Search Results (344)

Soil salinization poses a severe threat to agricultural sustainability in the Yellow River Delta, where conventional spectral indices are limited by vegetation interference and seasonal dynamics in coastal saline-alkali landscapes. To address this, we developed an inversion framework integrating spectral indices and vegetation temporal features, combining multi-temporal Sentinel-2 optical data (January 2024–March 2025), Sentinel-1 SAR data, and terrain covariates. The framework employs Savitzky–Golay (SG) filtering to extract vegetation temporal indices—including NDVI temporal extremum and principal component features, capturing salt stress response mechanisms beyond single-temporal spectral indices. Based on 119 field samples and Variable Importance in Projection (VIP) feature selection, three ensemble models (XGBoost, CatBoost, LightGBM) were constructed under two strategies: single spectral features versus fused spectral and vegetation temporal features. The key results demonstrate the following: (1) The LightGBM model with fused features achieved optimal validation accuracy (R² = 0.77, RMSE = 0.26 g/kg), outperforming single-feature models by 13% in R². (2) SHAP analysis identified vegetation-related factors as key predictors, revealing a negative correlation between peak biomass and salinity accumulation, and the summer crop growth process affects soil salinization in the following spring. (3) The fused strategy reduced overestimation in low-salinity zones, enhanced model robustness, and significantly improved spatial gradient continuity. This study confirms that vegetation phenological features effectively mitigate agricultural interference (e.g., tillage-induced signal noise) and achieve high-resolution salinity mapping in areas where traditional spectral indices fail. The multi-temporal integration framework provides a replicable methodology for monitoring coastal salinization under complex land cover conditions. Full article

The Source Region of the Yangtze and Yellow Rivers (SRYY), situated on the Qinghai-Tibet Plateau, serves as a vital ecological barrier and a critical component of the global carbon cycle. However, this region faces severe ecosystem degradation driven by climate change and human activities. This study establishes an integrated ecological security assessment framework that couples ecological risk, ecosystem health, and ecosystem services to evaluate ecological dynamics in the SRYY from 2000 to 2020. Leveraging multi-source data (vegetation, hydrological, meteorological) and advanced modeling techniques (spatial statistics, geographically weighted regression), we demonstrate that: (1) The Ecological Security Index (ESI) exhibited an initial increase followed by a significant decline after 2010, falling below its 2000 level by 2020. (2) The rising Ecological Risk Index (ERI) directly weakened both the ESI and Ecosystem Service Index (ESsI), with this negative effect intensifying markedly post-2010. (3) A distinct spatial gradient pattern emerged, shifting from high-security core areas in the east to low-security zones in the west, closely aligned with terrain and elevation; conversely, areas exhibiting abrupt ESI changes showed little correlation with permafrost degradation zones. (4) Vegetation coverage emerged as the key driver of ESI spatial heterogeneity, acting as the central hub in the synergistic regulation of ecological security by climate and topographic factors. Full article

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article

Under the context of global climate change, aridity responses exhibit significant differences across various latitudinal zones, and quantifying the dependency relationship between aridity and latitudinal zones is of great importance for differentiated water resource management. The Lancang River Basin in China spans 13 latitudinal zones with distinct altitudinal gradients, making it crucial to analyze the relationship between long-term aridity variation patterns and latitude for understanding basin hydrological response mechanisms. This study adopted the United Nations Environment Programme (UNEP) aridity index definition and utilized publicly available high-resolution datasets to divide the Chinese Lancang River Basin into 26 regions at 0.5° N intervals. The spatiotemporal evolution characteristics of the aridity index at interannual and seasonal scales from 1940 to 2022 were analyzed, and the trends of aridity index changes and their relationship with latitude were quantified. Results indicate: (1) The spring aridity index increased significantly (trend rate of 0.015/10a, Z = 2.39), driving an overall basin-wide humidification trend. (2) The aridity index exhibited significant spatial and seasonal differences with latitude: southern regions (south of 24.75° N) showed negative correlations, northern regions (north of 30.5° N) showed positive correlations, while central regions displayed distinct seasonal transitions and spatial differentiation characteristics bounded by 27.25° N. (3) The rate of aridity index change in regions north of 27.25° N was significantly higher than in southern regions (p < 0.001). This study reveals the latitudinal patterns of AI changes in the Lancang River Basin, providing guidance for developing adaptive water resource allocation strategies under climate change scenarios. Full article

