Search Results (1,588)

Flood prediction remains a critical challenge in environmental risk management and disaster preparedness. Accurate river-level forecasting is essential for the development of reliable early warning systems and the mitigation of flood-related risks. However, conventional ensemble approaches, such as averaging and majority voting, often exhibit limited adaptability when individual models respond differently to anomalies or incomplete data. To address this limitation, this study proposes a graph-based synchronization framework that integrates XGBoost and Random Forest models using a Graph Convolutional Network (GCN). The proposed framework represents the outputs of the base prediction models as graph nodes and employs graph message passing to learn context-dependent relationships between their predictions. The framework is evaluated using real-world hydrological observations from the Lepenec River Basin in North Macedonia together with meteorological data obtained from the OpenWeatherMap API. Experimental results demonstrate that the proposed GCN-based synchronization framework outperforms both the standalone prediction models and the previously proposed linear synchronization method, achieving an R² value of 0.91 and a Mean Absolute Error (MAE) of 0.21. The obtained results indicate that graph-based synchronization provides an adaptive approach for integrating heterogeneous machine-learning models and has the potential to support future flood early-warning systems and intelligent environmental monitoring applications. Full article

Artificial intelligence is increasingly used in asset management, although evidence that greater model complexity consistently improves investment performance remains limited. This study examines whether machine-learning methods generate incremental value in European equity selection beyond transparent investment rules. The analysis used 562 eligible monthly price series drawn from the March 2026 STOXX Europe 600 constituents and applied retrospectively as a common ex post reference universe. The models were trained on observations from January 2011 to December 2020 and evaluated out of sample using signals formed from January 2021 to March 2026, with corresponding portfolio returns realized from February 2021 to April 2026. Logistic regression, random forest, XGBoost, and an equal-probability ensemble are compared with momentum, low-volatility, and equal-weight strategies under common portfolio-construction rules. Logistic regression recorded the strongest classification results and the highest gross cumulative portfolio return. The transparent momentum strategy recorded the highest observed Sharpe ratio and substantially lower target-weight turnover, while the three-model ensemble underperformed both logistic regression and momentum. Newey–West inference did not establish a statistically significant difference in mean monthly returns between logistic regression and momentum. The results indicate that additional algorithmic complexity did not generate incremental investment value under the restricted technical information set and portfolio framework considered. The findings support evaluating model complexity jointly through predictive quality, gross performance, statistical uncertainty, implementation sensitivity, interpretability, and governance requirements. Full article

Accurate correction of daily satellite-derived precipitation estimates in data-scarce tropical regions remains a critical challenge for climate monitoring, agriculture, and public health. Satellite products such as CHIRPS offer broad spatial coverage but exhibit systematic biases relative to ground-based observations particularly in complex terrain under bimodal tropical regimes influenced by ENSO. We propose a Functional Generalised Additive Mixed Model (FGAMM) that corrects CHIRPS-derived precipitation estimates by treating the annual accumulated precipitation curve as a functional response and the satellite accumulation curve as a functional covariate, while incorporating station-level random effects and the Southern Oscillation Index. This functional formulation targets the systematic, slowly varying bias between satellite and ground-station accumulation, the quantity most relevant for water-balance applications such as reservoir management and agricultural planning rather than day-to-day storm nowcasting. Applied to 62 IDEAM stations in the Valle del Cauca department of Colombia (2012–2020), the FGAMM achieves a mean cross-validation RMSE of 0.68 mm/day (95% bootstrap CI: 0.61–0.75), a substantially lower error than linear regression, SVM, and Random Forest within this dataset, where the gap is statistically significant across all competing methods. This magnitude of advantage is not reproduced when applying the same fitting-and-differencing pipeline, via a simplified concurrent approximation, to an independent national-network dataset; we discuss the methodological factors that likely contribute to this discrepancy—including an inherent smoothness asymmetry between the penalised-spline FGAMM fit and the unconstrained benchmark models, and differences in validation design between the two checks—in the Discussion, and treat the true size of the FGAMM’s advantage as an open question pending a fully controlled comparison. Corrected estimates are currently restricted to the calibrated station locations; because CHIRPS provides near-global daily coverage from 1981 to the present, we discuss how the same modelling approach could in principle be applied to other tropical or subtropical regions with a sparse reference station network, including areas of Latin America, sub-Saharan Africa, and South Asia where station density is similarly limited. Full article

