TerraMosaic Daily Digest: Jan 19, 2026
Daily Summary
This digest synthesizes 78 selected papers and focuses on flood generation, routing, and hydroclimatic forcing, landslide process mechanics and slope evolution, high-resolution remote-sensing monitoring workflows. Top-ranked studies examine earthquake-triggered slope response and liquefaction, flood generation and hydroclimatic forcing, and coastal and submarine hazard coupling.
Across the full set, evidence converges on mechanism-constrained analysis with operational relevance, especially for risk, fragility, and resilience quantification and infrastructure-focused hazard performance. The strongest contributions pair interpretable process evidence with monitoring or forecasting workflows that support warning design and risk prioritization.
Key Trends
- Flood analyses are becoming event-specific and process-based: Papers emphasize precipitation structure, antecedent wetness, and catchment controls rather than static hazard descriptors.
- Landslide studies increasingly resolve process chains: Contributions connect triggering conditions, slope deformation, and mobility outcomes, improving the basis for warning thresholds and scenario testing.
- Monitoring workflows rely on integrated remote-sensing products: Multi-source satellite and airborne observations are used for deformation retrieval, change detection, and rapid post-event mapping.
- Risk studies move beyond hazard mapping to consequence pathways: Vulnerability, fragility, exposure, and recovery metrics are integrated to compare interventions under compound hazards.
- Infrastructure-facing outputs are increasingly decision-ready: Asset performance is evaluated with uncertainty-aware frameworks to support mitigation and maintenance prioritization.
Selected Papers
This digest features 78 selected papers from 2,414 papers analyzed across multiple journals. Each paper has been evaluated for its relevance to landslide research and includes links to the original publications.
1. Mountain resonance shapes the distribution of earthquake-induced landslides
Core Problem: Understanding the physical mechanisms governing the spatial distribution and size of earthquake-induced landslides.
Key Innovation: Demonstrates that the resonant frequency of mountains exerts a statistically significant control on the occurrence, spatial pattern, and size of coseismic landslides, highlighting an overlooked mechanism for landslide hazard assessment. This is today's Editor's Choice for its implications for coseismic landslide hazard assessment.
2. Efficient On-Board Processing of Oblique UAV Video for Rapid Flood Extent Mapping
Core Problem: On-board processing of high-resolution oblique aerial video for rapid flood extent mapping is bottlenecked by the strict Size, Weight, and Power (SWaP) constraints of Unmanned Aerial Vehicles (UAVs).
Key Innovation: Proposes Temporal Token Reuse (TTR), an adaptive inference framework capable of accelerating video segmentation on embedded devices by exploiting the intrinsic spatiotemporal redundancy of aerial video.
3. Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model
Core Problem: Accurate wetland mapping is essential for ecosystem monitoring, yet dense pixel-level annotation is prohibitively expensive and practical applications usually rely on sparse point labels, under which existing deep learning models perform poorly.
Key Innovation: Proposes WetSAM, a SAM-based framework that integrates satellite image time series for wetland mapping from sparse point supervision through a dual-branch design, where a temporally prompted branch extends SAM with hierarchical adapters and dynamic temporal aggregation to disentangle wetland characteristics from phenological variability.
4. Assessing Building Heat Resilience Using UAV and Street-View Imagery with Coupled Global Context Vision Transformer
Core Problem: Climate change is intensifying human heat exposure, particularly in densely built urban centers of the Global South. Low-cost construction materials and high thermal-mass surfaces further exacerbate this risk. Yet scalable methods for assessing such heat-relevant building attributes remain scarce.
Key Innovation: Proposes a machine learning framework that fuses openly available unmanned aerial vehicle (UAV) and street-view (SV) imagery via a coupled global context vision transformer (CGCViT) to learn heat-relevant representations of urban structures.
5. PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs
Core Problem: Scalable mapping of Concentrated Animal Feeding Operations (CAFOs) from aerial and satellite imagery.
Key Innovation: Infrastructure-first pipeline using YOLOv8, SAM2 masks, spatial cross-attention classifier, and gradient--activation analyses to link decisions to visible infrastructure.
6. Sensor Placement for Urban Traffic Interpolation: A Data-Driven Evaluation to Inform Policy
Core Problem: Optimizing the placement of traffic sensors for improved traffic volume interpolation in urban areas.
Key Innovation: Benchmarking data-driven strategies for sensor placement based on network centrality, spatial coverage, feature coverage, and active learning, demonstrating improved accuracy with fewer sensors.