As a flagship low-carbon transition zone in China, the Yangtze River Delta (YRD) faces challenges in synergizing green innovation efficiency (GIE) and urban ecological resilience (UER). This study establishes a dual-system evaluation framework to quantify their coupling coordination degree (CCD) across the 41 cities of the YRD from 2010 to 2023 using coupling coordination modeling, Geodetector, as well as Geographically and Temporally Weighted Regression (GTWR). Key findings reveal the following: (1) Temporally, GIE surged from 0.252 to 0.692, while UER rose steadily from 0.228 to 0.395. This joint improvement elevated the CCD from mildly discordant to primary coordination. (2) Spatially, an east–high, west–low gradient defined three regional typologies: coastal clusters with high coupling and intermediate coordination; the Yangtze River corridor with high coupling yet only primary coordination; and inter-provincial border zones with low coupling and low coordination. In these border zones, administrative fragmentation resulted in a CCD that was 10–23% lower than that of inland regions. (3) Mechanistically, the green innovation driving force and policy synergy degree were the dominant promoters. In contrast, urban expansion pressure and rigid ecological regulation exhibited spatially heterogeneous effects, with their overall inhibitory impacts most pronounced in highly urbanized coastal cores and inland industrial transition zones. The findings may serve as a practical case reference for tailoring governance strategies in global mega-city regions pursuing synergistic low-carbon transitions. Full article

Coastal wetlands are highly threatened by human activities, leading to water quality degradation and biodiversity loss. This study assessed spatial variation in 27 water quality parameters, sediment organic matter, and macroinvertebrate assemblages across 12 sites in the estuarine Cruces River wetland (CRW Ramsar site, southern Chile) during summer 2019. Our analysis identified three areas of sampling stations in the wetland, categorized by trophic gradient and salinity: freshwater (n = 5), mixed (n = 3), and estuary (n = 4). Freshwater sites were characterized by low salinity, turbidity, and high nitrate concentrations. Estuarine sites were characterized by higher salinities and turbidity and low nitrates and total organic carbon (TOC) concentrations, and mixed sites had low salinities, high turbidities, high TOC, and low nitrates. Throughout the CRW, the richness and densities of different invertebrates were recorded. Freshwater stations had higher species richness, and estuary stations had higher abundance. Macroinvertebrates found in the lower reaches of the CRW included species characteristic of estuarine environments, whereas the upper stations were dominated by invertebrates inhabiting low-salinity environments. According to the ordination plot of distance-based redundancy analysis (dbRDA) and distance-based linear model (DistLM), our results indicate that macroinvertebrate assemblages differ significantly among areas of the CRW, primarily due to physicochemical variables (i.e., salinity, total carbon, and dissolved phosphorus). Total organic matter content in sediments was higher in freshwater sites and lower in estuarine sites. Our findings will be used to monitor the wetland and implement appropriate management measures for human activities, thereby protecting and conserving the estuarine Cruces River Ramsar wetland. Full article