This study adopts the contingent valuation method (CVM), Logit model, and quantile regression to evaluate the compensation level of public welfare forests and identify the influencing factors of compensation standards from the perspective of forest farmers’ willingness to accept compensation. The results show that the forest farmers’ expected compensation standard ranges from 1034 to 1128 CNY·ha⁻¹·year⁻¹, more than three times the current official compensation standard. Based on forest farmers’ willingness to accept, the current forest ecological compensation level only reaches 30.6%–33.4% of the expected standard, which is insufficient to mobilize the enthusiasm of forest farmers for forest management and protection. Therefore, it is necessary to further raise the public welfare forest compensation standard and implement diversified compensation measures that adapt to the differentiated demands of forest farmers. In addition, analysis of zero-willingness samples reveals that poor policy cognition is the main reason for zero compensation bids among partial respondents, rather than the overall sample. A better understanding of ecological compensation policies can significantly promote farmers’ willingness to accept compensation, suggesting that targeted policy publicity should be strengthened to improve forest protection awareness and policy satisfaction among forest farmers. Furthermore, household age, annual household income, forestland area, forest management cost, and policy understanding degree are core factors affecting compensation willingness. Quantile regression results indicate that these influencing factors show heterogeneous effects at different compensation levels, which provides evidence for the formulation of differentiated compensation schemes. The robustness test and heterogeneity analysis further confirm the reliability of the research conclusions. This study provides a practical reference for governments to optimize forest ecological compensation policies, balance household welfare, and promote sustainable forest resource protection. Full article

Tea plantations are high-input agricultural systems and have been recognized as hotspots of soil nitrous oxide (N₂O) emissions; however, the key controlling factors of these emissions and their quantitative prediction remain insufficiently understood. We compiled 115 field-observation records from 26 published studies into a multi-factor database covering climate, soil properties, and fertilization management, and compared five machine learning models—multiple linear regression (MLR), ridge regression, support vector regression (SVR), random forest (RF), and gradient-boosting regression trees (GBRTs)—using 5-fold cross-validation, combined with Spearman correlation and feature-importance analyses. Annual N₂O emissions varied widely (0.40–73.20 kg·hm⁻²·a⁻¹; mean 9.85 kg·hm⁻²·a⁻¹), and the mean direct emission factor (EFd, 2.04%) far exceeded the IPCC default value. Emissions were significantly positively correlated with total nitrogen (TN) input but negatively correlated with mean annual temperature (MAT) and mean annual precipitation (MAP). GBRT performed best, effectively capturing nonlinear multifactor interactions; TN input and soil pH were the dominant predictors, followed by rainfall. However, feature importance rankings were method-dependent: the RF/SHAP analysis ranked MAT first rather than fifth, reflecting the different algorithmic mechanisms of the two approaches. The GBRT-based model provides a useful tool for estimating tea-plantation N₂O emissions (LOOCV R² = 0.668) and quantitative support for sustainable nitrogen management and targeted greenhouse gas mitigation strategies in tea production systems. Full article

