Search Results (6,689)

Understanding how climate and land use interact to shape freshwater quality remains challenging across heterogeneous river basins. This study analysed monthly citizen-science measurements of nitrate (NO₃), phosphate (PO₄), and turbidity, collected between 2016 and 2024, across seven Italian river basins representing contrasting climatic and land-use contexts. A non-parametric analytical framework combining Kruskal–Wallis tests, aligned rank transform analyses, principal component analysis (PCA), and basin-specific Somers’ D statistics was applied to ordinal concentration data. Significant differences among basins revealed persistent spatial structuring of water-quality regimes. PCA identified two largely independent gradients: a dominant nutrient axis defined by NO₃ and PO₄, and a secondary turbidity axis. Urban and industrial land use aligned with higher nutrient categories, while vegetated landscapes were associated with lower concentrations. Climatic effects were basin specific. Precipitation showed opposing relationships with NO₃, suggesting both mobilisation and dilution processes, whereas temperature was positively associated with PO₄ in several basins and negatively related to NO₃. Turbidity displayed variable links with precipitation and temperature, reflecting hydrological and seasonal controls. Overall, results indicate that land use represents the primary structural driver of nutrient variability, while climatic factors modulate basin-specific responses. The integration of citizen science observations with robust non-parametric approaches provides a scalable framework for detecting environmental drivers and supporting the targeted management of Mediterranean river systems. Full article

Rapid urbanization has transformed the face of Texas by converting agricultural and natural lands into expanding built-up areas. This study analyzes and simulates land-use and land-cover (LULC) changes in Kaufman County, Texas, one of the fastest-growing counties in the United States, using a hybrid Cellular Automata–Artificial Neural Network (CA–ANN) model within the Quantum Geographic Information System (QGIS) Modules for Land-Use Change Evaluation (MOLUSCE) framework. Multitemporal NLCD datasets (2001, 2011, and 2021) and six spatial drivers: Elevation, Slope, Aspect, Distance from Roads and Rivers, and Built-up Density were used in the modeling framework. Transition relationships were calibrated using the 2001–2011 LULC data, and the model was validated by simulating the 2021 LULC map from the 2011 baseline. The calibrated model was then used to simulate future LULC scenarios for 2031, 2041, and 2051. Model validation yielded an overall Kappa value of 0.84 and a correctness of 90.9%, indicating high similarity between the observed and simulated maps. The results indicate simulated urban expansion, with built-up areas increasing by nearly 30% by 2051 at the expense of cropland and open areas, with forest and water bodies slightly increasing, and wetlands remaining stagnant. The CA–ANN model effectively captured the nonlinear, spatially dependent land-transition patterns using open-source tools. These findings provided useful information for sustainable land-use planning and environmental management, with the potential to incorporate spatial modeling into regional development strategies in rapidly urbanizing areas of Texas. Full article

Quantifying the complex spatial–temporal correlations and generating representative high-dimensional coupled scenario sets are essential for the robust planning and risk assessment of large-scale hybrid energy systems (HESs). Although numerous models have been developed for this purpose, as the number of plants scales up to hundreds, existing approaches suffer from the curse of dimensionality, often resulting in high computational burden, posterior collapse, and distributional oversmoothing. To address this gap, this paper proposes a Spatial-Clustering Conditional Variational Auto-Encoder (SC-CVAE) framework, which employs spatial clustering to decompose the high-dimensional global problem into tractable subproblems and integrates adaptive deep networks to accurately capture high-dimensional spatiotemporal complementarity. Case studies on the Yalong River energy base, featuring massive wind and solar integration, demonstrate that SC-CVAE reduces global spatial correlation error by 56% compared to the Independent Baseline, while achieving a 2.4-fold computational speedup over the monolithic Global Baseline. Crucially, by mitigating posterior collapse to alleviate oversmoothing effects inherent in high-dimensional VAEs, the proposed framework improves the capture rate of high-impact extreme events by 3.4-fold and reduces the Energy Score error by 65%. This high-fidelity reconstruction of tail characteristics provides a more reliable basis for identifying supply-deficit risks in basin-wide HESs. The proposed framework enables scalable and high-fidelity generative modeling, establishing a robust methodology for stochastic optimization and long-term security assessments in the global transition toward decarbonized power systems. Full article