7. TriDF: Triplane-Accelerated Density Fields for Few-Shot Remote Sensing Novel View Synthesis
Core Problem: Efficient remote sensing novel view synthesis (NVS) from limited input views.
Key Innovation: A hybrid 3D representation that decouples color and volume density information, models high-frequency color information onto a compact triplane structure, and incorporates depth-guided optimization to mitigate overfitting.
8. A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery
Core Problem: Enhancing the resolution of synthetic aperture radar (SAR) imagery for improved ship target classification.
Key Innovation: A super-resolution framework that integrates classification objectives directly into the super-resolution process, optimizing loss functions for both image quality and classification performance.
9. Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Complex Catastrophic Slope Failure
Core Problem: Early and accurate identification of failure zones in landslide-prone areas is crucial for effective geohazard mitigation, but existing approaches often fall short in capturing both the spatial correlations and temporal dynamics.
Key Innovation: A novel approach that jointly incorporates spatial and temporal information, enabling the detection of complex landslides and including multiple successive failures occurring in distinct areas of the same slope, using spatiotemporal LID (stLID).
10. Over 100,000 landslides triggered by typhoon-induced rainfall in North China in July 2023
Core Problem: Extreme rainstorms triggered widespread, clustered landslide disasters in North China, requiring analysis of their spatial distribution, mobility, and influencing factors.
Key Innovation: Identification of 104,555 landslides using remote sensing imagery, analysis of their spatial distribution and mobility, and examination of influencing factors in relation to topography, geology, soil types, and rainfall.
11. Modeling the cumulative impact of river curvature on landslide-induced surge waves: refinement based on the Pan Jiazheng model
Core Problem: Existing models inadequately estimate surge wave magnitude influenced by river curvature, crucial for dam safety and disaster mitigation.
Key Innovation: Introduces a comprehensive computation length (S’) to capture river geometry, improving surge wave magnitude estimation in landslide-induced surge waves (LISW) modeling.
12. Remote sensing-based assessment of albedo changes on benchmark glaciers in the Western Himalaya, India, between 2001 and 2022 using Google Earth Engine: implications for glacier mass loss
Core Problem: Glacier shrinkage in the Himalayas poses risks to regional hydrology, ecosystems, and socioeconomic sectors, necessitating accurate monitoring of factors influencing glacier melt.
Key Innovation: Utilizes Google Earth Engine (GEE) to analyze spatiotemporal variability of glacier albedo using MODIS and Landsat-8 data, correlating albedo changes with geodetic surface lowering to understand glacier behavior.
13. Modeling the cumulative impact of river curvature on landslide-induced surge waves: refinement based on the Pan Jiazheng model
Core Problem: Existing models inadequately estimate surge wave magnitude influenced by river curvature, crucial for dam safety and disaster mitigation.
Key Innovation: Introduces a comprehensive computation length (S’) to capture river geometry, improving surge wave magnitude estimation in landslide-induced surge waves (LISW) modeling.
14. Ongoing geological processes on the flanks of active volcanoes. A multi-scale geomorphological approach applied to the La Fossa Caldera (Vulcano Island, Italy)
Core Problem: Understanding erosional processes shaping submarine volcanic flanks for geohazard assessments.
Key Innovation: Multi-temporal, multi-scale geomorphological approach integrating bathymetry and seafloor videos to analyze slope failures and rockfalls.
15. A self-organized criticality model of extreme events and cascading disasters of hub-and-spoke air traffic networks
Core Problem: Modeling cascading failures in critical infrastructure networks, particularly air transportation, due to extreme weather events.
Key Innovation: Application of Self-Organized Criticality (SOC) theory to model and analyze cascading failures in hub-and-spoke air traffic networks.
16. A large-scale seismic risk approach accounting for local site effects and modelling of building exposure based on open-access datasets
Core Problem: Improving regional seismic risk assessment by accounting for site effects and refining building exposure models.
Key Innovation: Integration of litho-stratigraphic site effects and high-resolution building height estimates using open-access spatial datasets to enhance macroseismic risk assessment.
17. Mapping seismic risk of existing highway bridges at a regional scale using Artificial Neural Networks
Core Problem: Efficiently assessing seismic risk of highway bridges for large-scale evaluations.
Key Innovation: Development of an ANN-based predictive model for seismic risk, integrated with models for degradation and traffic-related structural risk, and mapped using GIS.
18. Earthquake-hazard exposure of residents with potential access and functional needs in the United States
Core Problem: Addressing the gap in earthquake response plans for individuals with potential access and functional needs (AFN).