Ecotourism, as one of the most important forms of sustainable tourism, plays a significant role in the conservation of natural resources, the economic development of local communities, and the achievement of sustainable development goals. However, the sustainable development of this sector requires the accurate identification of suitable areas and the simultaneous assessment of the ecological capacities and limitations arising from anthropogenic activities, an issue that has received less attention at the national scale, particularly in countries with high environmental diversity such as Iran. Therefore, the present study was conducted with the aim of assessing the potential for ecotourism development in Iran based on Geographic Information System (GIS) and Spatial Multi-Criteria Decision Analysis (SMCDA). In line with the scope of the sub-factors evaluated, the assessed construct is referred to throughout this study as Environmental–Anthropogenic Ecotourism Suitability (EAES), reflecting an environmental-quality and anthropogenic-pressure perspective rather than a comprehensive assessment of ecotourism sustainability. The main innovation of this study lies in the simultaneous integration of natural and anthropogenic factors at the national scale and the sensitivity assessment of the results through the design of different development scenarios. In this study, a set of natural sub-factors including protected areas, vegetation cover, slope, precipitation, land use, and natural attraction density, as well as anthropogenic sub-factors including settlements, roads, accommodations, mines, industrial parks, dams, power transmission lines, dust, and the Global Human Modification (GHM) index were used. The weighting of the sub-factors was carried out using the Best–Worst Method (BWM), and spatial suitability maps were subsequently generated through the Weighted Linear Combination (WLC) method in the GIS environment. To evaluate uncertainty and examine the effect of the relative importance of sub-factors, three scenarios including natural factor dominance, anthropogenic factor dominance, and a balanced scenario were designed and analyzed. The results showed that protected areas (0.19), vegetation cover (0.17), and natural attraction density (0.16) were the most important natural sub-factors, while the GHM index (0.16), roads (0.15), and settlements (0.14) were the most important anthropogenic sub-factors affecting ecotourism development. The spatial pattern of the results indicated the concentration of areas with good potential in the Alborz and Zagros mountain ranges, the Hyrcanian forests, and parts of the protected areas of Iran. In the balanced scenario, approximately 24.8% of Iran’s area was classified within the suitable and highly suitable classes, while 46.3% was classified within the moderately suitable class. Furthermore, comparison of the scenarios showed that the use of one-dimensional approaches may lead to unrealistic estimates of ecotourism capacity. Overall, the results of this study indicate that the sustainable development of ecotourism in Iran requires the adoption of an integrated approach in which the conservation of natural assets and the management of anthropogenic interventions are simultaneously considered. The proposed framework can serve as a decision-support tool for spatial planning, investment prioritization, and sustainable ecotourism development policymaking in Iran and other similar regions. Full article

Achieving the Sustainable Development Goals (SDGs) requires a transition from conventional fossil-fuel-powered vehicles to alternative energy sources, such as electricity. However, reliably predicting energy consumption under highly variable real-world driving conditions and across different temporal horizons remains a critical challenge regarding the widespread adoption of Electric Vehicles (EVs), directly impacting the precision of range estimation, route planning, and charging strategies. To address this, a novel approach is proposed, combining advanced machine learning models—such as XGBoost, Random Forest, and regression-based techniques—with innovative dataset manipulation using statistical methods. The methodology integrates feature engineering to incorporate vehicle-specific metrics, including driving patterns and environmental conditions, ensuring that models dynamically adapt to real-world scenarios. The proposed framework demonstrates high accuracy and robustness in predicting energy consumption, providing valuable insights for sustainable transportation and efficient energy management toward SDG achievement. Full article

Quantifying the spatial variation in soil organic carbon (SOC) accumulation and its determining factors is vital for improving soil fertility and quality. The Fenhe River Basin is a major grain-producing area in Shanxi province in northern China, whereas the spatial heterogeneity of SOC accumulation is poorly known. Therefore, this study investigated SOC accumulation and its driving factors in arable soils across three representative counties (JL, FY, and XF) in the upper, middle, and lower reaches of the Fenhe River, respectively, which differ in climatic conditions and soil characteristics. SOC was determined using the potassium dichromate oxidation method. Results showed that both SOC content and stock were significantly lower in JL County (27–32% and 27–29% lower, respectively) compared to FY and XF. Within each county, the coefficient of variation was the highest in JL County (37.01%), indicating the highest spatial heterogeneity. Kriging interpolation indicated apparent spatial variation in SOC accumulation across the three counties. In JL, SOC was relatively higher in the northwest and southeast, while in FY, higher values occurred in the east. In XF, SOC generally decreased from north to south. The Spearman rank correlation and Random Forest analysis showed that SOC content and stock were significantly influenced by pH and cation exchange capacity (CEC), and pH had negative influences in JL County. Notably, in XF, available phosphorus and potassium, CEC, and clay proportion had positive influences on the SOC content and stock, while sand proportion had a negative relationship. These findings highlight the divergent, spatially heterogeneous nature of SOC accumulation across the Fenhe River Basin, driven by different factors. This study emphasizes the importance of specific soil management strategies in different locations to enhance carbon sequestration at the basin scale. Full article