Addressing the low efficiency associated with single-frequency serial acquisition in urban exploration using traditional electrical resistivity tomography (ERT) instruments, this study introduces a Frequency Division Multiplexing Electrical Resistivity Tomography (FDM-ERT) method and hardware system. By utilizing transmission modules that simultaneously output AC excitation signals at distinct frequencies, coupled with receiver modules that enable multi-channel parallel acquisition and data transmission, the system achieves a “one-time layout, multi-frequency synchronous measurement” workflow. Laboratory tests under controlled conditions and preliminary field tests conducted at the Xiangjiang River beach demonstrate that this method maintains relatively high consistency with traditional single-frequency measurements. The relative error of apparent resistivity across frequency points remains below 2%, with an inversion root mean square error (RMSE) of 0.4%. Furthermore, the multi-frequency synchronous mode reduces total measurement time by approximately 66.7%. While these results were obtained in relatively controlled environments, they substantiate the core feasibility of the FDM-ERT system for multi-frequency synchronous measurement, providing a certain hardware foundation for subsequent validation and optimization in complex, real-world urban settings. Full article

Active Channel Width (ACW) provides a robust indicator for tracking river corridor dynamics, yet automated extraction from multisensory imagery remains limited by spatial and temporal variability in spectral conditions. We developed and validated a workflow in Google Earth Engine (GEE) to delineate the active channel using multispectral indices derived from annual composite Landsat and Sentinel-2 imagery. The indices include the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI). The 34 km study segment of the Lircay River (Chile) served as a demonstration site undergoing substantial geomorphic change over a 20-year period (2003–2023) that spanned a decade-long mega drought (2010–2023) and two major floods (2006, 2023). Multispectral index thresholds were calibrated using manually digitized active channel polygons for a reference year and validated for five different years within the study period to assess their spatial transferability across reaches and temporal stability under varying hydrologic regimes. Sentinel-2 annual composites with the MNDWI-EVI pairing achieved the highest overall accuracy in estimating ACW (mean Kling-Gupta Efficiency = 0.72; Percent Bias = 12.69 across study reaches). Threshold values were tested at the cross-sectional and reach scales. Using cross-section-specific thresholds enhanced the accuracy of ACW estimation, indicating that threshold performance is strongly conditioned by the local characteristics present in the immediate surroundings of each cross section. These results suggest that spectral threshold selection is sensitive to small scale factors that vary across the river corridor, underscoring the need to explicitly consider local geomorphic and ecological conditions when defining thresholds. This reproducible, open-source workflow links automated channel delineation with cross-section-based morphology and explicitly quantifies uncertainty from spatiotemporal spectral variability. It enables high-resolution, repeatable measurements of river corridor change and underscores the need to consider evolving spectral and vegetation conditions when interpreting remotely sensed geomorphic indicators. Full article

Suspended sediment concentration (SSC) strongly influences estuarine erosion–deposition processes, navigation safety, and coastal engineering stability. However, conventional remote sensing techniques are limited to surface SSC and cannot characterize vertical sediment structures. In this study, Landsat 8 OLI imagery was combined with in situ SSC profiles from six stations in the Guan River Estuary–Haizhou Bay system to retrieve full-depth sediment distributions. A band-combination inversion model using (B3 + B2)/B1 achieved the highest accuracy (R² = 0.679), and an improved vertical distribution model was developed by incorporating turbulent shear (G) into the Rouse framework. Results indicate that surface SSC ranged from 0.15 to 0.86 kg/m³, while middle- and bottom-layer SSC reached up to 1.20 kg/m³ and 1.77 kg/m³, respectively, exhibiting a consistent east–high and west–low spatial pattern. Settling velocity (SSV) varied from 3 × 10⁻⁶ to 1.49 × 10⁻² m/s and showed a positive correlation with SSC at low concentrations and a negative correlation at high concentrations due to flocculation effects. This integrated framework provides a rapid, low-cost method for full-water-column sediment assessment in estuaries and coastal zones, supporting engineering design, navigation maintenance, and sediment management. A better understanding of sediment transport processes in Haizhou Bay is important for maintaining shoreline stability and ecological balance in this semi-enclosed coastal system. The findings of this study provide a scientific basis for sediment management and environmental regulation, which can contribute to the long-term sustainable development of coastal environments in the Yellow Sea region. Full article

