significant empirical equations to estimate strength parameters from key properties like porosity, applicable from basalts to trachytes.
105. Study on Mechanical Properties and Crack Propagation Mechanisms of Saturated Red-Bed Sandstone Under Cyclic Loading–Unloading Conditions
Core Problem: Understanding the mechanical properties and crack propagation mechanisms of saturated red-bed sandstone under cyclic loading-unloading, particularly how water-rock interaction influences deformation and failure in tunnel surrounding rock.
Key Innovation: Conducted seepage tests, cyclic loading-unloading experiments, and CT scanning to characterize the influence of confining pressure and bedding dip angle on peak strength, volumetric strain, crack propagation rate, and crack porosity, establishing mathematical expressions and energy dissipation analysis for saturated red-bed sandstone.
106. Discrete element analysis of pullout behavior of geogrid reinforced clay with thin sand layers
Core Problem: Understanding the pullout behavior and microscopic properties of sandwiched geogrid reinforced soil (SGRS) using clay and thin sand layers, particularly the influence of sand layer thickness.
Key Innovation: Used DEM software PFC2D to simulate SGRS pullout behavior, analyzing particle displacement, porosity, and principal stresses, demonstrating that sand layers significantly improve particle mobilization and displacement patterns, and identifying optimal sand layer thickness for reinforcement.
107. Seismic analysis based on a new interval method with incomplete information
Core Problem: Traditional Monte Carlo simulations for uncertainty analysis in seismic structural responses are computationally inefficient, especially when dealing with incomplete seismic acceleration information.
Key Innovation: Proposes DES-ES-SS, a new computational framework that improves the efficiency and accuracy of interval uncertainty analysis for seismic structures by enhancing CMA-ES and leveraging response dependency, applicable to time-variant uncertain seismic acceleration.
108. Hysteretic behavior of reinforced ultra-high-performance concrete columns under combined axial and flexural loads
Core Problem: Limited understanding and prediction models exist for the flexural strength and hysteretic behavior of reinforced ultra-high-performance concrete columns under cyclic (seismic) loading, particularly regarding the degradation of fiber-bridging capacity.
Key Innovation: Conducts experimental tests on reinforced ultra-high-performance concrete columns under monotonic and cyclic loading, revealing the impact of cyclic loading on load-carrying capacity and ductility, and proposes a new flexural strength prediction model for cyclically loaded members.
109. Energy-based displacement demand predictions for SDOF systems verified by shaking table tests
Core Problem: Accurate estimation of displacement demands is crucial for evaluating seismic performance and designing structural systems, and existing prediction methodologies can be enhanced for robustness.
Key Innovation: Introduces an energy-based methodology utilizing imparted seismic input energy (EI) to predict displacement demands in single-degree-of-freedom (SDOF) systems, validated through comprehensive numerical analyses and shaking table tests, offering robust empirical formulations.
110. Evaluating damping schemes for the discontinuum seismic analysis of masonry cross-vaults
Core Problem: Simulating the highly nonlinear seismic behavior of unreinforced masonry (URM) structures using discontinuum methods like DEM is challenging, particularly regarding the choice and tuning of damping schemes which can lead to inaccurate results or computational inefficiencies.
Key Innovation: Implements and validates an alternative Maxwell damping approach for Distinct Element Method (DEM) seismic analysis of masonry cross-vaults, introducing an optimization algorithm for tuning Maxwell elements to achieve near-uniform damping, demonstrating improved accuracy and efficiency.
111. Robust decision-based proactive prevention method for renewable distribution systems under typhoon involved with rainstorm events
Core Problem: Existing proactive prevention methods for renewable distribution systems (RDSs) under typhoon rainstorm events lack flexibility and economic efficiency due to insufficient consideration of disaster evolution and component status uncertainties.
Key Innovation: A robust decision-based proactive prevention framework for RDSs that establishes a mechanism-based and data-driven uncertainty model for typhoon rainstorms, develops a dual-uncertainty scenario generation method, and formulates a multi-objective MINLP model to dynamically prioritize scheduling schemes, thereby reducing system vulnerability.
112. Network recovery for UAV-assisted IoTs after cascading failures with heterogeneous graph neural networks
Core Problem: The resilience of UAV-assisted Internet of Things (IoT) against cascading failures is a significant challenge, as such failures can severely compromise network topological integrity and efficient recovery.
Key Innovation: Proposes a Network Recovery scheme with Heterogeneous Graph neural network (NRHG) that employs HGNN (with graph perception and communication layers) and a multi-agent reinforcement learning (MARL) framework to enable collaborative action decisions for UAVs, efficiently scheduling surviving UAVs to cover network blind spots and restore system throughput after cascading failures.
113. Has the latest IMERG V07 from GPM improved the performance of precipitation estimation of regional-scale compared to its predecessor?
Core Problem: A comprehensive regional-scale evaluation of the newly released IMERG V07 precipitation datasets and their improvements over V06, especially concerning natural hazard monitoring and forecasting, was lacking.
Key Innovation: Systematically evaluated and compared IMERG V07 and V06 precipitation products over the Yellow River Basin, demonstrating significant performance enhancements of V07 across various metrics, including extreme weather response and reduced sensitivity to environmental factors, providing critical guidance for users in natural hazard applications.