Key Innovation: Integrating spatially explicit data for AFN-related residential populations with earthquake hazards to identify vulnerable populations.
19. High-resolution annual desertification mapping in northern China using a novel comprehensive desertification index and unsupervised algorithm
Core Problem: Existing desertification monitoring methods lack clear remote sensing mechanisms and robust extraction methods, hindering accurate large-scale assessment.
Key Innovation: A comprehensive desertification index (CDI) integrating multisource remote sensing data (Sentinel-1/2) with a Gaussian mixture model (GMM) for automated 10 m-resolution annual desertification mapping.
20. Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana)
Core Problem: Characterizing hydrological dynamics of flood-pulsed wetlands is challenging due to significant seasonal water fluctuations.
Key Innovation: Using Sentinel-1 InSAR coherence time series to map flood pulse seasonality and extent, relating coherence patterns to land cover and flood frequency.
21. Automated TLS multi-scan registration in forest environments: A novel solution based on hash table
Core Problem: Efficiently and accurately aligning multiple terrestrial laser scans (TLS) in forest environments is challenging due to the density of vegetation and lack of clear features, hindering comprehensive 3D forest structure analysis.
Key Innovation: A novel automated TLS multi-scan registration algorithm, HashReg, utilizes a hash table for efficient stem mapping, coarse parameter estimation, factor graph optimization, and fine-tuned registration, achieving high accuracy and speed in diverse forest conditions.
22. Combining single-date mobile and multitemporal airborne laser scanning for retrospective estimation of individual tree growth over a 10-year period in boreal forests
Core Problem: Estimating individual tree growth accurately and scalably is difficult due to slow growth rates, sensor limitations, and the limited availability of historical stem-level data.
Key Innovation: A framework combines single-date mobile laser scanning (MLS) with multitemporal airborne laser scanning (ALS) to retrospectively estimate 10-year tree growth, using MLS for current stem curves and ALS-derived height growth factors to model past attributes, eliminating the need for historical MLS data.
23. Utilising Mobile Laser Scanning Point Clouds to Assess Harvesting Quality in Thinning Stands
Core Problem: Traditional methods for assessing harvesting quality in forestry are manual, time-consuming, and prone to human error.
Key Innovation: Mobile laser scanning (MLS) is used to measure harvesting attributes, assess the impact of thinning density assistants, and detect defective tree stems, providing an objective and data-driven approach to forest management.
24. Impact of plateau pika burrows on soil water infiltration: Insights from controlled experiments and numerical simulation
Core Problem: High density plateau pika activity reforms soil properties and affects hydrological processes, specifically preferential flow, but the ecohydrology of pika burrows is not well understood.
Key Innovation: Sandbox experiments and numerical simulations (COMSOL) to explore the influence of burrow structural parameters (diameter, depth, bottom length) and burrow density on water infiltration, confirming preferential flow in rodent burrows.
25. Response of hydrological processes to event- and annual-scale precipitation extremes in a rocky mountainous area of northern China
Core Problem: Increasing frequency of heavy rainfall and drought events due to uneven precipitation distribution poses significant challenges to ecosystem hydrology, and the short- and long-term reactions across multiple temporal scales remain inadequately quantified.
Key Innovation: Field runoff plots and soil moisture monitoring to investigate the effects of extreme rainfall events on hydrological processes (P, R, ET, SWC) in forest and grass ecosystems within a rocky mountainous area, revealing distinct hydrological responses between the two.
26. Experimental and numerical study on dynamic behavior of shield tunnels with internal structures under internal explosions
Core Problem: Assessing the blast resistance of shield tunnels with internal structures, which is crucial due to increasing threats from explosions.
Key Innovation: Combined experimental blast tests on a large-scale tunnel model with high-fidelity finite element modeling to investigate dynamic response and failure modes.
27. Uncertainty-aware geological prediction in TBM tunneling: A probabilistic bayesian framework with exploratory multi-source label construction
Core Problem: Deep learning models for TBM geological prediction lack reliability under unknown conditions and struggle with data heterogeneity.
Key Innovation: Developed an uncertainty quantification framework integrating a Gaussian Mixture Model (GMM) with a Probabilistic Bayesian Convolutional Neural Network (PBCNN) for robust geological identification.
28. A knowledge-data dual-driven framework for predicting surrounding rock classification in TBM tunneling and generalization analysis
Core Problem: Accurate real-time prediction of surrounding rock classification (SRC) in TBM operations is critical, but machine learning models lack generalization in practice.