In this study, the geological disasters in Guizhou Province serve as the research object, and a systematic susceptibility evaluation is conducted in light of the province’s prominent problems with frequent geological disasters. The current research primarily focuses on the application of a single model, often with deficiencies in factor interpretation. It has not yet systematically integrated the advantages of the traditional information model and multiple machine learning algorithms, nor introduced interpretable methods to analyze the disaster mechanism deeply. In this study, the information value (IV) model is combined with machine learning algorithms—logistic regression (LR), decision tree (DT), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—to construct a coupling model to evaluate the susceptibility to geological disasters. Combined with the Bayesian optimization algorithm, the geological disaster susceptibility evaluation model is built. The confusion matrix and receiver operating characteristic (ROC) curve were used to evaluate the model’s accuracy. The Shapley Additive exPlanations (SHAP) method is used to quantify the contribution of each influencing factor, thereby improving the transparency and credibility of the model. The results show that the coupling models, especially the IV-XGB model, achieved the best performance (AUC = 0.9448), which significantly identifies the northern Wujiang River Basin and the central karst core area as high-risk areas and clarifies the disaster-causing mechanism of “terrain–hydrology–human activities” coupling. The SHAP method further identified that NDVI, land use type, and elevation were the predominant controlling factors. This study presents a high-precision and interpretable modeling method for assessing susceptibility to geological disasters, providing a scientific basis for disaster prevention and control in Guizhou Province and similar geological conditions. Full article

The production and transport of dissolved inorganic carbon (DIC) is central to weathering reactions and the global carbon cycle. We investigated the spatiotemporal variability and export of inorganic carbon species along the rapidly weathering Langtang–Narayani river system in the central Nepal Himalaya. Over the course of one year, surface water samples were collected from sixteen stations spanning a wide range of elevations. DIC concentrations generally declined with increasing elevation, except in mid-mountain sites influenced by hot springs. Bicarbonate (HCO₃⁻) was identified as the dominant inorganic carbon species, contributing approximately 85% to the total DIC and with a similar dominant export rate of bicarbonate to total DIC export rate, followed by carbon dioxide (CO₂) and carbonate (CO₃²⁻). The river water exhibited a strong altitudinal gradient in carbonate chemistry, with CO₂ supersaturation in the lowlands and undersaturation at higher elevations. Metamorphic activities in the lower mid-mountain sites significantly influenced CO₂ concentrations and inorganic carbon dynamics. The partial pressure of CO₂ (pCO₂) varied widely (56 to 33,869 μatm), reflecting distinct geochemical and seasonal controls. The estimated DIC export rates were 93.66, 37.81, and 12.59 tons km⁻² yr⁻¹ from the Narayani River in the lowlands, the Trisuli River in the mid-mountains, and the Langtang River in the high Himalaya region, respectively. These findings highlight the critical role of elevation, seasonality, and geological processes in regulating carbon dynamics in Himalayan river systems, providing new insights into their contribution to regional carbon fluxes. A comprehensive array of significant univariate and multivariate predictive models is presented here, offering versatile applications, including the interpretation of full and partial derivatives explaining inorganic carbon dynamics within the Himalayan basin. Full article

Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges such as weathering, vegetation cover, and spectral overlap in complex sedimentary rock regions. This study proposes a hierarchical multi-feature random forest algorithm based on Feature-Preserved Compressive Sampling (FPCS). Using 3D laser point cloud data from the Manas River outcrop in the southern margin of the Junggar Basin as the test area, we integrate graph signal processing and multi-scale feature fusion to construct a high-precision lithology identification model. The FPCS method establishes a geologically adaptive graph model constrained by geodesic distance and gradient-sensitive weighting, employing a three-tier graph filter bank (low-pass, band-pass, and high-pass) to extract macroscopic morphology, interface gradients, and microscopic fracture features of rock layers. A dynamic gated fusion mechanism optimizes multi-level feature weights, significantly improving identification accuracy in lithological transition zones. Experimental results on five million test samples demonstrate an overall accuracy (OA) of 95.6% and a mean accuracy (mAcc) of 94.3%, representing improvements of 36.1% and 20.5%, respectively, over the PointNet model. These findings confirm the robust engineering applicability of the FPCS-based hierarchical multi-feature approach for point cloud lithology identification. Full article