114. Monotonic weight-constrained XGBoost model for enhanced prediction of soil-water characteristic curves in unsaturated soils
Core Problem: Conventional measurement of soil-water characteristic curves (SWCC) is time-consuming, and existing machine learning models often fail to enforce physical monotonicity between water content and matric suction, limiting their physical consistency.
Key Innovation: Developed a monotonicity-constrained machine learning framework (MGA-XGBoost) for accurate and physically consistent SWCC prediction, identifying key influential features and improving model generalizability and interpretability for simulating water transport in soils.
115. Sub‐annual spatiotemporal dynamics of sediment and organic matter storage in beaver ponds
Core Problem: Understanding the sub-annual spatiotemporal dynamics of sediment and organic matter storage in beaver ponds, and how these dynamics are influenced by factors like rainfall and pond position, to better assess their sediment trapping potential.
Key Innovation: Used sonar to monitor sediment and organic matter thickness at finer spatial (meters) and temporal (months) resolutions than previously published. Found that pond position and maximum antecedent rainfall account for 76% of variation in daily elevation change, with rainfall intensity influencing net accretion/erosion patterns.
116. Paper: Suspended sediment transport in the Mariuá archipelago: A megacomplex anabranching system in the Negro River (Amazon Basin)
Core Problem: Understanding the interactions between sediments and biota and the dynamics of suspended sediment transport in a supply-limited cratonic river system (Mariuá Archipelago) to assess ecosystem stability and potential threats from increased sediment inputs.
Key Innovation: Characterized minimal inter-channel suspended sediment concentration (SSC) variation and quantified net sediment retention within the archipelago using satellite imagery and hydrological data. Identified that downstream lakes act as sediment sinks, supporting distinct aquatic plant communities, and highlighted the role of geomorphological constraints in promoting long-term ecological stability.
117. Solar‐Wind Triggering of Substorm Onset During the May 2024 Superstorm: Coordinated Global Observations and Simulations
Core Problem: The underlying mechanism for directly driven intense substorms, particularly whether they can be triggered without significant prior magnetic flux accumulation (loading-unloading cycle), remains unresolved.
Key Innovation: For the first time, global observations and simulations strongly indicate that an intense substorm during the May 2024 superstorm was directly triggered by an enhanced southward interplanetary magnetic field, which rapidly drove global-scale plasma convection and auroral currents, thinning Earth's nightside current sheet with negligible flux loading.
118. Formation of Secondary Structures Associated With the Gradient Drift Instability in the High‐Latitude Ionosphere
Core Problem: While the linear growth and primary nonlinear development of elongated striations due to the gradient drift instability (GDI) in the high-latitude ionosphere are established, the formation of secondary, smaller branch structures remains not fully understood.
Key Innovation: Using two-dimensional numerical simulations, this study shows that smaller branch structures evolve asymmetrically on the sides of striations when the background electric field or wavevector has a component along the density gradient, highlighting the critical roles of these factors in generating secondary GDI structures and improving space weather prediction.
119. Event‐Type‐Based Multi‐Dimensional Diagnostics of Process Limitations in Hydrological Models
Core Problem: Aggregated evaluation metrics and overlooked hydrological process variability in individual streamflow events hinder a deep understanding of how well hydrological processes are encoded in models and limit the identification of specific process limitations.
Key Innovation: Introducing a novel event-type-based multi-dimensional diagnostic framework that evaluates performance variation (timing and relative magnitude errors) for different streamflow event types and uses explainable machine learning (XAI) to analyze the relative importance of event properties, model process limitations, and initial model errors, providing valuable insights for advancing hydrological research and practice.
120. DINOMotion: Advanced Robust Tissue Motion Tracking With DINOv2 in 2D-Cine MRI-Guided Radiotherapy
Core Problem: Accurate tissue motion tracking in 2D-Cine MRI-guided radiotherapy is critical but challenging for existing methods due to large misalignments and lack of interpretability.
Key Innovation: DINOMotion, a novel deep learning framework based on DINOv2 with Low-Rank Adaptation (LoRA) layers, which provides robust, efficient, and interpretable motion tracking by automatically detecting corresponding landmarks for image registration, achieving superior performance (e.g., Dice scores >90%) and processing speed (~30 ms/scan) compared to state-of-the-art methods.
121. LDFENet: A Lightweight Dilated Feature Enhanced Network for SAR Ship Detection
Core Problem: Existing CNN-based SAR ship detectors often lack sensitivity to small-scale targets due to inadequate multiscale feature representation and limited expressiveness in complex environments, while also being computationally expensive.
Key Innovation: Proposes LDFENet, a lightweight dilated feature enhanced network, incorporating a CSBS module for small ship information retention, an AD-ELAN module for multiscale adaptability, a mixed attention module for feature enhancement, and powerful-IoU loss, achieving superior accuracy and efficiency in SAR ship detection with strong generalization.
122. NorESM2–DIAM: a coupled model for investigating global and regional climate-economy interactions
Core Problem: Existing climate-economy models are often limited by coarse spatial aggregation, simplified climate representation, or lack of interaction between climate and economy, hindering accurate assessment of climate change impacts.
Key Innovation: Development of NorESM2–DIAM, the first framework to fully couple an Earth System Model (NorESM2) with a high-resolution cost-benefit Integrated Assessment Model (DIAM), enabling spatially disaggregated and realistic climate-economy interaction studies and highlighting regional economic impacts.
