Legacy phosphorus (P) stored in soils from historical human activities can be remobilized under changing hydrological conditions, undermining water quality management and causing delayed pollution impacts. However, current management strategies lack the evaluation of legacy dynamics and the consideration of spatiotemporal lags, which limits the effectiveness of pollution control. In this study, we developed a novel management framework by integrating hydrological connectivity with interpretable machine learning. Using the aggregated index of connectivity, we elucidate how hydrological connectivity governs legacy P transport and identify environmental thresholds that modulate this relationship. Results revealed a phosphorus transport lag of more than 10 years in the Xiangjiang River Basin, with legacy P contributing more than 80% of the total phosphorus flux. Agricultural areas with high connectivity values were identified as primary sources of legacy P, whereas forested areas with low connectivity values temporarily retained phosphorus but posed sudden-release risks. We further found that precipitation and ground surface temperature were key environmental factors with critical thresholds influencing the relative importance of hydrological connectivity in driving legacy P transport. Accordingly, we propose a dynamic, threshold-based management framework incorporating hydrological connectivity and environmental drivers to guide the targeted mitigation of long-term legacy P impacts across subtropical watersheds.
Rivers are increasingly exposed to combined anthropogenic and climate-driven pressures, particularly along rural-to-urban gradients where land use, population density, and contaminant sources progressively intensify downstream. In parallel, prolonged droughts followed by intense rainfall events are becoming more frequent, potentially enhancing contaminant accumulation during dry periods and their rapid mobilization during subsequent first-flush events. However, the interactions between these hydrological extremes and spatial anthropogenic gradients in controlling river geochemistry remain insufficiently understood. This study investigates the spatial and seasonal evolution of river chemistry along the Greve River (Tuscany, Italy), a tributary of the Arno River, characterized by a marked rural-to-urban transition, during the exceptional 2022 European drought and subsequent heavy rainfall event. Six sampling campaigns were conducted between May and October 2022, combining high-resolution spatial monitoring with geochemical and isotopic tracers, including total dissolved solids (TDS), nutrients, and trace elements. The results reveal a progressive downstream enrichment in anthropogenic tracers (e.g., NO3- and K+, up to 9 mg/L and 6.6 mg/L, respectively) and pronounced evaporative isotope enrichment during the driest months (slope of the evaporation line ~ 4), indicating strong hydrological stress conditions. The first intense rainfall event triggered rapid remobilization of accumulated material and heterogeneous contaminant transport dynamics. Elevated nutrient concentrations (e.g., NH4+, up to 1.4 mg/L) in downstream sectors further reflect the combined influence of hydrological stress and urban inputs. These findings demonstrate how climate-driven hydrological extremes can amplify contaminant mobilization along urbanized river continuums. The integration of isotopic and geochemical tracers provides a transferable framework for assessing water-quality vulnerability in river basins increasingly exposed to hydroclimatic instability.
Hydrological seasonality can influence the exposure patterns and risk priorities of dissolved heavy metals (HMs) in semi-arid river systems. In this study, operationally dissolved HMs, defined as the < 0.45 μm fraction, were investigated at 35 sampling sites in the Weihe River basin during low-flow, mean-flow, and high-flow seasons to characterize seasonal shifts in concentration patterns, metal associations, and human-health and ecological risks. Total operationally dissolved HM concentrations increased from 33.21 μg/L in the low-flow season to 40.16 and 43.35 μg/L in the mean-flow and high-flow seasons, respectively. Cr, As, and Hg were generally enriched during low flow, whereas Al, Zn, Cu, and Pb were elevated during high flow. Multivariate analyses revealed a relatively persistent V-Cr association, partly involving As and Cu, together with seasonally strengthened mixed-metal groupings during the mean-flow and high-flow seasons. Human-health risk was dominated by Cr and As through the drinking-water pathway and reached the highest value in children during the low-flow season (6.13 × 10-6 yr-1). Relative ecological priority was more strongly associated with Hg and Cu, which showed low HC5 values of 1.36 and 6.35 μg/L, respectively, although all PAF values remained below 5%. These findings indicate that hydrological seasonality can decouple human-health and ecological risk priorities, highlighting the need for season-specific exposure assessment and risk management.