Reliable spatial information on soil organic carbon (SOC) is important for soil-fertility management, climate-resilient coffee production, and land-restoration planning, yet the heterogeneity of smallholder agricultural landscapes is difficult to represent with field observations alone. This study characterised SOC variability, compared four machine-learning (ML) algorithms—Random Forest (RF), Cubist, Gradient Boosted Machines (GBM), and Support Vector Machine (SVM)—identified the main predictive environmental covariates, and generated SOC prediction maps for a smallholder Robusta coffee landscape in South–Central Uganda. A total of 126 georeferenced surface-soil observations (0–30 cm) were analysed for SOC using the Walkley–Black dichromate wet-oxidation method and related to climatic, terrain, spectral, soil, land-cover, and spatial-position covariates within the SCORPAN framework. Models were tuned within an SOC-stratified training subset and evaluated using a withheld 20% random holdout. Observed SOC ranged from 0.31% to 5.30%, with a mean of 2.06% and a coefficient of variation of 62.7%. Independent holdout performance was limited. SVM performed best (R² = 0.36; RMSE = 1.05), followed by Cubist, RF, and GBM. Annual mean temperature and spatial position had the highest predictive importance, followed by soil type, Landsat 9 Band 5, and annual precipitation. All four models reproduced a broad pattern of relatively higher SOC in Kalungu and central Masaka and lower SOC in Southern Kyotera and parts of Lwengo, although the magnitude of local variation differed among algorithms. The resulting maps are, therefore, best suited for landscape-level screening, sampling prioritisation, and identifying areas requiring field verification, rather than farm-level SOC prescription. Full article

Accurate quantification of forest biomass in the karst mountainous region of Southwest China is critical for regional carbon sink accounting. However, remote sensing-based inversion of forest biomass in this region is subject to the dual constraints of scale effects and spatial heterogeneity. Taking two typical karst landforms—plateau karst and peak-cluster depression karst—as case studies, this study developed an inversion framework that integrates multi-scale synergy (plot—small watershed—region) with geomorphological zoning modeling. Specifically, the high-precision forest aboveground biomass (AGB), retrieved by integrating high-resolution satellite imagery (2 m) of small watersheds with field plot data, was used as the scale-conversion bridge. The dominant class variability-weighted method was applied to upscale the spatial resolution from 2 m to 30 m. Subsequently, Landsat-8 OLI imagery, land use/land cover data, and topographic factors were integrated to construct landform-specific neural network models, namely BPANN-GY for plateau karst and BPANN-FC for peak-cluster depression karst, with validation RMSEs of 11.02% and 12.39%, respectively. The results showed that the mean forest AGB in the plateau karst region was 110.66 t·ha⁻¹ in 2013 and 111.46 t·ha⁻¹ in 2024; in the peak-cluster depression karst region, the mean forest AGB was 123.98 t·ha⁻¹ in 2014 and 125.57 t·ha⁻¹ in 2024. Compared with the baseline years (2013/2014), total forest AGB in both regions increased significantly by 2024, up to 11.43% in the plateau karst region and 6.43% in the peak-cluster depression karst region. Spatially, the most marked increase in AGB occurred in the mid-to-high slope zones, while the differences in forest AGB among slope grades gradually narrowed, indicating progressively enhanced forest structural integrity and functional stability under effective land management. This study methodologically validates the scientific soundness and feasibility of using high-precision AGB retrieved from high-resolution satellite imagery as a scale-conversion bridge. The established AGB inversion system provides a methodological framework and data foundation for carbon sink accounting, ecological restoration, and land management in the karst region of Southwest China, and offers a transferable approach for forest AGB inversion in other highly heterogeneous landscapes. Full article

Background/Objectives: Diffuse large B-cell lymphoma (DLBCL) remains clinically heterogeneous despite standard first-line immunochemotherapy. We evaluated whether machine learning (ML) models using routinely available diagnostic variables could predict primary refractory DLBCL. Materials and Methods: We performed a retrospective single-center study of 369 patients with DLBCL treated between 2015 and 2023. Primary refractory disease was defined as failure to achieve at least a partial response after first-line therapy. Logistic regression, random forest, gradient boosting, support vector machine with radial basis function kernel (SVM-RBF), and multilayer perceptron models were trained using baseline clinical and laboratory predictors. Performance was assessed using a stratified 80/20 holdout split, nested cross-validation, repeated random splits, and sensitivity analysis with complete hyperparameter retuning. Results: Primary refractory disease occurred in 111 patients (30.1%). In the holdout test set (n = 73), IPI achieved the highest discrimination (AUC 0.692; 95% CI 0.545–0.828). Among ML models, gradient boosting performed best (AUC 0.658; 95% CI 0.503–0.801), followed by SVM-RBF (AUC 0.619; 95% CI 0.471–0.760). DeLong comparisons showed no significant differences between ML models and IPI. In internal validation, gradient boosting showed the highest nested CV AUC (0.712 ± 0.049). Feature importance identified IPI as the dominant predictor. A weighted ML-derived clinical score identified a high-risk group with the highest refractory rate (52.9%), although the trend was not statistically significant (p = 0.063). Conclusions: ML models showed comparable but not superior performance to IPI for predicting primary refractory DLBCL. These findings support further external validation and development of multimodal clinically interpretable prediction frameworks for risk-adapted management. Full article