123. The representation of climate impacts in the FRIDAv2.1 Integrated Assessment Model
Core Problem: Traditional Integrated Assessment Models (IAMs) often limit or exclude the representation of feedback loops between climate and society, hindering a full understanding of coupled human-Earth system dynamics and climate change effects.
Key Innovation: FRIDAv2.1, a new IAM, internalizes climate-to-society feedbacks (climate impacts) through global impact functions, enabling valuable insights into the dynamics of the coupled human-Earth system and the response to future emissions scenarios.
124. A revised temperature-dependent remineralization scheme for the Community Earth System Model (v1.2.2)
Core Problem: Existing Earth system models do not adequately incorporate temperature-sensitive mechanisms like bacterial respiration (remineralization) into the ocean carbon cycle, leading to less accurate predictions of climate response in warming scenarios.
Key Innovation: A new temperature-dependent parameterization for remineralization (Tdep) is employed in CESM1, which more accurately captures regional heterogeneity in particulate organic carbon transfer efficiency, improving predictions of carbon storage in the warming ocean.
125. Incorporating natural variability in master recession curves
Core Problem: Traditional Master Recession Curves (MRCs) may not fully represent the natural variability in streamflow recession, which is influenced by antecedent hydroclimatic and heterogeneous storage conditions.
Key Innovation: The paper hypothesizes and demonstrates that a continuum of recession curves, represented by a family of percentile curves, better captures the natural variability (aleatory uncertainty) in streamflow recession, supported by correlation techniques and a two-store qualitative model.
126. Multi-year La Niña–El Niño transition influenced Earth’s extreme energy uptake in 2022–2023
Core Problem: The causes of Earth's extreme energy uptake in 2022–2023 are unclear, reflecting limited knowledge of how internal climate variability shapes Earth’s energy imbalance.
Key Innovation: Multi-model climate simulations and satellite observations reveal that the transition from multi-year La Niña to El Niño was the key driver of Earth's extreme energy uptake in 2022–2023, explaining about 75% of the observed uptake when combined with externally forced components.
127. Ice sheets big and small
Core Problem: Identifying the controls on ice sheet stability and predicting their future behavior in a warming climate is challenging, given their variable sensitivity to environmental perturbations.
Key Innovation: Reconstructions of past ice sheet variability are highlighted as crucial for understanding the factors that control their stability and how they may respond to future warming scenarios.
128. Climatological characterisation and trends of extreme winds and their application to power transmission lines in China
Core Problem: The structural stability of long-distance power transmission networks is challenged by extreme wind events, but their spatial variability and potential risk to infrastructure are poorly quantified at a national scale.
Key Innovation: Assessed spatiotemporal characteristics and trends of extreme winds along major transmission lines using ERA5 reanalysis and in-situ observations, and developed a potential risk index from normalized wind speed return periods to identify regions most exposed to extreme wind hazards, providing crucial insights for infrastructure design and region-specific risk assessments.
129. Record-breaking rainfall in Sri Lanka in 2014 enhanced by the MJO
Core Problem: The impact of the Madden–Julian Oscillation (MJO) on rainfall over Sri Lanka during the October–November–December (OND) period is not well understood, despite its influence on extreme rainfall events.
Key Innovation: Demonstrated that record-breaking rainfall in Sri Lanka in 2014 was enhanced by persistent MJO activity (phases 2 and 3), leading to anomalous ascent and increased moisture convergence, highlighting the need for better understanding of MJO-local thermodynamics for extreme precipitation forecasts.
130. An approach for quantifying the three-dimensional joint roughness coefficient of rock joints during rotary drilling
Core Problem: Limitations in accurately quantifying the three-dimensional roughness of rock joints, especially those formed during rotary drilling, which is crucial for understanding rock mass behavior.
Key Innovation: Developed a method to quantify 3D joint roughness coefficient (JRC) using 3D laser scanning, extracting parameters like mean height, RMS, and average inclination angle, and establishing a functional relationship with JRC, showing improved stability over classical methods.
131. Quantitative evaluation of borehole density impact on 3D geological modeling of quaternary structures
Core Problem: The challenge of accurately reconstructing heterogeneous Quaternary geological structures, including small- to medium-scale features like lenses and channels, with limited borehole data for 3D geological modeling.
Key Innovation: Quantitatively evaluated the impact of borehole density and sampling layout on the accuracy of 3D geological models of Quaternary structures, identifying a critical borehole density threshold required for reliable reconstruction of complex features.
132. Microbial-induced calcium carbonate precipitation combined with aeolian soil for crack repair in Xinjiang Desert open channel: a multi-scale study
Core Problem: Cracks in the bank slope linings of open channels in the Xinjiang Desert threaten structural stability, and traditional crack repair materials perform poorly in arid areas, especially for wider cracks.
Key Innovation: Proposed microbial-induced calcium carbonate precipitation (MICP) combined with desert aeolian soil for environmentally friendly crack repair, clarifying the critical crack width for MICP alone and demonstrating that incorporating aeolian soil significantly enhances repair efficiency for wider cracks.
133. Effect of Fluid pH on Subcritical Crack Growth and Mineral Morphology of Marble Under Double Torsion Tests
Core Problem: Inadequate understanding of how fluid pH influences the subcritical crack growth and mineral morphology of marble, which is crucial for predicting rock weathering and fracture behavior in subsurface engineering and heritage preservation.
