TerraMosaic Daily Digest: May 31, 2026
Daily Summary
The May 31 literature points to a more process-resolved treatment of landslide hazard. Several papers move beyond static susceptibility by linking initiation, runout, post-seismic renewal, hydrologic triggering, and infrastructure interaction within the same decision frame. The strongest contributions are not simply higher-accuracy classifiers: they make slope failure more measurable in time, from atmospheric-river subsurface wetting and GNSS deformation networks to multi-earthquake inventories and rapid optical landslide mapping. This gives the issue a clear scientific center: landslide prediction is being recast as a coupled monitoring and dynamics problem rather than a map-only exercise.
The broader geohazard papers reinforce the same shift. Flood, liquefaction, rockburst, tunnel-slope, and freeze-thaw studies emphasize uncertainty, coupled forcing, and physically interpretable machine learning. Remote sensing papers contribute faster InSAR phase unwrapping, active-learning segmentation, physics-guided flood mapping, and vision-language grounding for Earth observation, but their value is strongest where sensor outputs are constrained by terrain process, hydrology, or deformation mechanics. Together, the selected work favors transferable physical structure over black-box novelty.
Key Trends
Five movements define the issue: event evolution, hydroclimate forcing, constrained monitoring, coupled infrastructure risk, and uncertainty-exposing transfer methods.
- From susceptibility maps to event evolution: Landslide studies increasingly connect where failure may occur with how it initiates, propagates, reactivates, and terminates. Post-seismic inventory analysis, start-to-stop runout mapping, long-runout probabilistic modelling, and debris-flow entrainment studies all extend hazard assessment into the time-dependent mechanics of moving mass.
- Hydroclimate forcing is becoming a first-order model component: Atmospheric-river monitoring, climate-extreme susceptibility, loess-slope infiltration tests, and coupled flood-landslide modelling treat water not as a background trigger but as a measurable driver that changes slope strength, failure timing, and downstream exposure.
- Monitoring architectures are moving toward constrained learning: The strongest AI contributions combine data-driven models with topography, sensor topology, deformation physics, or uncertainty estimation. Dynamic graph learning for GNSS networks, Mamba-based landslide mapping, active-learning segmentation, and physics-guided flood mapping all aim to reduce annotation burden while preserving process meaning.
- Infrastructure geohazards are treated as coupled systems: Tunnel-landslide interaction, rainfall-seismic rock-slope stability, railway deformation, soil liquefaction, rockburst, and underground excavation studies show a common direction: infrastructure risk is being assessed through interacting ground, water, dynamic loading, and structural-response pathways rather than isolated failure modes.
- Transfer methods matter when they expose uncertainty: Remote-sensing and geotechnical methods with the clearest cross-domain value quantify uncertainty, accelerate observation, or make hidden state variables observable. Fast InSAR phase unwrapping, Bayesian soil-hydraulic inversion, hyperspectral LiDAR soil retrieval, and vision-language grounding are useful because they can tighten the measurement chain feeding hazard models.
Selected Papers
This issue contains 66 selected papers from 2,368 papers analyzed. The selected papers are ordered by relevance score and emphasize landslide dynamics, multi-hazard modelling, deformation monitoring, hydroclimate forcing, and transferable remote-sensing or geotechnical methods.
1. Evolutionary controls on post-seismic new landslides revealed by multi-earthquake inventories
Core Problem: Post-earthquake landslide inventories often describe event aftermaths but do not isolate how new failures evolve across repeated earthquakes.
Key Innovation: Uses multi-earthquake inventory evidence to identify controls on post-seismic new-landslide generation, improving temporal interpretation of cascading slope hazards.
2. From start to stop: Simple methods for mapping susceptibility to landslide runout and debris-flow inundation
Core Problem: Many susceptibility workflows emphasize initiation zones while leaving runout and inundation poorly represented for operational planning.
Key Innovation: Frames a start-to-stop mapping strategy that connects initiation susceptibility with downstream landslide runout and debris-flow inundation potential.
3. Rapid and robust landslide mapping from optical EO imagery using a mamba-based deep learning framework
Core Problem: Rapid landslide mapping from optical Earth-observation imagery remains constrained by complex terrain, spectral ambiguity, and post-event response timelines.
Key Innovation: Introduces a Mamba-based deep learning framework for robust optical landslide mapping, advancing efficient post-disaster inventory production.