Key Innovation: Proposed a knowledge-data dual-driven SRC prediction framework (KD-SRC) based on rock-machine interaction principles, achieving high accuracy and cross-project applicability.
29. Centrifuge modelling of dewatering-excavation effects on overlying and adjacent large-diameter shield tunnels
Core Problem: Foundation pit dewatering and excavation in groundwater-rich strata severely threatens adjacent large-diameter shield tunnels.
Key Innovation: Developed a novel testing device to simulate sequential dewatering, excavation, and backfilling while dynamically controlling groundwater levels in centrifugal model tests.
30. Experimental investigation on permeability of sand-foam mixture under soil pressure in mechanized tunnelling
Core Problem: The seepage behaviour of excavated sand becomes increasingly complex due to foam injection in mechanized tunneling.
Key Innovation: Examined the impact of soil pressure on the hydraulic conductivity of sand-foam mixtures through a newly developed permeameter.
31. Experimental and numerical study on the anti-impact performance of fluted tapered tube
Core Problem: Rockbursts present a significant challenge in underground resource extraction and infrastructure development, requiring improved energy absorption in hydraulic supports.
Key Innovation: Proposed a fluted tapered tube energy absorption component connected to the column, designed to trigger stable, axisymmetric buckling for efficient energy absorption.
32. Mind the sump: Solution mining-based reconstruction of sediment-filled regions for improved stability modeling of salt caverns
Core Problem: Stability assessments of salt caverns for Underground Energy Storage (UES) are compromised by neglecting sediment-occupied space (sump).
Key Innovation: Employed solution mining simulation to reconstruct sediment-occupied space, integrated with sonar-mapped data for comprehensive cavern models.
33. Influence of rock creep on the performance of lined caverns under cyclic pressurization and hydrogen embrittlement
Core Problem: Evaluating the long-term stability of lined rock caverns (LRCs) for underground hydrogen storage, considering rock creep and steel degradation.
Key Innovation: Developed a two-dimensional multiscale model capturing interactions between the LRC structure and fractured rocks, integrating rock viscoelasticity and steel hydrogen embrittlement mechanisms.
34. Temporal psychological and physiological responses to 120-hour isolation in an underground rescue chamber: A preliminary study
Core Problem: Understanding human psychological and physiological responses to prolonged confinement in underground rescue chambers.
Key Innovation: Conducted a 120-hour simulated experiment to investigate how exposure duration interacts with individual characteristics to influence human responses.
35. Integrating non-stationarity into extreme rainfall risk assessment: A GAMLSS-based framework for large-scale region
Core Problem: Neglecting non-stationarity of extreme rainfall in disaster risk assessment may lead to an underestimation of potential risks.
Key Innovation: A framework incorporating non-stationarity into potential risk assessment of extreme rainfall using GAMLSS.
36. Improved flash drought forecasting and attribution: A spatial-temporal causality-aware deep learning approach
Core Problem: Flash droughts pose significant challenges to water resource management and agricultural sustainability, making it imperative to improve their predictability to mitigate potential risks.
Key Innovation: A deep learning framework that integrates a spatial–temporal causality-aware module into a CNN-LSTM hybrid architecture to enhance flash drought prediction.
37. Multi-objective optimization of green-grey infrastructure with comprehensive consideration of spatial layout and parameter configuration
Core Problem: Rapid urbanization and climate change have intensified rainy-season surface runoff and peak flow, exacerbating urban flooding.
Key Innovation: A multi-objective optimization framework integrating the Storm Water Management Model (SWMM) with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize green-grey infrastructure.
38. From little things big things flow: the drivers of flood risk adaptation
Core Problem: Climate change and the urbanization of floodplains are intensifying flood risk internationally, driving demand for adaptation strategies and socio-hydrological models to more accurately capture human agency.
Key Innovation: Analysis of longitudinal, mixed-methods data that was collected during initial and follow-up engagements with 641 households across two flood-prone regions to reveal that previous adaptive actions are the strongest predictor of subsequent adaptation.
39. Evolution of streamflow intensity, regional synergistic patterns, and driving mechanisms of the Yangtze River
Core Problem: Driven by global climate change and human activities, understanding the spatiotemporal evolution of streamflow structure is essential for effective water resources regulation and risk management.
Key Innovation: A multi-class streamflow contribution series is constructed using an equal-quantile method, and principal component analysis is applied to detect seasonal regional coherence.
40. Steeper spatiotemporal distribution of extreme precipitation intensity in urban than rural regions
Core Problem: Urban flooding is highly sensitive to the organization of precipitation—its intensity, timing, spatial extent, and concentration. However, it remains unclear how urbanization alters these structure-related characteristics of extreme precipitation across time and space.