Key Innovation: Quantified the pH dependence of marble's subcritical crack growth index (n) and mineral morphology, revealing distinct responses to acidic and alkaline environments, and highlighted the role of chemical reactions in fracture behavior, with implications for engineering and weathering assessment.
134. A data-driven approach for rapid seismic risk prediction of RC buildings
Core Problem: The need for rapid and accurate determination of vulnerable buildings within existing stock for seismic risk assessment and pre-earthquake decision-making.
Key Innovation: Developed an AI-based methodology using machine learning (specifically XGBoost achieving 92% accuracy) to rapidly and reliably estimate seismic risk levels in reinforced concrete frame structures, based on a simulated dataset of building profiles.
135. Predictive models for the in-plane strength and drift capacity of unreinforced masonry walls with various types of failure modes using machine learning algorithms
Core Problem: The need for a comprehensive predictive model to accurately estimate the in-plane strength and drift capacity of URM walls, regardless of the dominant failure mode, for seismic vulnerability assessment, given the high uncertainties in masonry material.
Key Innovation: Developed and validated machine learning algorithms (with Gradient Boosting showing best performance) to accurately predict the in-plane strength and drift capacity of URM walls, integrating various failure modes and overcoming material uncertainties, based on a compiled dataset of 191 URM wall tests.
136. Development of a new seismic performance assessment method for bridges considering uncertainties in the entire process of seismic wave propagation and structural dynamic response: a case study of precast segmental pier bridges
Core Problem: Accurately assessing the seismic performance and fragility of long-span bridges, particularly precast segmental pier bridges, requires comprehensively accounting for multiple sources of uncertainty throughout the entire seismic analysis process (ground motion, SSI, structural parameters).
Key Innovation: Developed a new seismic performance assessment method for bridges that integrates uncertainties from seismic wave propagation, soil-structure interaction, and structural parameters, using site-specific transfer functions, parameterized FE models, and an ANN-based model for efficient fragility curve prediction.
137. Seismic performance evaluation of subway station structures with concrete-filled-steel-tube columns
Core Problem: Enhancing the seismic performance of underground subway station structures and quantitatively comparing the effectiveness of novel CFST columns against conventional RC columns, considering complex nonlinear soil-structure interaction.
Key Innovation: Numerically simulated and compared the seismic performance of subway station structures with CFST and RC columns, explicitly considering nonlinear SSI, and quantified that CFST column structures exhibit an average 44% lower damage probability, providing a reference for performance-based seismic design.
138. Data-driven dynamic optimization of TBM rock-breaking efficiency: A robust and generalizable framework with geological safety constraints
Core Problem: Achieving dynamic optimization of Tunnel Boring Machine (TBM) rock-breaking efficiency in deep and long tunnels is difficult due to geological complexity and the uncertainty of experience-based control, making it challenging to balance efficiency with geological safety.
Key Innovation: A data-driven multi-objective dynamic optimization framework is proposed that reconstructs tunneling data based on spatial geological similarity, develops a lightweight rock-breaking response prediction model using TPE and LightGBM, and employs U-NSGA-III to optimize TBM control parameters while incorporating geological safety thresholds as constraints, significantly enhancing both efficiency and geological adaptability.
139. Climate Change Will Enhance Hypercapnic Hypoxia Threatening Mangrove Habitats
Core Problem: The suitability of mangrove habitats, crucial for marine species and storm protection, is threatened by climate change, but the specific impacts of global warming and rising atmospheric CO2 on their water chemistry (dissolved oxygen and CO2 levels) are not fully understood.
Key Innovation: Observations from 23 mangrove-lined estuaries reveal existing mild to severe hypercapnic hypoxia, and projections show that climate change will significantly decrease oxygen and increase CO2 concentrations by 2100, leading to more frequent, longer, and more severe hypercapnic hypoxia events, threatening mangrove biodiversity and coastal fisheries.
140. Role of Dead‐End Regions and Transmitting Pores in Mixing and Reactivity in Unsaturated Porous Media
Core Problem: A limited unified understanding of the distinct roles of transmitting pores (TP) and dead-end regions (DER) in mixing interface evolution and upscaled reaction rates in unsaturated porous media, where complex pore-scale processes control mixing-limited reactions.
Key Innovation: Using high-resolution multiphase flow simulations and developing a two-compartment model to investigate how water saturation influences mixing interface evolution across Péclet numbers, demonstrating that under low saturation, the prolonged elongation of the mixing interface substantially enhances reaction rates, highlighting the critical role of saturation-driven heterogeneity.
141. Mapping Evapotranspiration and Diffuse Recharge via Variational Assimilation of GOES LST and SMAP Soil Moisture
Core Problem: Large-scale mapping of evapotranspiration and groundwater recharge is hindered by sparse in-situ observations and uncertainties in land surface models.
Key Innovation: A scalable variational data assimilation (VDA) framework (LIDA-2) that jointly assimilates hourly GOES land surface temperature and SMAP surface soil moisture into a coupled water balance and dual-source energy model to produce high-resolution maps of ET and recharge, showing substantial improvement in estimates.
142. Simulated Hydrologic Impacts of Cloud Seeding in the North Platte and Little Snake River Basins of Wyoming
Core Problem: The need to quantify the hydrological impacts of cloud seeding as a strategy to enhance water supplies in drought-prone regions amidst climate change.