The microplastic pollution of freshwater systems has been a global environmental concern, yet there is a lack of knowledge on tropical rivers. This paper examined the relationships between spatiotemporal microplastic abundance and river water physicochemical and hydrological parameters in the surface water of four rivers of Sarawak in Malaysia (Baram, Miri, Sibuti, and Niah), followed by ecological risk assessment using PLI, PHI, and PERI indices. Surface waters were collected during dry and wet seasons and analyzed using stereomicroscopy and ATR-FTIR spectroscopy. The abundance of microplastics ranged from 6.33 ± 1.53 to 27.67 ± 2.31 items/L. Two-way ANOVA revealed a significant spatial difference (p < 0.001), but no seasonal difference (p > 0.05). The microplastics were predominantly fibers, in black, blue, and clear colors, and 100-1000 µm in size. Dominant microplastic polymers were polyethylene and polypropylene. During the dry season, microplastic abundance correlated positively with salinity (ρ = 0.799) and temperature (ρ = 0.708) but negatively correlated with pH (ρ = -0.764). No significant correlations were found in the wet season. PLI values exceeded 1 in all rivers, while PHI values varied among rivers and seasons (7.16-536.46) (Category II: moderate-IV: danger). The presence of high-hazard polymers, such as rubber, elevated polymer-based risks and increased PERI values in some locations. This study provides baseline knowledge on the seasonal effects and anthropogenic pressures on microplastic distribution in tropical rivers in Sarawak, Malaysia.
Semi-arid wetland vegetation is increasingly threatened by limited water availability. Managing environmental flows can help reduce impacts of river regulation and drought, but effective implementation requires timely and predictive quantification of vegetation response. To address this, we develop a scenario analysis framework that enables the continuous quantification of vegetation response under alternative environmental flow scenarios and accounts for predictive uncertainty. The framework allows for probabilistic comparisons of vegetation outcomes and the robust identification of critical thresholds for environmental flow intervention. We demonstrate this framework by applying a coupled eco-hydrological model to simulate inundation extent and vegetation condition in the Narran Lakes, Murray-Darling Basin in Australia, over 58 years. The scenario analysis reveals three key outcomes. First, at annual or five-year scales, there is no clear evidence of vegetation condition decline over the last twenty years, but prolonged drought periods have increased the duration of vegetation stress. Second, by explicitly accounting for predictive uncertainty, we identified the critical vegetation threshold: during the growing season, a leaf area index below 0.4-0.6 indicates a critical state that requires environmental flows to improve vegetation condition. Third, scenario analysis indicates that additional environmental water is needed to improve vegetation from critical conditions, although improvement in condition reaches a saturation point at around 50,000 ML for a flow event in the Narran Lakes.