4. Incorporating landslide failure mechanisms into susceptibility mapping under climate extremes: A hybrid NFR-RF approach in the Western Ghats, India
Core Problem: Climate-extreme susceptibility maps can be weak when they treat failure as a purely statistical terrain-label relation.
Key Innovation: Embeds landslide failure mechanisms into a hybrid NFR-RF susceptibility model, linking climate extremes with process-aware slope failure assessment.
5. Dynamic-process-based quantitative hazard assessment for earthquake-induced landslide clusters in the Northern Mountain of Tianshui
Core Problem: Clustered earthquake-triggered landslides are difficult to evaluate with static susceptibility methods alone.
Key Innovation: Develops a dynamic-process-based quantitative hazard assessment for coseismic landslide clusters, strengthening event-scale hazard representation.
6. A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy
Core Problem: Flash-flood and landslide susceptibility are commonly modelled in separate pipelines despite shared terrain and rainfall controls.
Key Innovation: Builds a unified susceptibility framework for both hazards in Liguria, supporting multi-hazard terrain assessment under Mediterranean storm regimes.
7. Domain knowledge constrained dynamic graph learning of GNSS sensor networks for spatio-temporal slope deformation monitoring
Core Problem: GNSS monitoring networks generate spatio-temporal signals whose topology and deformation physics are not fully used by generic sequence models.
Key Innovation: Constrains dynamic graph learning with domain knowledge to model sensor-network deformation patterns for slope monitoring.
8. Post-earthquake landslide hazard evolution: Spatio-temporal analysis of active fault zone in Western Himalayas
Core Problem: Post-earthquake landslide risk changes over time, especially in active fault zones where conditioning and triggering persist.
Key Innovation: Uses spatio-temporal analysis to characterize hazard evolution in the Western Himalayas, linking active tectonics with changing landslide exposure.
9. Detection and classification of active landslides along the Yellow River corridor using InSAR
Core Problem: Large river corridors require systematic identification of actively deforming slopes that may not be visible in optical imagery.
Key Innovation: Uses InSAR to detect and classify active landslides along the Yellow River corridor, improving deformation-based inventory mapping.
10. Probabilistic hazard assessment method for high-altitude long-runout landslides: parameter uncertainty and dual dynamic indicators joint probability model
Core Problem: High-altitude long-runout landslides require hazard estimates that reflect uncertain parameters and dynamic impact indicators.
Key Innovation: Develops a joint-probability assessment using parameter uncertainty and dual dynamic indicators for long-runout landslide hazard.
11. A multiphase flow model for simulating fluidization and basal liquefaction of landslide
Core Problem: Fluidization and basal liquefaction are key to rapid landslide mobility but remain difficult to simulate consistently.
Key Innovation: Introduces a multiphase-flow simulation framework for landslide fluidization and basal liquefaction.
12. Rainfall-Induced failure of unsaturated loess slopes in the Three-River Source Region: an integrated experimental and numerical investigation
Core Problem: Unsaturated loess slopes respond to rainfall through coupled infiltration, suction loss, and mechanical weakening.
Key Innovation: Combines experiments and numerical modelling to explain rainfall-induced loess-slope failure in the Three-River Source Region.
13. Assessment of extreme subsurface hydrologic conditions captured during atmospheric river storms in the San Francisco Bay area (California, USA) with applications to shallow landslide early warning
Core Problem: Early warning for shallow landslides needs direct evidence of subsurface hydrologic extremes during atmospheric-river storms.
Key Innovation: Assesses storm-driven subsurface hydrologic conditions in the San Francisco Bay area and links them to shallow-landslide warning applications.
14. A century of tunnel-landslide interaction: Anatomy of T7 Tunnel, Nurhak-Golbasi (Türkiye)
Core Problem: Long-lived tunnel corridors can interact with slope instability over decades, but the coupled history is rarely reconstructed.
Key Innovation: Analyzes a century-scale tunnel-landslide system, clarifying how underground infrastructure and slope movement co-evolve.
15. Dynamic response characteristics and stability evaluation of high-steep bedded rock slopes containing tunnels under rainfall-seismic coupling effects
Core Problem: High-steep tunnel slopes may fail under coupled rainfall and seismic loading, yet these drivers are often assessed separately.
Key Innovation: Evaluates dynamic response and stability of bedded rock slopes with tunnels under rainfall-seismic coupling.