Key Innovation: Using a 36-member ensemble of WRF-WxMod simulations to force a spatially distributed hydrological model (WRF-Hydro) to study how simulated cloud seeding impacts hydrology (snow water equivalent, streamflow, soil moisture) in specific river basins, quantifying the water budget changes.
143. Research on drift limits for floating offshore substations based on the mechanical strength of dynamic cables
Core Problem: Floating Offshore Substations (FOSSs) design is constrained by the conflict between load-bearing capacity and draft limitations, lacking mature design guidelines and established displacement criteria to protect critical submarine cables.
Key Innovation: Simulated a Cable–FOSSs model under dynamic wave loads to determine tensile tension and bending curvature, analyzed maximum FOSS displacement before cable failure, and proposed optimization schemes for cable attachments, providing key design criteria.
144. Lung-DDPM: Semantic Layout-Guided Diffusion Models for Thoracic CT Image Synthesis
Core Problem: Data scarcity in medical imaging (e.g., lung cancer screening) due to costly annotation and privacy concerns limits the application of AI, and existing generative models may not produce anatomically reasonable or high-fidelity 3D synthetic images.
Key Innovation: Lung-DDPM, a semantic layout-guided denoising diffusion probabilistic model (DDPM) for thoracic CT image synthesis, which generates high-fidelity, anatomically reasonable 3D synthetic CT images, significantly outperforming SOTA generative models and improving downstream lung nodule segmentation tasks (8.8% Dice, 18.6% sensitivity improvement).
145. Efficient Super-Resolution Bayesian Eletromagnetic Brain Imaging
Core Problem: Electromagnetic source imaging at super-resolution is challenging due to the need to estimate thousands of parameters from limited sensor data, and existing Bayesian approaches are computationally inefficient and rely on arbitrary thresholds for source determination.
Key Innovation: A robust and efficient Bayesian approach for super-resolution brain source and noise reconstruction that incorporates hyperparameter pruning during optimization, dynamically removing near-zero hyperparameters to accelerate convergence and determine sparsity, achieving statistically significant improvements in reconstruction accuracy and runtime efficiency compared to benchmarks.
146. Design of a Flexible Push-Button Two-Dimensional Ultrasound Array Probe
Core Problem: Achieving real-time positioning of flexible wearable ultrasonic transducer array units under dynamic conditions for two-dimensional area array ultrasonic phased imaging.
Key Innovation: A novel transducer array based on a button flexible structure, featuring a push-button flexible hinge for three-dimensional adaptive deformation and a dual ultrasonic array combining rigidity and flexibility for real-time ultrasonic positioning in dynamic wearable scenes.
147. Spatial Ergodicity of Doppler Characteristics in Polarimetric Ocean Radar Scattering: A Numerical Study
Core Problem: Understanding the spatial ergodicity of Doppler characteristics in polarimetric ocean radar scattering, particularly how illumination size affects Doppler shift accuracy for sea surface current retrieval.
Key Innovation: Numerically investigates spatial ergodicity of Doppler characteristics in polarimetric ocean radar scattering using the SSA-2 model, defining a Doppler shift error threshold and quantitatively evaluating ergodicity, finding that co-polarization achieves ergodicity with smaller illumination sizes than cross-polarization.
148. MLAWind: A Monthly Sea Surface Wind Dataset Derived from an Interpretable Machine Learning Approach Integrating In-Situ Observations and Satellite Data
Core Problem: A comprehensive, long-term, and reliable gridded sea surface wind dataset is needed to understand atmospheric circulation changes and air-sea interactions at various time scales.
Key Innovation: Development of MLAWind, a 1°×1° monthly sea surface wind dataset (1950-2023) using an interpretable machine learning model that integrates satellite and in-situ data, demonstrating robust performance and interpretability for global climate change research.
149. Mapping water content dynamics in SAT systems using 3D electrical tomography
Core Problem: Understanding the influence of recharge strategies and reactive barriers on hydration-drainage fronts and biofilm development in Soil-Aquifer Treatment (SAT) systems, particularly how soil heterogeneity causes water to infiltrate through preferential pathways, is challenging.
Key Innovation: Cross-hole electrical resistivity tomography is used to map 3D water content dynamics and potential biofilm formation in SAT systems, revealing that water infiltration occurs through preferential pathways, reactive barriers enhance water retention, and pulsed recharge promotes biofilm growth more effectively.
150. Stewarding AI in agriculture research
Core Problem: The need to effectively and equitably leverage AI to unlock agricultural data and address global challenges for vulnerable smallholder farmers, while mitigating risks.
Key Innovation: Proposing a framework or approach for responsible stewardship of AI development and deployment within agricultural research to ensure relevant and equitable outcomes for vulnerable farmers.
151. YORU: Animal behavior detection with object-based approach for real-time closed-loop feedback
Core Problem: The challenge of robust and accurate real-time analysis of dynamic social behaviors in animals, especially with multiple interacting individuals, using conventional pose estimation methods.
Key Innovation: Development of 'YORU,' an object detection deep learning algorithm that directly identifies behaviors as 'behavior objects' based on animal shape, enabling robust, accurate, and real-time detection of social behaviors across species, and facilitating closed-loop feedback.