Land use change and intense rainfall, particularly in urban areas, are among the key factors contributing to flood occurrences. This study investigates the impact of land use and rainfall changes on flood events using the Soil Conservation Service - Technical Release 20 (SCS-TR20) and the Santa Barbara Urban Hydrograph method (SBUH) models within the HydroCAD software. A key novelty of this study is the comparative application of the SBUH model-never before used in Iran for rainfall-runoff simulation-alongside SCS-TR20, combined with a detailed multi-temporal land use change analysis over a near-decade period (2010-2019) in the Doab-Veysian watershed. For this analysis, characteristics of five sub-basins (Bahramjo, Karganeh, Chenar-Khoshkeh, Cham-Anjir, and Doab-Veysian) including drainage area, time of concentration, curve number (CN), and cross-sectional geometry of river reaches were defined in HydroCAD. A 24-hour Type II rainfall pattern was selected as the optimal regional rainfall model. The models were then calibrated based on two rainfall events (24/04/2010 and 21/10/2014) and validated using a third rainfall event (01/04/2019). The results demonstrated that both the SCS-TR20 and SBUH models are capable of simulating floods with R² and Nash-Sutcliffe efficiency (NSE) values exceeding 90%, though these metrics were slightly lower for the SBUH model compared to SCS-TR20. An analysis of land use changes between 2010 and 2019 revealed that the expansion of dry farming (from 53,000 to 66,000 hectares), the decline of poor and moderate rangelands (from 50,000 to 43,000 hectares), and the reduction of forests (from 116,000 to 108,000 hectares) were additional factors increasing flood risks. Moreover, residential, commercial, and infrastructural developments in urban and rural areas expanded significantly, with such lands growing from 2,744 hectares in 2010 to 6,897 hectares in 2019. These changes led to an increase in the curve number (CN) from 81.72 to 83.47 in the Doab-Veysian watershed and from 91 to 92 in the urban area of Khorramabad. Combined with an increase in rainfall (from 21 mm to 116 mm), these changes resulted in a substantial rise in peak flood discharge (from 106 to 2,227 m³/s) and flood volume (from 3.83 to 98.33 million m³) according to the SCS-TR20 model. The SBUH model also indicated an increase in discharge (from 77.79 to 1,687 m³/s). These findings highlight the significant influence of land use changes and heavy rainfall on flood occurrences. In urban areas, flood discharge estimated by the SCS-TR20 model increased from 11.97 m³/s in 2010 to 135.87 m³/s in 2019, while flood volume rose from 0.146 to 2.42 million m³ over the same period. Correspondingly, the average flood depth across the urban watershed increased from 6 mm to 66 mm. These figures underscore the substantial impact of land use changes and increased rainfall in exacerbating floods in urban areas. In conclusion, the findings indicate that although both models perform effectively, SCS-TR20 simulates peak discharge more accurately than SBUH. Furthermore, rainfall was introduced as the major and dominant factor in flood generation in the region, although the role of antecedent soil moisture (AMC) land use changes and the CN should not be overlooked.
Rapid urbanization, population growth, and climate-induced water stress have made interbasin water transfers (IBTs) a central strategy for augmenting urban water supplies worldwide. Istanbul, Türkiye's largest metropolitan area, relies heavily on water transfers to meet its rising demand. However, concerns around the long-term sustainability, efficiency, and equity of these transfers, particularly their impact on donor regions, remain underexamined. This study investigates the hydrological and social performance of Istanbul's major water transfers, with a focus on balancing urban water security with environmental and regional justice considerations. A socio-hydrological case study approach was used to assess three major water transfers supplying Istanbul from Düzce, Tekirdağ, and Kırklareli provinces between 2000 and 2023. Two quantitative indicators were applied: the Natural Efficiency Index, which compares transfer volumes to renewable freshwater availability in both donor and recipient basins; and the Stress Relief Index, which analyzes social efficiency by evaluating the water demand- and population-weighted change in water stress resulting from each transfer. In addition, a document-based qualitative analysis grounded in hydrosocial theory was conducted to explore governance narratives, trade-offs, and regional impacts. Findings reveal substantial variation in both hydrological and social efficiency across the three IBTs. The Melen system transfer from Düzce demonstrates a relatively high efficiency in relieving Istanbul's water stress with moderate ecological cost. In contrast, transfers from the Istranca system in Tekirdağ, and Kırklareli exhibit lower natural and social efficiency, suggesting a disproportionate burden on already water-stressed source regions. The qualitative assessment highlights that these transfers, while effective in meeting Istanbul's supply needs, often reinforce centralized, supply-driven governance models and overlook the socio-environmental impacts on donor regions. Recurring droughts, reduced streamflow, and competition with local agricultural needs further exacerbate these tensions. The study demonstrates that water transfers can create uneven outcomes between donor and recipient regions, particularly when hydrological limitations and social vulnerabilities are not explicitly addressed in planning. While large-scale transfers may appear effective in securing urban water supply, they may also deepen regional inequalities and environmental risks. The findings call for a shift toward integrated and adaptive water governance models that consider long-term hydrological sustainability, ecosystem health, and inter-regional equity. For cities like Istanbul, this means rethinking reliance on external water sources and investing in demand management, local resilience, and participatory planning frameworks.