Key Innovation: Examine the urbanization-induced asymmetric spatiotemporal reorganization of extreme precipitation during the rainy season.
41. Addressing class imbalance extends the performance frontier of classification–regression satellite-gauge precipitation fusion
Core Problem: The classification–regression framework for precipitation fusion has been widely applied to enhance the spatiotemporal accuracy of precipitation estimates. However, existing frameworks often overlook the sample imbalance among different precipitation types, thereby limiting further improvements in fusion accuracy.
Key Innovation: A classification–regression satellite-gauge precipitation fusion framework that explicitly addresses sample imbalance.
42. Prediction of mine water inflow from multi-source recharge: based on multi-scale time-frequency features and lag mechanism
Core Problem: Accurate prediction of mine water inflow volume is a core technical aspect in mining construction and development, with strategic significance for ensuring construction safety and optimizing resource allocation. Traditional forecasting methods for multi-source recharge mine water inflow predictions face two major limitations: first, the linear superposition assumption fails in multi-source recharge scenarios, leading to significant prediction bias; second, the lag effects of different aquifers are not taken into account, resulting in reduced prediction reliability.
Key Innovation: A nonlinear prediction model considering the dynamic coupling of multiple water sources and lag effects is proposed.
43. Improved $\varepsilon$-constrained and adaptive hybrid crossover operator-based NSGA-III for reservoir multi-objective ecological operation
Core Problem: Conventional multipurpose reservoir operational paradigms focus on power generation rather than the ecological integrity.
Key Innovation: This study proposed an integrated multi-objective ecological operation model for reservoirs comprehensively considering power generation, flood control, ecological flow maintenance, and hydrological process requirements for fish reproduction.
44. Statistical Postprocessing of Subseasonal Cumulative Precipitation Forecasts Using a Spatial Heterogeneity-aware U-Net
Core Problem: Accurate and reliable cumulative precipitation forecasts at subseasonal-to-seasonal scales (S2S) are vital for reservoir scheduling and water resources management. Traditional statistical postprocessing methods reduce systematic biases but are constrained by parametric assumptions and limited flexibility.
Key Innovation: We propose a spatial heterogeneity–aware framework that augments U-Net with three complementary inputs: (1) a localized network for location-specific correction patterns, (2) a learnable input channel encoding large-scale atmospheric circulation, and (3) a learnable geographic input encoding local stationary features.
45. An LSTM network for joint modeling of streamflow and hydropower generation for run-of-river plants
Core Problem: Historical records from such plants are often limited, and typically only contain hydropower generation data, which are truncated at the plants’ capacity limits and therefore do not capture high-flow conditions.
Key Innovation: The proposed LSTM model improves predictions in data-sparse and ungauged catchments, and for high-flow conditions, by learning from both hydropower generation data from ROR plants and streamflow data from other Norwegian catchments.
46. Integrating dynamic land surface processes and machine learning into a hydrological modeling framework: application to the Yellow River Basin
Core Problem: Under global warming and continuous vegetation greening, hydrological processes in river basins exhibit stronger nonlinearity and spatial heterogeneity, posing challenges for traditional models.
Key Innovation: This study systematically improved Water Balance Model Developed by Research Center on Climate Change (RCCC-WBM) by incorporating Normalized Difference Vegetation Index (NDVI) and refined snow and permafrost processes, developing the Dynamic Water Balance Model (RCCC-DWBM).
47. Multi-task deep learning for spatiotemporal reconstruction of groundwater dynamics in the North China Plain
Core Problem: Accurate prediction of groundwater spatiotemporal dynamics is critical for sustainable groundwater resource management.
Key Innovation: This study presents a novel Multi-Task Learning Groundwater Model (MTLGW), which integrates time-series decomposition with GRU neural networks to reconstruct regional-scale groundwater levels across the North China Plain (NCP), including data-scarce regions.
48. A graph neural network embedded with heat kernel for multistep forecasting spring discharge
Core Problem: Groundwater from karst aquifers supplies freshwater for 25% of the world population. High-accuracy modeling of the karst hydrological process is vital in water resources planning, management and development.
Key Innovation: This paper develops a graph neural network (GNN) embedded with a heat kernel (HK) model to depict rainfall-runoff converging and groundwater diffusing processes and finally realize spring discharge modeling.