152. Mechanical properties and disaster-causing mechanism of sandstones under multistage low-cycle loading considering stress conditions
Core Problem: Understanding the cyclic mechanical properties, strength degradation, and fatigue life of sandstone under multi-stage low-cycle loading and varying confining pressures is crucial for ensuring safety in underground engineering projects.
Key Innovation: Systematic experimental investigation using multi-stage cyclic loading tests and acoustic emission (AE) analysis quantifies the profound influence of confining pressure on rock fatigue deformation, strength degradation, and micro-crack propagation. It identifies a tensile-dominated mixed cracking mode and a competition between hardening and damage mechanisms, informing optimized construction strategies for tunnels.
153. Investigating the textural characteristics and engineering properties of Marly rocks in the Tabriz region, East Azerbaijan (NW Iran)
Core Problem: It is essential to understand the parameters governing the mechanical behavior of local soft marly rocks for construction in the Tabriz region, specifically assessing the relative importance of mineralogy and rock texture.
Key Innovation: Comprehensive study correlating mineralogical coefficients (MC) and texture coefficients (TC) with various mechanical and physical properties of marly rocks. It demonstrates that regression models based on TC generally exhibit better predictive performance for key engineering properties like adhesion, elastic modulus, internal friction angle, and UCS, highlighting the importance of microstructural analysis.
154. In-situ stress field distribution characteristics and lithology influence of underground roadway in western Guizhou mining area
Core Problem: The lack of thorough understanding of in-situ stress field distribution characteristics and the specific influence of lithology on these stresses, which are critical for safe design and disaster prevention in underground roadways.
Key Innovation: Developed a computational mechanical model and a self-designed small-aperture hydraulic fracturing system to measure in-situ stress in a mining area, revealing the influence of lithology on stress distribution and providing valuable data for optimizing underground roadway design and support.
155. Machine learning techniques for estimating saturated soil hydraulic conductivity at the watershed scale: advances in pedotransfer functions
Core Problem: The high cost and complexity of directly measuring saturated soil hydraulic conductivity (Ksat) and the limited availability of reliable pedotransfer functions (PTFs) for subtropical soils.
Key Innovation: Developed and evaluated machine learning-based pedotransfer functions (PTFs) for estimating saturated soil hydraulic conductivity (Ksat) in subtropical Brazilian soils, achieving high predictive performance (R²=0.75) with Random Forest and ANN models using readily available soil variables.
156. Machine learning-based uniaxial compressive strength estimation for lignite in an underground coal mine
Core Problem: The time-consuming, costly, and impractical nature of laboratory uniaxial compressive strength (UCS) testing for continuous field applications in mining, despite its criticality for design and disaster prevention.
Key Innovation: Developed a machine learning-based framework for rapid and low-cost estimation of uniaxial compressive strength (UCS) of lignite in an underground coal mine, using easily obtainable Schmidt hammer rebound hardness and point load strength indices, with the ANN model achieving superior predictive accuracy.
157. Influence of joint angles and in-situ stress on blasting effects in layered rock mass
Core Problem: Lack of comprehensive understanding of the complex coupling mechanisms between joint parameters and in-situ stress on blasting outcomes (unbalanced energy distribution, poor forming, over-excavation) in layered rock masses.
Key Innovation: Identified a dual control mechanism where joints guide damage anisotropy (channel effect, V-shaped crack velocity with angle) and in-situ stress controls overall damage extent (confining pressure inhibits crack propagation), revealing a non-linear competition between structural guidance and strengthening effect.
158. Inversion and similarity characterization on the interface of geological formations using conditional random field and image structural similarity
Core Problem: Accurately inverting and characterizing the similarity of Interfaces of Geological Formations (IGF) is challenging due to sparsity and trend characteristics in measurements and the need to mitigate human factors in prior information, which are critical for underground structure construction.
Key Innovation: Developed an IGF inversion algorithm integrating Bayesian-based conditional random field with trend functions to handle sparsity and trend characteristics, and an IGF similarity characterization method combining this algorithm with image structural similarity (SSIM) theory, providing a rational and quantifiable assessment of IGF similarity.
159. Intelligent inversion of industrial solid waste-stabilized subgrade soil based on adaptive optimization strategy
Core Problem: The challenge of efficiently determining the optimal mix ratio for industrial solid waste-stabilized soft soil subgrades due to the need for extensive orthogonal tests.
Key Innovation: Developed an intelligent mixing optimization method (XGBoost-PSO model) that predicts unconfined compressive strength and determines optimal mix ratios for industrial solid waste-reinforced soft soil subgrades, improving resource utilization and guiding roadbed construction.
160. Microstructural Evolution and Fracture Toughness Degradation of Granite under High-Cycle Thermal Shock
Core Problem: Understanding the progressive degradation of granite's microstructure and fracture resistance under high-cycle thermal shock, particularly its implications for enhanced geothermal systems (EGS) stability.
Key Innovation: Quantifying the nonlinear decrease in fracture toughness with thermal cycles, observing crack path evolution, and developing a thermo-mechanical damage model that reproduces the degradation trends and fracture paths, illuminating temperature-dominant damage acceleration and mixed-mode frictional mitigation.