The Northeast black soil region is a major grain-producing area in China, where county-scale assessment of topsoil quality is essential for soil conservation and provides a potential indicator framework for regional soil quality monitoring. In this study, 500 composite cultivated-layer samples were collected from dryland croplands in Keshan County, Heilongjiang Province. Soil physical properties, basic chemical properties, and macro-, secondary, and micronutrient contents were measured to establish a total data set (TDS). Three hierarchical total data sets (TDS1, TDS2, and TDS3) were established from the original TDS according to the progressive incorporation of soil physical properties, basic chemical properties, macronutrients, secondary nutrients, and micronutrients. Within each hierarchical TDS, key indicators were selected using random forest (RF), mutual information (MI), principal component analysis (PCA), and minimum spanning tree (Tree) methods to construct algorithm-specific minimum data sets (MDSs). TDS1 included soil physical properties, basic chemical properties, and macronutrients; TDS2 further incorporated secondary nutrients; and TDS3 additionally included micronutrients to represent progressively comprehensive soil nutrient information. The soils exhibited considerable soil organic matter and cation exchange capacity, with mean values of 51.45 g kg⁻¹ and 34.85 cmolc kg⁻¹, respectively, and a mean pH of 6.04. Across the four algorithms, commonly retained indicators included SOM, TN, pH, CEC, and Silt, while nutrient-related indicators such as AP, AK, S, Zn, and Fe were additionally selected under different hierarchical MDSs, reflecting the importance of multi-nutrient information in soil quality characterization. Available phosphorus, available potassium, sulfur, and zinc showed greater spatial variability than basic physicochemical properties. The four algorithms differed in indicator selection, reflecting their distinct sensitivities to linear variation, information gain, multilevel contributions, and network structure. RF_MDS1 showed the highest agreement with the TDS (R² = 0.924), indicating its strong capability in preserving overall soil quality information using a simplified indicator set. RF_MDS3 maintained a high consistency with the TDS (R² = 0.836) while incorporating additional secondary nutrients and micronutrients. Therefore, RF_MDS3 was considered a more comprehensive MDS framework when multi-nutrient representation and potential nutrient constraint identification were prioritized, whereas RF_MDS1 remained an efficient option for simplified soil quality assessment. This framework may provide a transferable approach for soil quality assessment and nutrient management in comparable black-soil regions and dryland farming systems. Full article

Cropland fragmentation affects regional ecosystem functioning and biodiversity conservation, making the spatio-temporal coupling between cropland fragmentation (CLF) and habitat quality (HQ) a key basis for land management and ecological protection. However, the reported CLF–HQ coupling differs in sign between regions, and the mechanism underlying this divergence remains unclear. This study evaluated the spatio-temporal dynamics of CLF and HQ and their interrelationship across Hunan Province, China, at the township scale (∼2450 units) from 1994 to 2024. CLF was measured by a four-dimensional index consisting of Scale (SPI), Natural Endowment (NPI), Aggregation (API), and Convenience (CPI), with weights determined by a combined AHP–Entropy method; HQ was modelled with InVEST. The relationship between the two was analysed through bivariate spatial autocorrelation, Mantel tests, Random Forest, redundancy analysis (RDA), and XGBoost–SHAP. CLF peaked in urban fringes and was lowest in the western mountains; mean HQ declined modestly, mainly before 2014. Spatially, CLF and HQ were negatively associated (bivariate Moran’s I between −0.38 and −0.51, $p<0.001$), opposite in sign to the positive coupling found in arid Northwest China. Decomposition of the composite index showed that the negative association was carried largely by the natural-endowment dimension, in which slope and elevation were the dominant indicators. Structural fragmentation dimensions uniquely explained only 1.5–5.4% of HQ variance, and controlling for terrain reduced the CLF–HQ correlation by 67–96%. The sign also reversed under an alternative directional standardisation, so it is not robust to a defensible analytical choice. In these data, the observed negative coupling is largely consistent with a shared topographic gradient rather than a direct fragmentation effect. For management, this correlational evidence suggests that reducing fragmentation alone may do little to improve habitat quality; conservation is better guided by the underlying terrain and land-use gradients. More broadly, CLF–HQ assessments become more reliable when cropland structure is separated from natural endowment and terrain confounding is accounted for. Full article