49. Artificial neural network-based surrogate models for predicting multiple seismic slope engineering demand parameters derived from material-point analyses
Core Problem: Existing slope stability models are mesh-based and cannot model post-failure large deformation, restricting the derivation of EDPs like run-out distance and sliding volume.
Key Innovation: Developed ANN-based surrogate models using data from material point method (MPM) simulations to efficiently predict multiple seismic slope EDPs, accounting for strain softening and large deformations.
50. Stochastic collocation enhanced radial-circumferential importance sampling method for efficiently estimating rare failure probability with high-dimensional inputs and its application in slope structures
Core Problem: Complex reliability problems involving high-dimensional random inputs and rare failure events in slope stability analysis are computationally expensive.
Key Innovation: A stochastic collocation enhanced radial-circumferential importance sampling method is proposed to efficiently explore the rare failure domain and estimate failure probability with reduced variance.
51. An adaptive multiscale cohesive phase-field method for hydraulic fracturing in cleat-developed coal seams
Core Problem: Traditional damage mechanics models fail to simultaneously describe fracture evolution across different scales.
Key Innovation: An adaptive cohesive phase-field model based on the multiscale finite element framework is proposed.
52. Practical guidelines for connecting critical states, strengths and dilatancies of scalped and natural soils
Core Problem: Soil laboratory testing often requires the removal of oversized particles to satisfy specimen-size limitations, resulting in a modified particle size distribution (PSD) that can significantly influence the measured shear strength and volume-change response.
Key Innovation: This study presents a physically based framework, grounded in fractal theory, for relating the critical state line (CSL) of a scalped soil to that of the original field material.
53. Stochastic collocation enhanced radial-circumferential importance sampling method for efficiently estimating rare failure probability with high-dimensional inputs and its application in slope structures
Core Problem: Complex reliability problems involving high-dimensional random inputs and rare failure events in slope stability analysis are computationally expensive.
Key Innovation: A stochastic collocation enhanced radial-circumferential importance sampling method is proposed to efficiently explore the rare failure domain and estimate failure probability with reduced variance.
54. Artificial neural network-based surrogate models for predicting multiple seismic slope engineering demand parameters derived from material-point analyses
Core Problem: Existing slope stability models are mesh-based and cannot model post-failure large deformation, restricting the derivation of EDPs like run-out distance and sliding volume.
Key Innovation: Developed ANN-based surrogate models using data from material point method (MPM) simulations to efficiently predict multiple seismic slope EDPs, accounting for strain softening and large deformations.
55. Assessment of pavement–subgrade deformation in permafrost highways using UAV photogrammetry and ground-penetrating radar: Case study of Qinghai–Tibet highway
Core Problem: Permafrost-related deformation of highway embankments is a major constraint on the long-term serviceability. Freeze–thaw cycles, water migration and heavy traffic loads produce rutting, corrugation and differential settlement.
Key Innovation: Integrates UAV photogrammetry with ground-penetrating radar (GPR) to examine coupled pavement–subgrade behaviour. UAV quantifies rut depth, roughness, and elevation differentials, while GPR identifies stratigraphic undulations and the permafrost table.
56. A rapid and nondestructive detection method for concealed accumulated water beneath airport pavement using hydrothermal parameter variations
Core Problem: Detecting concealed accumulated water beneath airport pavement is a challenge due to limited detection time and nondestructive requirement.
Key Innovation: A rapid and nondestructive detection method was proposed based on pavement surface temperature variations, which result from differences in the specific heat capacity and thermal conductivity of subgrade soils with varying water content.
57. Liquefaction Characteristics and Damage Evolution of Rapid and Long-traveling Landslides
Core Problem: Understanding the causes of rapid and long-traveling landslides, which pose significant threats due to their high velocities and large scales.
Key Innovation: Investigation of the shear mechanical behavior and liquefaction mechanism of landslide soil through undrained ring shear tests, development of a statistical damage constitutive model using damage theory and the Weibull distribution function, and numerical restoration of the landslide process.
58. Probability density contour-based slope reliability analysis involving implicit limit state functions
Core Problem: Accurate and efficient reliability analysis for slope stability, especially when dealing with complex slope problems and implicit limit state functions.
Key Innovation: Development of a practical probability density contour-based reliability method (CBRM) that incorporates a discrete grid model and vine copula theory to handle complex slope reliability problems with implicit limit state functions, offering an alternative to traditional sampling methods.
59. Probability density contour-based slope reliability analysis involving implicit limit state functions
Core Problem: Accurate and efficient reliability analysis for slope stability, especially when dealing with complex slope problems and implicit limit state functions.