161. Effect of Pulse Injection on Fracture Initiation and Propagation in High-Temperature Granite: Application of Soft Fracturing in Enhanced Geothermal Systems
Core Problem: Conventional hydraulic fracturing in Enhanced Geothermal Systems (EGS) faces challenges like high fracture initiation pressure, simplistic fracture geometry, and potential for induced seismicity.
Key Innovation: Proposed and tested a novel soft hydraulic fracturing (SHF) method with pulse injection, demonstrating its ability to reduce breakdown pressure (10-30%), increase stimulated reservoir volume (129.56-311.59%), and enhance fracture conductivity by nearly an order of magnitude, while also improving fracture complexity and heat transfer efficiency.
162. Comparative Mechanical Behavior of Marine Clay Under Axisymmetric and Plane Strain Conditions
Core Problem: The mechanical behavior (static and dynamic) of marine clay, particularly plastic vertical strain, hysteresis, and resilient modulus, differs significantly under axisymmetric and plane strain conditions, and a unified model to describe this behavior considering different stress states is lacking.
Key Innovation: Experimentally compared the static and dynamic mechanical behavior of marine clay under axisymmetric and plane strain conditions, identifying key differences in strength, plastic vertical strain, hysteresis, and resilient modulus. Developed a unified model for plastic vertical strain that accounts for the effects of strengths in different stress states.
163. Rank aggregation to predict the fundamental frequency of historic masonry towers
Core Problem: Estimating the fundamental frequency of historic masonry towers for seismic vulnerability assessment is challenging due to variability and reliance on single empirical models, leading to unreliable predictions.
Key Innovation: Proposes an original rank aggregation methodology based on the Plackett-Luce model to combine multiple empirical and data-driven predictive models, improving prediction reliability and integrating diverse knowledge sources while reducing individual model errors.
164. Using reduced-order models to simulate inelastic seismic response of base-isolated buildings
Core Problem: Accurately simulating the inelastic seismic response of base-isolated buildings under extreme earthquakes, especially when superstructures may undergo inelastic excursions, which traditional linear elastic assumptions fail to capture.
Key Innovation: Proposed a novel reduced-order model, the base-isolated Generalized Building Model (GBM), consisting of two sticks (shear and flexural), which accurately estimates the peaks and phases of displacement/acceleration histories and plastic hinge distribution in base-isolated buildings under extreme seismic events.
165. Macromodeling and experimental investigation of RC frames infilled with clay bricks and autoclaved aerated concrete blocks under cyclic loading
Core Problem: Understanding and improving the seismic resistance of RC frames with different infill materials, particularly the performance of novel AAC blocks compared to traditional clay bricks, and developing accurate macromodels for their behavior.
Key Innovation: Experimentally demonstrated that AAC infill significantly outperforms brittle clay infill in terms of ductility enhancement for RC frames under cyclic loading, and developed accurate multi-strut macromodels that capture the distinct brittle vs. ductile behavior of different infill types.
166. Finite element analysis of seismic energy dissipation and quantitative damage evaluation in reinforced concrete frame structures
Core Problem: The need for accurate investigation of collapse mechanisms and quantitative damage assessment for reinforced concrete frame structures subjected to seismic events, to mitigate severe casualties and economic losses.
Key Innovation: Established 3D solid finite element models of RC frame structures, verified with pseudo-dynamic tests, to perform elastic-plastic time history analysis, and developed a quantitative damage assessment method based on "energy damage" and "stiffness damage" thresholds for various seismic states.
167. Parametric study of masonry-infilled RC frames with openings: subassembly contributions to shear resistance and the role of vertical confinement
Core Problem: The seismic performance of masonry-infilled RC frames is significantly affected by openings, which are often neglected or simplified, and the role of vertical confinement needs systematic quantification.
Key Innovation: Conducted a systematic parametric investigation using a calibrated micromodelling framework to quantify component-wise shear resistance contributions in infilled RC subassemblies with various opening types and sizes, demonstrating the effectiveness of vertical confinement in delaying infill degradation and enhancing out-of-plane stability.
168. A resilience enhancement approach for interdependent networks incorporating recovery coupling mechanisms
Core Problem: Modern interdependent infrastructure networks are vulnerable to cascading failures from localized disturbances, and existing resilience approaches do not fully account for recovery-coupling effects where disrupted subsystems draw resources from others during restoration.
Key Innovation: An Interdependent Network Resilience Enhancement Framework with Recovery Coupling Mechanisms (INRCM) that uses a fifth-order tensor to model time-varying, two-layer systems, jointly embeds recovery-coupling and cascading-failure dynamics, derives a three-phase structural-resilience metric, and employs a graph neural network-guided genetic algorithm for optimizing recovery node sets.
169. Active learning Kriging with functional dimension reduction for reliability analysis of stochastic dynamical systems
Core Problem: Efficiently and accurately evaluating the reliability (specifically first-passage failure probabilities) of complex, computationally intensive stochastic dynamical systems under various uncertainties, especially with limited computational resources for building high-accuracy surrogate models.
Key Innovation: A novel active learning Kriging method (AKFDR) that constructs surrogate models in a latent functional space obtained through functional dimension reduction, combined with a new learning function incorporating a weighted correlation criterion and an error-based stopping criterion for efficient sample selection and convergence.
170. Nonlinear random walks on hypergraphs characterized by higher-order interactions
Core Problem: Existing random walk models on higher-order networks assume linear relationships between transition probabilities and node properties, which inadequately represent the nonlinear properties of higher-order interactions and thus fail to accurately identify critical nodes or understand system robustness.