Flood risk maps play a critical role in land-use regulation, infrastructure planning, and community preparedness, which are key components of sustainable and climate-resilient urban development. Their production, however, remains costly, labor-intensive, and time-demanding as it relies on simulation-driven workflows that combine hydrodynamic modeling with expert interpretation and extensive validation. To address this issue from a sustainability perspective, we develop a novel, practical, and near-real-time large language model (LLM)-based framework to support property-level flood risk assessment. This framework, which synthesizes geospatial, hydrological, infrastructural, and historical flood information, extends existing research and explores novel risk estimation methods for use in planning practice. Using Brisbane, Australia, as a case study, we develop Flood-LLM, a multi-agent system that transforms multi-source urban datasets into structured textual representations, models diverse neighborhood conditions, and fine-tunes a reasoning model using expert-assessed risk classifications. The results show that Flood-LLM can reproduce official flood risk labels for creek, river, storm tide, and overland-flow hazards with reasonable accuracy, outperforming classical machine learning, deep learning, and untuned LLM baselines. Visual and quantitative analyses indicate that the framework demonstrates a qualitatively nuanced capability to capture salient spatial patterns present in the official maps, while generating a textual chain-of-thought providing a transparent audit trail for its labeling decisions. These findings suggest that such LLM-based approaches can produce potential complementary tools to expert-reviewed planning classifications and support more sustainable, adaptive flood risk management by enabling timely map production and updates that facilitate informed decision-making in rapidly changing environmental conditions. Full article

The urban ecosystem constitutes a complex adaptive system comprising interdependent subsystems—environment, population, infrastructure, public services, environmental governance, and socio-economic factors. Conventional system dynamics (SD) modeling relies on expert-derived causal assumptions, which have limitations in objectivity, transferability, and adaptability. To solve these, this study develops a data-driven SD modeling framework that infers causal structures from time-series data of 38 sustainability indicators. The framework integrates multiple causal inference techniques to identify causal relationships among variables, then systematically identifies stock variables and constructs an SD simulation model. Applying it to panel data from 41 cities in China’s Yangtze River Delta (2013–2022), the study characterizes the causal network topology, interaction patterns between subsystems, dominant feedback loops, and temporal evolution trajectories of key stock variables. Results show: (1) There is significant cross-city variation in causal network structure due to differences in urban development and institutional configurations; (2) Environmental conditions are the most frequently affected terminal node with an average normalized causal strength of 0.277, higher than other subsystems; (3) Several cross-subsystem positive and negative feedback loops are identified, highlighting potential path dependencies and intervention-sensitive nodes for sustainable urban transitions. This study provides a replicable, comparable, and scalable framework for urban sustainable development analysis, offering data-driven support for smart city management and policy formulation. Full article

Wetlands play a vital role in the global carbon cycle and serve as critical carbon sink systems. However, increasing human disturbances and land-use changes have led to widespread wetland degradation, severely weakening their carbon sequestration capacity. This study investigated the Huixian Wetland in the Li River Basin of Southwest China to examine the impacts of wetland degradation on soil physicochemical properties, organic carbon fractions, and carbon fluxes. Based on vegetation and environmental conditions, the wetland was classified into four degradation gradients: non-degraded (ND), slightly degraded (SD), moderately degraded (MD), and heavily degraded (HD), and their spatial differences were systematically analyzed. The results showed that with increasing degradation, soil moisture, total nitrogen, and total phosphorus significantly decreased, whereas soil bulk density and electrical conductivity exhibited an increasing trend. Total organic carbon and active organic carbon fractions, including readily oxidizable organic carbon, light fraction organic carbon, microbial biomass carbon, and dissolved organic carbon, exhibited a pronounced decreasing trend along the degradation gradient, with the decline being most evident in the HD area. Among the labile carbon fractions, microbial biomass carbon (MBC) and light fraction organic carbon (LFOC) exhibited the most drastic declines in heavily degraded areas, indicating their high sensitivity as early warning indicators of wetland degradation. Observations of CO₂ fluxes revealed that from April to September, the net ecosystem exchange (NEE) was negative across all areas, indicating that the wetland functioned as a carbon sink overall. However, NEE values increased with higher degradation levels, suggesting a progressive decline in the carbon sequestration capacity of the wetland; ecosystem respiration (ER) peaked in July and increased with the degree of degradation. The findings indicate that wetland degradation leads to soil environment deterioration, reduction in organic carbon storage, and enhanced CO₂ emissions, ultimately weakening its carbon sink function. To enhance carbon sequestration capacity and maintain ecological functions, sustainable management strategies such as hydrological restoration and vegetation reconstruction are recommended. This study provides a scientific basis for wetland ecological conservation and carbon management in the context of climate change. Full article