Assessment of cultural ecosystem services (CESs) is a key component in advancing the sustainable development of urban ecosystems. Mapping the spatial distribution of CESs provides spatially explicit insights for urban landscape planning. However, most assessments lack regional adaptability, particularly in cities with pronounced environmental and cultural heterogeneity. To address this gap, this study focused on the central urban area of Lhasa, using communities as units to develop a tailored CES assessment framework. The framework integrated the MaxEnt model with multi-source indicators to analyze the spatial distribution of five CES categories and their relationships with environmental variables. Spatial statistics and classification at community level informed the CES spatial optimization strategies. Results indicated that high-value CES areas were predominantly concentrated in the old city cluster, typified by Barkhor and Jibenggang subdistricts, following an east–west spatial pattern along the Lhasa River. Distance to tourist spot contributed 78.3% to cultural heritage, 86.1% to spirit and religion, and 42.2% to ecotourism and aesthetic services, making it the most influential environmental variable. At the community level, CESs exhibited a distinct spatial gradient, with higher values in the central area and lower values in the eastern and western peripheries. For the ecotourism and aesthetic category, 61.47% of the community area was classified as low service, whereas only 1.48% and 7.33% were identified as excellent and high. Moreover, communities within subdistricts such as Barkhor and Zhaxi demonstrated excellent service across four CES categories, with notably lower performance in the health category. This study presents a quantitative and adaptable framework and planning guidance to support the sustainable development of CESs in cities with similar characteristics. Full article

Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan (CZT) urban agglomeration by selecting 17 socioeconomic and natural environmental factors within a risk assessment framework encompassing hazard, exposure, vulnerability, and resilience. Additionally, the Patch-Generating Land Use Simulation (PLUS) and multilayer perceptron (MLP)/Bayesian network (BN) models were coupled to predict flood risks under three future land use scenarios: natural development, urban construction, and ecological protection. This integrated modeling framework combines MLP’s high-precision nonlinear fitting with BN’s probabilistic inference, effectively mitigating prediction uncertainty in traditional single-model approaches while preserving predictive accuracy and enhancing causal interpretability. The results indicate that high-risk flood zones are predominantly concentrated along the Xiang River, while medium-high- and medium-risk areas are mainly distributed on the periphery of high-risk zones, exhibiting a gradient decline. Low-risk areas are scattered in mountainous regions far from socioeconomic activities. Simulating future land use using the PLUS model with a Kappa coefficient of 0.78 and an overall accuracy of 0.87. Under all future scenarios, cropland decreases while construction land increases. Forestland decreases in all scenarios except for ecological protection, where it expands. In future risk predictions, the MLP model achieved a high accuracy of 97.83%, while the BN model reached 87.14%. Both models consistently indicated that the flood risk was minimized under the ecological protection scenario and maximized under the urban construction scenario. Therefore, adopting ecological protection measures can effectively mitigate flood risks, offering valuable guidance for future disaster prevention and mitigation strategies. Full article

Urban estuarine environments face increasing water safety risks due to microbial contamination from combined sewer overflows (CSOs), particularly during heavy rainfall events. In megacities like Tokyo, where waterfronts are widely used for recreation, such contamination poses significant public health risks. The challenge is compounded by the variability in both intensity and spatial distribution of rainfall across the catchment, combined with complex tidal dynamics making effective water quality management difficult. To address this challenge, we conducted a series of hydrodynamic–microbial fate simulations to examine the spatial and vertical behavior of Escherichia coli (E. coli) under different rainfall–tide conditions. Focusing on the Sumida River estuary, rainfall data from eight drainage areas were classified into six event types using cluster analysis. Two contrasting events were selected for detailed analysis: a light rainfall (G2, 15 mm over 13 h) and an intense event (G6, 272 mm over 34 h). Vertical water quality profiling was performed along an 8.5 km transect from the Kanda–Sumida River confluence to the Tokyo Bay Tunnel, illustrating E. coli and salinity. The results showed that the rainfall intensity and tidal phase at the event onset are critical in shaping both the magnitude and vertical distribution of microbial contamination. The intense event (G6) led to deep microbial intrusion (up to 6–7 m) and major salinity disruption, while the lighter event (G2) showed surface-layer confinement. Salinity gradients were more strongly affected during G6, indicating freshwater intrusion. Tidal phase also influenced transport: the flood-high condition retained E. coli, whereas ebb-low tides facilitated downstream flushing. These findings highlight the influence of rainfall intensity and tidal timing on microbial distribution and support the use of vertical profiling in estuarine water quality management. They also support the development of dynamic, event-based water quality risk assessment tools. With appropriate local calibration, the modeling framework is transferable to other urban estuarine systems to support proactive and adaptive water quality management. Full article

Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article

The Qiantang tidal bore is globally renowned for its spectacular landscape and its strong impacts on the Qiantang riverbed erosion/deposition process. However, due to the extremely high spatial gradient of its water level along the propagation direction around the bore front, as well as the very swift movement of this front, the numerical reproduction of the bore formation and propagation has been a challenge for several decades. Here, using GPU acceleration and Local Time Stepping (LTS), we present a high-resolution simulation of the Qiantang tidal bore formation and propagation, achieving a 10 m resolution across 1169 km², which captures the bore dynamics and the full formation-to-decay processes while simulating 2-day tidal bore phenomena in 1.2 h. The formation mechanisms of three typical tidal bores (cross-shape bore, thread-shape bore, and returned bore) are revealed numerically. The cross-shape bore appears first and is generated by flow division around the mid-channel bars; further upstream, the thread-shape bore is formed due to the increasingly narrow river along a straight reach; and its reflection results in the returned bores at the YC bending reach. This study also highlights how variations in flow discharge affect the tidal bore. When the discharge increases to the annual mean discharge, the intensity of the tidal bore is increased, whereas extremely high flood peak discharge inhibits bore propagation. This study provides a scientific basis for conserving tidal bore landscapes and offers decision support for sustainable estuarine governance. Full article

Coseismic landslides are geological events in which slopes, either on the verge of instability or already in a fragile state, experience premature failure due to seismic shaking. On 5 September 2022, an Ms 6.8 earthquake struck Luding County, Sichuan Province, China, triggering numerous landslides that caused severe casualties and property damage. This study systematically interprets 13,717 coseismic landslides in the Luding earthquake’s epicentral area, analyzing their spatial distribution concerning various factors, including elevation, slope gradient, slope aspect, plan curvature, profile curvature, surface cutting degree, topographic relief, elevation coefficient variation, lithology, distance to faults, epicentral distance, peak ground acceleration (PGA), distance to rivers, fractional vegetation cover (FVC), and distance to roads. The analytic hierarchy process (AHP) was improved by incorporating frequency ratio (FR) to address the subjectivity inherent in expert scoring for factor weighting. The improved AHP, combined with the Pearson correlation analysis, was used to identify the dominant controlling factor and assess the landslide susceptibility. The accuracy of the model was verified using the area under the receiver operating characteristic (ROC) curve (AUC). The results reveal that 34% of the study area falls into very-high- and high-susceptibility zones, primarily along the Moxi segment of the Xianshuihe fault and both sides of the Dadu river valley. Tianwan, Caoke, Detuo, and Moxi are at particularly high risk of coseismic landslides. The elevation coefficient variation, slope aspect, and slope gradient are identified as the dominant controlling factors for landslide development. The reliability of the proposed model was evaluated by calculating the AUC, yielding a value of 0.8445, demonstrating high reliability. This study advances coseismic landslide susceptibility assessment and provides scientific support for post-earthquake reconstruction in Luding. Beyond academic insight, the findings offer practical guidance for delineating priority zones for risk mitigation, planning targeted engineering interventions, and establishing early warning and monitoring strategies to reduce the potential impacts of future seismic events. Full article