Key Innovation: Proposes a nonlinear random walk on hypergraphs that explicitly considers higher-order collaborative structures and nonlinear dynamics by introducing a nonlinear mapping between transition probabilities and node hyperdegrees, demonstrating superior performance in identifying critical nodes and understanding system robustness.
171. User and data-centric artificial intelligence for mapping and benchmarking urban deprivation for a global sample of cities
Core Problem: The lack of reliable geospatial information on the location and extent of deprived urban areas (DUAs) hinders efforts to improve living conditions and support SDG Indicator 11.1.1.
Key Innovation: Develops a User- and Data-centric AI approach, leveraging open Earth Observation and geospatial data with a tailored multi-branch encoder-decoder CNN and an iterative model refinement process, co-designed with local communities, to accurately map DUAs.
172. Improving national forest attribute maps of Sweden with machine learning
Core Problem: Existing modeling approaches for nationwide forest attribute mapping in Sweden could be improved in accuracy and predictive capacity, particularly by leveraging advanced machine learning and spatially correlated airborne laser scanning (ALS) data.
Key Innovation: Application of XGBoost and CNN with aggregated ALS and National Forest Inventory (NFI) data to significantly improve the accuracy of national forest attribute maps in Sweden, demonstrating the benefits of ML and incorporating surrounding spatial information.
173. Modification of RMR rock mass classification system for improved performance prediction of rock TBMs
Core Problem: The traditional Rock Mass Rating (RMR) system, while effective for rock mass stability and tunnel support design, exhibits limited correlation with Tunnel Boring Machine (TBM) performance prediction in hard rock conditions.
Key Innovation: An optimized RMR framework, RMRTBM, is introduced, which refines the RMR system by integrating artificial intelligence algorithms (non-linear regression, decision trees, Random Forest) to adjust parameter ratings and weightings, thereby significantly improving the prediction of TBM performance metrics (FPI, boreability, excavability) and stability conditions along the tunnel route.
174. Long-term monitoring of a high-energy beach using joint salinity-based inversion of different ERT and sampling data
Core Problem: Accurately monitoring and understanding the temporal dynamics of saltwater-freshwater distribution in high-energy beach environments, particularly the deeper salinity dynamics, which is challenging with traditional independent data analysis.
Key Innovation: A joint salinity-based inversion approach that integrates continuous porewater salinity logging, direct-push measurements, electrode chain data, and ERT measurements, directly inverting for salinity and deriving a formation factor model, providing 2D images of seasonal changes in subterranean estuaries.
175. Flocculation dynamics of suspended particulate matter under various tidal conditions
Core Problem: Understanding how flocculation dynamics and floc size distributions of suspended particulate matter vary under different tidal conditions (turbulence, salinity) in estuarine and coastal environments.
Key Innovation: Field observations at contrasting sites (turbulent/low-salinity vs. weak/high-salinity) combined with a 1D vertical hydrodynamic and population balance flocculation model to demonstrate how turbulence and salinity govern aggregation and breakup processes, providing insights into sediment transport.
176. Hydrogeology of reservoirs for deep geological storage of saline coal mine drainage in groundwater Basin
Core Problem: The need for safe, long-term disposal of saline coal mine drainage (SCMD) produced by membrane treatment, given the rapid increase in coal mine water discharge.
Key Innovation: Demonstrated the feasibility and effectiveness of deep geological storage (DGS) of SCMD in sandstones sealed by a multilayer caprock system over 18 months, confirming reservoir integrity and no hydraulic connection with shallow groundwater, and showing machine/deep learning methods outperform classical ARIMA for injection pressure prediction.
177. Evaluating saturated hydraulic conductivity schemes: impacts on soil moisture simulations and soil texture-dependent applicability
Core Problem: Existing saturated hydraulic conductivity (Ksat) estimation schemes have not been thoroughly evaluated for their impact on soil hydrology simulations within land surface models (LSMs), and their applicability across different soil textures is unclear.
Key Innovation: Evaluated five Ksat schemes against laboratory measurements and within CLM5.0 for the Tibetan Plateau, identifying scheme performance variability based on soil texture and organic matter, thereby enhancing LSM capability for simulating soil hydraulic processes.
178. Seismic line disturbances affect spatial and temporal patterns of snow accumulation in boreal forests
Core Problem: The effect of seismic line disturbances on wintertime conditions, specifically snow accumulation patterns and melt dynamics in boreal forests, remains poorly understood.
Key Innovation: Used time-lapse photography to investigate the impact of seismic lines on snowpack conditions, revealing statistically significant differences in maximum snow depth, timing of maximum depth, and ablation duration, highlighting implications for soil thermal and hydrologic regimes.
179. Decoding human-climate interactions in China’s water-stressed aquifers: mechanistic modeling approach
Core Problem: The intricate interplay between climate variability and human activities in water-stressed aquifers, particularly regarding groundwater depletion and the efficacy of managed recharge, requires a robust mechanistic modeling approach.
Key Innovation: Developed an integrated groundwater modeling framework combining in-situ monitoring and GRACE/GFO satellite observations to analyze historical evolution and project future trajectories of the Beijing-Tianjin-Hebei aquifer, establishing mechanistic linkages between surface interventions and aquifer responses under various SSPs.