Key Innovation: Development of a practical probability density contour-based reliability method (CBRM) that incorporates a discrete grid model and vine copula theory to handle complex slope reliability problems with implicit limit state functions, offering an alternative to traditional sampling methods.
60. Field experiments and numerical simulation study of the five-step single-side drift method for large-section loess tunnels
Core Problem: Challenges in constructing large-section loess tunnels due to low rock strength, loose structure, and water-induced softening, leading to crown settlement and stress concentration.
Key Innovation: Integrating field experiments with finite element simulations to compare different construction methods, demonstrating the effectiveness of the Five-step Single-side Drift Method (FSSDM) in redistributing stress, reducing settlement, and improving structural stability.
61. Prediction of continuous–discontinuous failure of granite subjected to direct shearing using acousto-optical-mechanical scheme
Core Problem: Predicting catastrophic failure of deep rocks, which exhibits a continuous–discontinuous behavior.
Key Innovation: Developing a multimodal acoustic–optical–mechanical (AOM) monitoring system integrating acoustic emission (AE) analysis, digital image correlation (DIC), and mechanical loading to investigate AOM responses during shear rupture and establish an AOM-based framework to characterize and model coupled failure.
62. Stability of base-exposed backfill roof considering interfaces between adjacent drifts in underhand drift-and-fill mining
Core Problem: Ensuring the stability of base-exposed backfill roof in underhand drift-and-fill mining, considering the weakening effect of interfaces between adjacent filled drifts.
Key Innovation: Investigating failure modes and strength requirements of backfill roof in aligned and staggered drifts by FLAC3D, with a focus on varied shear parameters of the interfaces, to optimize drift layouts and perform cost-effective backfill roof strength design.
63. The contribution of a surge event to infilling in a barrier‐enclosed estuary: Insights from field observations
Core Problem: Understanding sediment transport pathways and loading in constricted, sheltered estuaries during extreme weather events.
Key Innovation: Observational dataset capturing the impact of a storm surge on sediment transport pathways in a sheltered estuary, revealing a sixfold increase in sediment import and a 600-fold increase in sediment flux to the upper estuary.
64. Fluvial deposits of the Ahr river (western Germany) reveal recurring high‐magnitude flood events over the last 1,500 years
Core Problem: Reconstructing past high-magnitude flood events to improve flood hazard assessments in Central Europe.
Key Innovation: Chemo- and lithostratigraphical analysis of Ahr flood deposits, revealing that centennial- to millennial-scale high-energy flood deposits are not the exception but the rule, documenting at least three other flood events in the last 1,500 years with comparable magnitude to the 2021 flood.
65. Econometric modelling for estimating direct flood damage to firms: a micro-scale approach using post-event records in Italy
Core Problem: Estimating flood damage to businesses is crucial for risk management but requires detailed micro-scale data and appropriate modeling techniques.
Key Innovation: Uses econometric modeling with micro-scale damage records to predict economic damage based on business size, water depth, and economic sector, identifying sector-specific vulnerabilities.
66. Dynamic analysis of drought propagation in the context of climate change and watershed characterization: a quantitative study based on GAMLSS and Copula models
Core Problem: Understanding how climate change and watershed characteristics influence drought propagation is essential for effective drought management and mitigation.
Key Innovation: Employs GAMLSS and Copula models to analyze drought spread, quantifying the impact of climate change and watershed characteristics on drought propagation probabilities and thresholds.
67. Flocculated Silt, Not Clay, Dominates River Suspended Sediment Load
Core Problem: River mud's settling velocity, crucial for floodplains and carbon burial, is poorly understood regarding grain size and flocculation.
Key Innovation: Demonstrates that flocculated silt, not clay, dominates river sediment load, driven by physical trapping and organic matter binding rather than clay mineral flocculation.
68. More Water, More of the Time: Spatial Changes in Flooding Over 83 Years in the Upper Mississippi River Floodplain and Relationships With Streamgage‐Derived Proxies
Core Problem: Changes in the hydrologic regime of the Upper Mississippi River (UMR) have led to greater discharges and longer-lasting high-flow conditions, but the spatial expression of these changes as floodplain inundation is not well understood.
Key Innovation: Assesses changes in floodplain inundation characteristics from 1940 to 2022 using a geospatial model to simulate daily inundation depths, revealing significant shifts in inundation characteristics across pools and highlighting the importance of spatially explicit modeling.