16. Potential failure scenarios in the Santa Cruz range, Argentina: 20 years after a natural dam collapse
Core Problem: Natural dam-collapse settings can retain evolving slope-failure scenarios long after the initial disaster.
Key Innovation: Reassesses potential failure scenarios in the Santa Cruz range two decades after a natural dam collapse.
17. Deep neural network framework for predicting debris flow entrainment growth rate in diverse terrain conditions
Core Problem: Debris-flow entrainment growth varies across terrain conditions and is difficult to parameterize for hazard modelling.
Key Innovation: Uses a deep neural network framework to estimate entrainment growth rate across diverse terrains.
18. Progressive deformation and post-failure residual displacement of the 16 October 2025 Tongren loess landslide, Qinghai, China: insights for long-term extra-high voltage transmission line monitoring
Core Problem: Critical transmission corridors need long-term understanding of progressive slope deformation and post-failure residual motion.
Key Innovation: Documents the Tongren loess landslide deformation sequence and extracts monitoring implications for extra-high-voltage infrastructure.
19. Assessment of soil liquefaction hazard using the liquefaction potential and severity index in Osmaniye, Türkiye
Core Problem: Urban and infrastructure planning in seismic regions requires liquefaction hazard metrics that capture both likelihood and severity.
Key Innovation: Applies liquefaction potential and severity indices to evaluate soil liquefaction hazard in Osmaniye.
20. Probabilistic liquefaction hazard assessment in urban centres with a non-liquefiable surface layer overlying a liquefiable stratum
Core Problem: Liquefaction hazard in cities can be altered by non-liquefiable surface layers overlying liquefiable strata.
Key Innovation: Develops a probabilistic liquefaction assessment for urban settings that explicitly represents the surface-layer effect.
21. Uncertainty-Aware Label-Efficient Landslide Segmentation in Open-Pit Mines via Transformer Transfer Learning and Active Learning
Core Problem: Open-pit mine landslide mapping faces limited labels and high uncertainty in disturbed terrain.
Key Innovation: Combines transformer transfer learning with active learning to reduce annotation demand and expose uncertainty in landslide segmentation.
22. SBAS-InSAR-Based Monitoring and Hierarchical Spatiotemporal Deep Learning for Subsidence Monitoring and Prediction in Active Mining Areas: A Case Study of the Dexing Copper Mine
Core Problem: Active mines require deformation monitoring that can predict future subsidence rather than only detect past movement.
Key Innovation: Integrates SBAS-InSAR monitoring with hierarchical spatio-temporal deep learning for subsidence prediction.
23. Identification of shallow water flows (SWF) geohazard in the deep-water southern Caspian basin, Iran: application of geophysical attributes and prediction of S-wave velocity log
Core Problem: Shallow water flows pose deep-water development hazards but are difficult to identify consistently from sparse offshore data.
Key Innovation: Combines geophysical attributes with S-wave velocity prediction to identify SWF geohazards in the deep-water southern Caspian basin.
24. Reconstructing 90 years of urban occupation and mass movement dynamics in Campos do Jordão (SP), Brazil: insights from ERT and FDEM methods
Core Problem: Urban expansion on unstable terrain requires historical and geophysical evidence of mass-movement dynamics.
Key Innovation: Combines long-term urban reconstruction with ERT and FDEM geophysics to interpret mass movement dynamics.
25. Tsunamis in the Laptev Sea caused by earthquakes: assessing worst-case scenarios
Core Problem: Arctic tsunami hazard from mid-ocean ridge earthquakes is underconstrained compared with lower-latitude margins.
Key Innovation: Assesses worst-case earthquake-generated tsunami scenarios in the Laptev Sea, extending hazard evaluation into an Arctic setting.
26. Flood risk zonation and agricultural vulnerability assessment using AHP and land use analysis in the Burhi Gandak basin in Lower Gangetic Plains, India
Core Problem: Agricultural flood vulnerability needs spatially explicit risk zonation that can support basin-scale planning.
Key Innovation: Combines AHP and land-use analysis to map flood risk and agricultural vulnerability in the Burhi Gandak basin.
27. Probabilistic flood hazard and exposure assessment using hydraulic modelling and geospatial analysis: insights from Golapganj Upazila, Sylhet district, Bangladesh
Core Problem: Flood planning needs probabilistic hazard and exposure estimates that combine hydraulic dynamics with geospatial exposure.