Urban and peri-urban river systems subjected to intensive agriculture are vulnerable to diffuse metal(loid) inputs, yet the integration of hydrological compartments, bioindicators, and human health risk remains poorly explored. This study investigated the seasonal dynamics, bioaccumulation patterns, and potential human health risks associated with metal(loid)s in the Santa Virgem River (Brazil–Paraguay border), using water from backwater zones and three plant groups (Apiaceae angiosperms, mosses, and the liverwort Dumortiera sp.). Water and plant samples were collected during five seasonal campaigns (2019–2020) and analyzed by ICP OES. Multivariate analysis (PCA) was applied, and biological accumulation coefficients (BAC) and chronic daily intake (CDI) were estimated for adults and children under different ingestion scenarios. Results showed that Mg, Fe, K, S, and P dominated water chemistry, while As, Cd, Cr, Cu, Pb, and Se were mostly below detection limits. PCA explained 77.6% of total variance, distinguishing agricultural and hydrological phases. Bryophytes exhibited markedly higher BAC values, particularly for Mn (up to 2.3 × 10⁵) and Fe, compared with Apiaceae. CDI and hazard assessment indicated negligible non-carcinogenic risk for most elements (HQ < 1), except phosphorus, which dominated the Hazard Index due to its low reference dose. Overall, the results demonstrate that hydrodynamic conditions and plant functional traits jointly control metal(loid) dynamics, highlighting the value of multispecies biomonitoring in peri-urban river systems. Full article

Dust and sand storms occurring in northern China are strongly controlled by near-surface aerodynamics, yet the spatial heterogeneity of these processes remains poorly understood. We obtained field measurements of the wind above gobis, sandy surfaces, and dry lakebeds in the Hexi Corridor Desert and Heihe River Basin, and sandy surfaces in northern China. First, the slope of wind profile (a₁) reveals distinct drag reversal with increasing wind speed: under low winds, a₁ increases from sandy to dry lakebed to gobi surfaces, whereas under high winds, actively saltating sandy surfaces exhibit the highest a₁, surpassing gobi and dry lakebed. Second, the dynamic feedback between sediment transport and aerodynamics is clear: at below-threshold winds, friction velocity ($u_{*}$) and aerodynamic roughness length (z₀) are lowest for sand; however, as wind speed increases to initiate significant saltation, the sandy surface develops the highest $u_{*}$ and z₀, highlighting the dominant role of grain-borne roughness. Third, the focal height (z_f) shows regional disparity, varying by up to two orders of magnitude for both sandy and gobi surfaces, with a strong correlation to local gravel coverage. This work provides spatially explicit parameterizations of surface type, offering a physical basis for modeling dust emission and transport in northern China and similar arid regions globally. Such parameterizations are essential for developing reliable early warning systems and evidence-based land management strategies. These advances contribute directly to ecosystem sustainability and community resilience in vulnerable arid and semi-arid regions under climate change. Full article

Intrabasinal low uplifts in lacustrine rift basins are key targets for sedimentological and petroleum geological research, as they can act as local source areas and exert critical controls on intrabasinal “source-to-sink” systems. Due to the discontinuous sediment supply, these systems often demonstrate the subtle and intermittent nature, and their roles in the development of depositional systems are usually overlooked. To clarify the controlling effect of intrabasinal local provenances on sedimentary system evolution, this study reconstructed the dynamic tectonic evolution of the Weixinan Low Uplift in the Beibuwan Basin, and systematically analyzed its control on “source-to-sink” systems and sedimentary filling using integrated high-resolution 3D seismic, core, well logging and geochemical data. Our results demonstrate that the activity of Fault 3 dominated the paleogeomorphic evolution of the Weixinan Low Uplift and its surrounding areas, which further governed the spatiotemporal development of the “source-to-sink” system and the distribution of sedimentary systems, with distinct evolutionary stages as follows: During the Ls2 Member stage (48.6–40.4 Ma), Fault 3 was inactive, the Weixinan Low Uplift was manifested as a gently dipping subaqueous slope under the influence of regional lacustrine transgression, and only small-scale braided river deltas were developed on the slope belt with weak sediment supply from the Qixi Uplift. During the Ls1 Member stage (40.4–33.9 Ma), the Ls13 Sub-member stage (lower Ls1 Member stage) was characterized by initiation of Fault 3 with segmented activity, triggering the formation of the Eastern Sub-sag of the Haizhong Sag and subaqueous uplift of the Weixinan Low Uplift; clastic sediments from the central Qixi Uplift were transported northeastward, developed braided river deltas and large-scale basin-floor lacustrine fans. In the Ls12 Sub-member stage (middle Ls1 Member stage), Fault 3 continued to propagate and was gradually linked, leading to further uplift of the Weixinan Low Uplift and expansion of the Haizhong Sag; Clastic materials from the central Qixi Uplift were almost entirely trapped in the Eastern Sub-sag of the Haizhong Sag. During the Ls11 Sub-member stage (upper Ls1 Member stage), further intensification of Fault 3 activity caused the Weixinan Low Uplift to be subaerially exposed and evolve into an intrabasinal local provenance, which supplied clastic sediments to surrounding sags and developed braided river deltas on the gentle slope belts and small-scale lacustrine fans on the lower slope. This study demonstrates that the tectonic evolution of the Weixinan Low Uplift has induced prominent changes in the basin paleogeomorphology, which in turn triggered dynamic shifts in the provenance and sediment transport pathways, and thus gave rise to complex local “source-to-sink” systems and depositional styles. Full article

