2455 papers
This paper introduces a novel spatio-temporal Hawkes process model that incorporates strain-rate-dependent triggering kernels to capture the self-exciting, clustered dynamics of ocean eddy formation, supported by a derived Volterra integral equation, an EM-based parameter estimation algorithm, and a two-stage simulation framework validated against high-frequency ocean flow data.
This study reveals that while soil moisture over South Asia exhibits significant persistence, it lacks independent predictive power for subsequent rainfall due to a seasonal reorganization of land-atmosphere interactions, thereby distinguishing between the storage, expression, and predictive reach of terrestrial climate memory.
This paper presents a new conceptual ecological model for Lostmans Slough that links freshwater delivery and other environmental drivers to ecological responses across four zones, aiming to guide adaptive restoration management while accounting for spatial heterogeneity, tradeoffs, and uncertainties in the southern Everglades.
Using an instrumental variable approach on rural Chinese data from 2010 to 2020, this study demonstrates that climate hazard exposure significantly increases involuntary immobility by eroding migration capabilities, particularly among the poorest households, thereby highlighting the urgent need for adaptation policies that support those trapped by climate change rather than focusing solely on migrants.
This paper employs a systematic literature review and bibliometric analysis to clarify the complementary relationship between sustainable landscape (as a desired spatial outcome) and landscape sustainability (as a developmental process), thereby establishing a conceptual framework to guide future assessment, planning, and design while addressing the current anthropocentric bias in the field.
This review synthesizes the current applications of machine learning and deep learning across the mineral exploration pipeline, from prospectivity mapping to geophysical inversion, while critically examining persistent challenges such as data scarcity and model interpretability, and outlining future directions including physics-informed learning and foundation models.
This study assesses sea level rise risks along the Egyptian Mediterranean coast by integrating multi-source data (GPS, tide gauges, GRACE, and satellite altimetry) with machine learning models, identifying XGBoost as the most accurate predictor and forecasting a continued rise of 2.8 to 4.09 mm/year through 2050 that threatens coastal cities like Alexandria, Port Said, and El Arish.
This study presents a high-resolution, observation-based global assessment of annual gross greenhouse gas emissions and removals from 2016 to 2024, revealing that while land acts as a net sink of -3.6±4.1 Gt CO2e yr⁻¹, these fluxes are highly concentrated spatially, with half of emissions occurring in just 4.5% of emitting land.
Analysis of micro-laminated diatomites from the Norwegian Sea reveals that high-latitude marine ecosystems maintained persistent seasonal cycles of productivity and stratification during the onset of the Paleocene–Eocene Thermal Maximum, providing rare sub-annual evidence of how surface-ocean dynamics responded to extreme greenhouse warming.
This study utilizes a multi-regional input–output framework to analyze the energy intensity of meat supply chains across the Americas, revealing that trade dependence, logistics performance, and structural institutional conditions are more critical determinants of efficiency than income levels or renewable energy shares, thereby offering new insights for integrating food security and energy policy.