Key Innovation: Integrates hydraulic modelling and geospatial analysis for probabilistic flood hazard and exposure assessment in Bangladesh.
28. Extreme flood events as a neglected natural hazard for marginalized Roma communities of Slovakia
Core Problem: Extreme flood events can remain under-recognized hazards for marginalized communities with high exposure and limited adaptation support.
Key Innovation: Examines extreme flood exposure among Roma communities in Slovakia, connecting social marginalization with neglected flood-risk management.
29. A physics-guided and interpretable machine learning framework for rapid flood inundation mapping in complex flood retention areas
Core Problem: Rapid flood-inundation mapping in complex retention areas requires models that remain physically plausible and interpretable.
Key Innovation: Combines physical guidance with interpretable machine learning for fast inundation mapping in complex flood-storage systems.
30. Deep Learning for Remote Sensing to Improve Flood Inundation Mapping
Core Problem: Remote-sensing flood products need more reliable deep-learning approaches for inundation detection across scenes.
Key Innovation: Reviews or develops deep-learning remote-sensing strategies to improve flood inundation mapping performance.
31. Rainstorm Disaster Risk Assessment in the Yangtze River Basin with Fengyun Satellite Precipitation Products
Core Problem: Rainstorm disaster risk assessment needs satellite precipitation products that can support basin-scale hazard evaluation.
Key Innovation: Uses Fengyun satellite precipitation products to assess rainstorm disaster risk across the Yangtze River Basin.
32. Curved Megathrust Geometry and Locking Heterogeneity Contributed to the Rupture of the 2025 Mw 8.8 Kamchatka Earthquake: Insights from InSAR and GPS Joint Inversion
Core Problem: Megathrust rupture interpretation requires resolving geometry and locking heterogeneity from geodetic observations.
Key Innovation: Combines InSAR and GPS inversion to connect curved megathrust geometry and locking heterogeneity to the 2025 Kamchatka earthquake.
33. A Rockburst Hazard Assessment Framework for Strainbursting
Core Problem: Strainbursting hazards in underground rock engineering need structured assessment frameworks.
Key Innovation: Proposes a rockburst hazard assessment framework focused on strainbursting mechanisms.
34. Influence of Fracture Geometry Parameters on the Mechanical Properties and Spatiotemporal Crack Evolution in Granite: Insights from Integrated AE and DIC Monitoring
Core Problem: Rock-slope and underground stability depend on fracture geometry, but strength and failure processes are hard to observe directly.
Key Innovation: Integrates AE and DIC monitoring to connect fracture geometry with granite mechanical properties and spatiotemporal crack evolution.
35. Research on the Application of Moment Tensor Inversion Method Based on the Improved PSO in the Mechanism of Rock Mass Fracture in Mines
Core Problem: Metal-mine rock mass failure requires interpreting fracture evolution from microseismic source mechanisms.
Key Innovation: Applies moment tensor inversion with improved particle swarm optimization to infer mine rock-mass fracture mechanisms.
36. Enhancing landslide susceptibility modelling based on hybrid machine learning approaches and optimization of mapping method
Core Problem: Landslide susceptibility modelling often depends on algorithm choice and mapping optimization choices that can change spatial predictions.
Key Innovation: Tests hybrid machine learning and optimized mapping strategies to improve landslide susceptibility modelling.
37. Friction-induced heating and softening of shear band soil in high-velocity rotary shear tests for assessing landslide risk
Core Problem: High-velocity shear band behavior controls rapid landslide mobility but is difficult to quantify experimentally.
Key Innovation: Uses rotary shear tests to link frictional heating and soil softening with landslide-risk assessment.
38. Deformation monitoring and risk assessment of railway transit based on SBAS-InSAR and FR-machine learning
Core Problem: Railway transit corridors require deformation monitoring that can translate InSAR signals into risk assessment.
Key Innovation: Combines SBAS-InSAR with frequency-ratio machine learning for railway deformation monitoring and risk mapping.
39. Hypoplastic modeling and finite-element simulation of root-reinforced soils
Core Problem: Vegetation effects on soil strength and deformation require constitutive models that can be used in finite-element analysis.
Key Innovation: Combines hypoplastic modelling and finite-element simulation to represent root-reinforced soil behavior.