Machine Learning (ML) models effectively capture complex, nonlinear, multimodal, and time-dependent patterns in indoor environments. In thisstudy, the relationship between indoor CO₂ concentrations and environmental variables in different areas of a university hospital was investigated using ML methods. The dataset is a total of 114, including 80% train and 20% test. CO₂ concentration measured at 16:00 was defined as the target variable, while eight inputs (number of occupants, room volume, room floor area, average relative humidity, average temperature, average CO₂ concentration, the change in CO₂ concentration, room orientation) were selected as model inputs. Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Linear Regression were applied to predict CO₂ concentration. Model performance was evaluated based on prediction accuracy criteria, such as Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), so it enabled a comparative consideration of their effectiveness. When CO₂-related variables were included, RF achieved the best performance (R² = 0.804, MAPE = 8.34%, MAE = 70.65, RMSE = 104.24), followed by XGBoost (R² = 0.776, MAPE = 9.07%, MAE = 78.10, RMSE = 111.44). In contrast, ANN (R² = 0.250, MAPE = 19.62%, MAE = 159.81, RMSE = 203.72) and Linear Regression (R² = 0.175, MAPE = 19.44%, MAE = 169.37, RMSE = 213.77) showed comparatively lower predictive performance. Overall, the results indicate that tree-based ML models can provide promising predictive performance and practical insights for indoor air quality monitoring and management in healthcare facilities, while their applicability and generalizability could be further strengthened through future evaluations involving data from diverse healthcare environments. Full article

Recent advances in numerical modelling, remote sensing, and artificial intelligence are bringing a transformation in our ability to assess sediment transport. Nevertheless, climate change is fundamentally altering sediment production, transport, and deposition through intensifying hydrological extremes, sea-level rise, changing storm regimes, cryosphere degradation, and increasing human modification of sediment pathways. These interacting drivers challenge conventional sediment transport assessment, which has largely evolved within separate fluvial, estuarine, coastal, and marine disciplines and often lacks an integrated perspective capable of representing source-to-sink sediment connectivity under non-stationary environmental conditions. Although significant advances have been made in process-based numerical modelling, Earth observation, and artificial intelligence (AI), these approaches are commonly reviewed independently, limiting their collective application to regional climate-responsive sediment assessment. This review examines state-of-the-art process-based numerical models, observational tools, and machine-learning approaches for sediment transport from source-to-sink. A transparent benchmarking scheme is used to compare leading modelling systems (e.g., AdH, SRH-2D, FLO-2D, HEC-RAS, TELEMAC, Delft3D, EFDC, SCHISM, XBeach, ROMS), highlighting differences in dimensionality, sediment-process representation, computational demands, and climate-scenario readiness. Remote sensing (optical, SAR, LiDAR, UAV) and AI/ML/DL methods (e.g., random forests) are reviewed as complementary tools that enhance model parametrization, improve validation, and address uncertainty in data-limited regions. A reproducible bibliometric synthesis based on Dimensions.ai records (2000–2026) reveals accelerating growth in sediment-transport research, with strong recent expansion in coastal, estuarine, and data-driven modelling applications. Major challenges include cohesive sediment physics, cross-environment coupling, limited long-term validation datasets, and the need for scalable workflows compatible with climate-model forcing. In this manuscript, we analyse and propose a future roadmap for near-term integration of satellite–field data streams, medium-term development of hybrid physics–AI models, and long-term coupling of sediment modules within Earth-system and regional climate frameworks. Collectively, this review provides a foundation for next-generation, climate-responsive sediment transport assessment supporting sustainable river basin and coastal management. Full article