69. The Relative Importance of Floodplain Storage and Flow Path Dispersion on Flood Attenuation in Mountain Streams
Core Problem: Intensifying flooding in the western United States threatens infrastructure and human life, and the degree to which floodplains can reduce peak flows is not well constrained.
Key Innovation: Quantifies flood attenuation provided by floodplains in mountain streams using two-dimensional hydrodynamic models, finding high discharge attenuation and identifying storage in floodplain depressions and flow path diversity as key mechanisms.
70. Rain-on-wet-soil compound floods in lowlands: the combined effect of large rain events and shallow groundwater on discharge peaks in a changing climate
Core Problem: Flood severity in lowlands is determined by rainfall and groundwater depth, with climate change exacerbating the issue.
Key Innovation: Investigated the trade-off between rainfall and groundwater depth and its effect on river discharge peaks under current and future climate scenarios, finding increased flood risk in winter and spring.
71. Emerging hotspots of agricultural drought under climate change
Core Problem: Agricultural drought is increasing due to changing soil moisture patterns.
Key Innovation: Identified regions in Europe, southern Africa, northern South America, and western North America as emerging hotspots for agricultural drought, projecting persistence until 2100 based on climate reanalyses and model simulations.
72. Integrated seismic damage scenario and resilience assessment using multimodal data and support vector machine, a case study of Jiangyou City, Southwestern China
Core Problem: Efficient seismic vulnerability and resilience evaluation is needed for disaster prevention and urban planning, but traditional in-situ fieldwork is resource-intensive and costly at large scales.
Key Innovation: An integrated geospatial framework using support vector machine (SVM) and multimodal data for efficient seismic vulnerability and resilience evaluation with minimal fieldwork.
73. Aggregated flood susceptibility mapping in Upper Chao Phraya River Basin using Shannon’s Entropy, Machine Learning, and Stacking ensemble methods
Core Problem: Flood susceptibility mapping in large and heterogeneous basins requires methods capable of representing spatial variability that conventional basin-wide models often overlook.
Key Innovation: A sub-basin aggregation framework integrating localized susceptibility modelling into a unified basin-scale product, using Shannon’s Entropy and machine learning algorithms.
74. Characterising the high spatial heterogeneity of urban flood resilience under extreme rainfall using mobile signal data
Core Problem: Characterisation of the spatial heterogeneity of urban flood resilience (UFR) under extreme rainfall events can support decision-making in urban planning and emergency management, but traditional methods face challenges in capturing urban operational efficiency.
Key Innovation: A new framework of UFR heterogeneity that utilizes mobile signal big data as a dynamic metric for urban operational efficiency and a spatial attention-based machine learning method to attribute the heterogeneity of UFR.
75. A framework of evidence-based multi-hazard risk screening for disaster risk reduction and resilience planning
Core Problem: Multi-hazard risk assessment is essential for informed decision-making in disaster risk reduction (DRR), but conducting fine-scale disaster risk assessments for a large number of elements at risk remains challenging.
Key Innovation: A bottom-up, evidence-based framework for multi-hazard risk screening that develops a risk index system for individual elements at risk and statistically aggregates these indices to generate risk profiles at various administrative levels.
76. Diversity and differences of flood evacuation strategies among rural households in the Hengduan Mountains, Southwest China
Core Problem: Flash floods are among the most destructive hazards in mountainous regions, where their sudden onset and spatial heterogeneity increase uncertainties in household evacuation strategies.
Key Innovation: Integration of rural household surveys with GIS-based terrain classification to examine evacuation strategies across valleys, hills, and mountains, using binary logistic regression and scenario simulations.
77. The Sequential Joint-Scatterer InSAR for Sentinel-1 Long-Term Deformation Estimation
Core Problem: Efficiently processing large stacks of SAR images for long-term deformation monitoring with high accuracy is computationally intensive, especially in areas with complex terrain.
Key Innovation: A sequential-based Joint-Scatterer InSAR (S-JSInSAR) method is proposed, dividing large SLC stacks into flexible batches and integrating results for efficient long-term deformation estimation, mitigating decorrelation effects and reducing processing time.
78. Static Stress Transfer and Fault Interaction Within the 2008–2020 Yutian Earthquake Sequence Constrained by InSAR-Derived Slip Models
Core Problem: Understanding fault interactions in transtensional tectonic settings to assess seismic hazards, particularly in regions with complex local stress fields.
Key Innovation: Using Sentinel-1 InSAR data to derive the coseismic deformation and slip model of the 2020 Mw 6.3 Yutian earthquake, and incorporating published slip models for previous events to analyze static Coulomb stress transfer and fault interactions.