The Aksu River Basin, the main headwater of the Tarim River, contributes more than 70% of the main stream’s runoff and is therefore critical in maintaining hydrological stability in this arid river system. In recent decades, rapid oasis expansion and growing agricultural water withdrawals have intensified competition for surface and groundwater, posing increasing ecological risks to the downstream Tarim River Basin. To quantitatively characterize river–groundwater hydrological responses under intensive water use, we combined statistical analysis, field observations, and distributed hydrological modeling within a basin-scale conceptual framework. Multiple lines of evidence—water level monitoring, hydrochemical tracers, stable isotopes, and the integrated surface–groundwater model MIKE SHE—were used to identify river–groundwater interaction mechanisms in the Aksu alluvial plain. Results reveal a typical three-stage spatial exchange pattern: river recharge to groundwater in the upstream reach, groundwater discharge to the river in the midstream, and renewed river infiltration to groundwater downstream. The patterns inferred from water levels, hydrochemistry, and isotopes are broadly consistent, while water-level data better resolve left–right bank asymmetry. The MIKE SHE model supports the seasonal bidirectional exchange dynamics and reproduces runoff behavior with acceptable performance (RMSE and residual standard deviation within 20% of observed means and R² > 0.7 during both calibration (2010–2017) and validation (2018–2021)). The proposed multi-evidence framework captures the spatio-temporal variability of river–groundwater interactions in arid regions and provides spatially differentiated guidance for conjunctive surface–groundwater regulation and integrated water resources management in the Tarim River Basin. Full article

Large woody debris (LWD), a key indicator of riparian vegetation disturbance and river corridor dynamic, plays a crucial role in habitat complexity, geomorphic dynamics and river management. Accurate mapping and monitoring of LWDs are therefore essential for river process analysis and ecosystem assessment, particularly in highly dynamic braided river systems. However, mapping and monitoring LWD remains challenging due to its variable morphology, spectral similarity, and dynamics of braided river. Advancements in artificial intelligence (AI) and unmanned aerial vehicle (UAV) remote sensing offer promising opportunities for addressing these applied geoscience challenges. In this study, we evaluate different AI techniques for the accurate detection of LWD in braided rivers. Specifically, using RGB-UAV imagery, we test two DL models, U-Net and DeepLabv3+, and compare them to other classifiers to identify the most accurate and transferable approach. The results indicate that the DeepLabv3+ method effectively captures the actual spatial distribution of LWD, and two-class classifications were more efficient than multi-class ones. Furthermore, the DL model demonstrated strong transferability when applied to a different spatiotemporal area, highlighting its utility for applied geoscience investigations and river management. Full article

Flood control briefings are critical emergency response documents that provide timely decision support for urban safety and regional development under climate change challenges. However, existing large language models (LLMs) face significant difficulties in domain-specific adaptation, content controllability, and logical consistency when processing complex water conservancy data. This study aims to develop a robust automated text generation method that ensures high accuracy and logical rigor for flood prevention in the Lixiahe region. We propose an IP-CoT method that integrates Chain-of-Thought (CoT) reasoning for structured information extraction and an Inverse Prompting (IP) mechanism with beam search to optimize content relevance using the DeepSeek-R1 model. Validated on a constructed dataset comprising flood control records from the Lixia River network from 2010 to 2024, the proposed method achieved an accuracy rate of 95.32% in the verification of emotional attributes, which is 2% to 15% higher than most traditional models. Additionally, in the verification of thematic attributes, fluency and diversity were improved, showing significant enhancements compared to the baseline model. This approach significantly enhances the quality and efficiency of domain-specific text generation, providing a reliable intelligent solution for modernizing regional flood control decision-making systems. Full article