40. Hypoplastic modelling of thermo-mechanical behavior of root-reinforced soils
Core Problem: Root-reinforced soils experience coupled mechanical and thermal effects that can affect near-surface slope performance.
Key Innovation: Develops hypoplastic modelling for thermo-mechanical behavior of root-reinforced soils.
41. Controlling mechanism and quantitative characterization of thermal-hydraulic coupling properties of moraine soil containing frozen inclusions
Core Problem: Frozen inclusions alter thermal-hydraulic behavior in moraine soils, affecting cold-region slope response.
Key Innovation: Quantifies thermal-hydraulic coupling mechanisms in moraine soil containing frozen inclusions.
42. Correlation of tree stem diameter with root architecture, mechanical properties, and slope stability in a mountainous Cryptomeria plantation
Core Problem: Slope-protection forests require measurable proxies linking vegetation structure to root reinforcement and slope stability.
Key Innovation: Relates tree stem diameter to root architecture, mechanical properties, and slope stability in a mountainous plantation.
43. Landslide displacement prediction model based on improved STL and multi-scenario verification
Core Problem: Displacement prediction must separate trend, seasonal, and residual components in landslide monitoring time series.
Key Innovation: Improves STL-based decomposition for landslide displacement prediction and tests the model across multiple scenarios.
44. Phase separation in velocity profiles of saturated granular-liquid flows
Core Problem: Rapid mass flows depend on coupled granular and liquid velocity structures that are difficult to observe.
Key Innovation: Analyzes phase separation in saturated granular-liquid flow velocity profiles, informing debris-flow and landslide mobility physics.
45. Phase Unwrapping in Seconds: A Spectral ADMM Algorithm for Large-Scale InSAR
Core Problem: Large-scale InSAR phase unwrapping can be computationally limiting for rapid deformation analysis.
Key Innovation: Introduces a spectral ADMM approach that accelerates phase unwrapping for large InSAR datasets.
46. Multi-Image Grounding for Remote Sensing with Large Vision-Language Models
Core Problem: Remote-sensing foundation models need grounding methods that handle multiple images and spatially explicit references.
Key Innovation: Extends large vision-language models to multi-image grounding for remote sensing, with transferable value for hazard-scene interpretation.
47. Task-driven remote sensing image captioning with quadtree-based instruction tuning
Core Problem: Generic image captions often miss task-relevant spatial detail in Earth-observation imagery.
Key Innovation: Uses quadtree-based instruction tuning to produce task-driven remote-sensing captions that may support hazard interpretation workflows.
48. Hyperspectral LiDAR for high-resolution spectral retrieval of understory and soil surface properties
Core Problem: Understory and soil-surface properties are hard to retrieve at high resolution but influence hydrology and slope processes.
Key Innovation: Demonstrates hyperspectral LiDAR retrieval of understory and soil-surface properties relevant to terrain-condition mapping.
49. Wave modulation of flood-driven jet dynamics, sediment dispersion, and morphological evolution in the Modaomen Estuary
Core Problem: Flood-driven sediment jets interact with waves, changing dispersion and estuarine morphology.
Key Innovation: Quantifies wave modulation of flood-jet dynamics and morphological evolution in the Modaomen Estuary.
50. Global Contribution of Individual Submarine Groundwater Discharge to Coastal Water Budget and Land-Sea Dissolved Carbon Flux
Core Problem: Coastal water budgets need global-scale quantification of submarine groundwater discharge and associated fluxes.
Key Innovation: Estimates individual submarine-groundwater-discharge contributions to coastal water and dissolved-carbon budgets.
51. Bayesian inference of rainfall frequency and derived flood frequency under linear and nonlinear rainfall-runoff responses
Core Problem: Flood-frequency estimates are sensitive to rainfall frequency and nonlinear rainfall-runoff responses.
Key Innovation: Uses Bayesian inference to link rainfall-frequency uncertainty with derived flood-frequency estimates.
52. Posterior distribution estimation of unsaturated soil hydraulic properties by combining physics-informed neural networks and Bayesian inference
Core Problem: Unsaturated soil hydraulic properties are uncertain but central to rainfall infiltration and slope stability analysis.
Key Innovation: Combines physics-informed neural networks and Bayesian inference to estimate posterior distributions of hydraulic properties.
53. Safety assessment of tunnel infrastructure under upgrades of ventilation systems through side rock blasting
Core Problem: Tunnel upgrades involving side rock blasting can compromise surrounding ground and infrastructure safety.
Key Innovation: Assesses tunnel-infrastructure safety under ventilation-system upgrades conducted through side rock blasting.
54. Water Potential in Frozen Soil
Core Problem: Water potential in frozen soil controls moisture migration, frost heave, and cold-region ground stability.
Key Innovation: Clarifies water-potential behavior in frozen soil, supporting better cold-region geotechnical and slope models.
55. Whole-Life Analysis Framework for Pile Performance in Soft Soil: Probability-Based Pile Settlement and Life-Cycle Assessment
Core Problem: Pile performance in soft soil requires both probabilistic settlement prediction and life-cycle evaluation.
Key Innovation: Develops a whole-life analysis framework combining probability-based settlement and life-cycle assessment.
56. Extent of soil disturbance and spatial strength degradation induced by spudcan penetration and extraction in clay
Core Problem: Offshore foundation installation and extraction can degrade clay strength spatially, affecting geotechnical stability.
Key Innovation: Quantifies soil disturbance and strength degradation caused by spudcan penetration and extraction.
57. Development and application of a catchment-scale model for predicting loess cave density in gully areas
Core Problem: Loess cave density in gully terrain affects erosion, collapse, and slope stability but is rarely predicted at catchment scale.
Key Innovation: Develops a catchment-scale model to predict loess cave density in gully areas.
58. Discussions on analytical solutions of water inflow and hydraulic head for circular tunnel considering the influence of tunnel water storage and surrounding rock damage
Core Problem: Tunnel inflow and hydraulic head estimates must account for water storage and damaged surrounding rock.
Key Innovation: Discusses analytical solutions for circular tunnel inflow and hydraulic head under storage and damage effects.
59. Spalling failure characteristics and mechanisms of surrounding rock of a shallowly buried super-large section railway tunnel: Case study and discrete element analysis
Core Problem: Shallowly buried super-large railway tunnels can experience complex spalling failure in surrounding rock.
Key Innovation: Combines case evidence and discrete element analysis to explain tunnel surrounding-rock spalling mechanisms.
60. Dynamic fracture and mechanical behaviour of underwater metro tunnels adjacent to pile foundations under seismic excitation
Core Problem: Underwater metro tunnels near pile foundations face coupled dynamic fracture risks during earthquakes.
Key Innovation: Analyzes dynamic fracture and mechanical behavior of adjacent tunnel-pile systems under seismic excitation.
61. A calibration framework for stochastic models in nonlinear random vibrations: Application to lateral response of single pile in layered soils subjected to earthquake excitations
Core Problem: Earthquake response of piles in layered soils involves nonlinear random vibrations and uncertain model parameters.
Key Innovation: Proposes a calibration framework for stochastic vibration models applied to pile response under earthquake excitation.
62. ViTConvKAN: a hybrid transformer-KAN modeling for post-earthquake building damage mapping using VHR imagery
Core Problem: Post-earthquake response requires accurate high-resolution building damage mapping from imagery.
Key Innovation: Combines transformer and Kolmogorov-Arnold network modelling for post-earthquake building damage mapping with VHR imagery.
63. Enabling local action and reflection: The community disaster resilience capability framework
Core Problem: Local disaster resilience planning needs frameworks that connect capability assessment with action and reflection.
Key Innovation: Introduces a community disaster resilience capability framework for local action-oriented assessment.
64. The role of self-efficacy in emergency preparedness: An empirical test of an all-hazards scale
Core Problem: Preparedness behavior depends on self-efficacy, but all-hazards scales need empirical validation.
Key Innovation: Tests an all-hazards self-efficacy scale for emergency preparedness research.
65. Unsure and underinsured: exploring flood insurance decision-making and uptake behaviours via community engagement with flood-prone households
Core Problem: Flood-prone households often remain underinsured because risk perception, trust, and decision barriers are poorly understood.
Key Innovation: Uses community engagement to analyze flood-insurance decision-making and uptake behavior.
66. Local-level flood disaster governance in Nepal: Integrating social learning with institutional analysis and development framework
Core Problem: Local flood governance requires institutional analysis that captures learning, coordination, and implementation constraints.
Key Innovation: Integrates social learning with the institutional analysis and development framework for Nepalese flood governance.