We present 42 years (1979-2021) of elemental and isotopic data (Cu, Zn, Cd, Pb) from oysters (Crassostrea gigas) in the Gironde Estuary (SW France), integrated with historical river discharge records to disentangle anthropogenic and environmental drivers of trace metal bioaccumulation. Cu and Zn concentrations in oyster tissues show pronounced interannual variability-up to twofold-closely tracking river flow. Their fluctuations likely reflect hydrological controls on the mobilization, speciation, and bioavailability of legacy-bound metals rather than new contaminant inputs. Cd concentrations decline more steadily, consistent with the attenuation of upstream industrial sources, while Pb displays a more complex trend shaped by legacy decline, hydrological variability, and possible changes in source composition. Stable isotope signatures (δ66Zn, δ114Cd, δ65Cu, 206Pb/207Pb) remained remarkably stable over four decades, indicating a constant source apportionment with a dominance of anthropogenic sources on the observed fingerprints. Nevertheless, metal-specific fractionation processes imprint distinct signatures: Zn is enriched in heavy isotopes due to kinetic adsorption onto suspended particles, whereas Cd is isotopically light, consistent with selective desorption via chloride complexation. Slightly heavier δ65Cu values likely reflect vineyard runoff coupled with complexation in the dissolved phase. Pb isotope ratios reveal a gradual shift toward radiogenic values, shaped by diminishing industrial inputs and increasing lithogenic contributions. Together, these findings demonstrate that integrating hydrological data with multi-isotope analysis can disentangle source persistence from estuarine reactivity, offering a framework for interpreting long-term contamination dynamics under changing hydrological regimes.
This research delivers a comprehensive future-oriented multi-dimensional drought appraisal for Tiruchirappalli District, Tamil Nadu, India, by inter-linking Google Earth Engine (GEE) cloud computing, machine learning algorithms, geospatial analysis, and socioeconomic indicators. Four primary drought dimensions-meteorological, hydrological, agricultural, and socioeconomic vulnerability-are interfaced into a holistic drought evaluation framework during 2014-2025, representing an epoch with intensified climate variability in South India. Meteorological drought is assessed through the Standardized Precipitation Index (SPI), while hydrological drought is assessed through the Standardized Water Level Index (SWI) from the observations of the Public Works Department (PWD) groundwater. Agricultural drought conditions are considered using multi-sensor satellite indices on the GEE platform, namely, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Precipitation Condition Index (PCI). Socioeconomic vulnerability parameters, which included population density, literacy, household density, and workforce-related characteristics. These socioeconomic indicators were standardized and weighted separately to derive a socioeconomic vulnerability map, while meteorological, hydrological, and agricultural drought indicators were integrated into Multi-Drought Severity Index. The spatio-temporal assessment shows cyclic drought occurrences, which became stronger in the 2015-2018 period and revived from 2021 onward, particularly in the years 2023-2025. LULC analysis with Random Forest classification for 2014, 2018, and 2025 pointed toward rapid urbanization and consequent land-use change, which increases drought vulnerability. Future drought manifestation for 2035 was done by employing multi-year geospatial trends, historical RF-based LULC spatio-temporal change analysis, ANN-based LULC-2035 predictions, and long-term drought indicators. The integrated ANN-based LULC-2035 and MDSI-2035 analysis predicts extreme drought in Thuraiyur, Omandhur, and Thachankurichi, with varying severity across other regions. This study demonstrates the successful application of multi-indicator drought modelling together with machine-learning-driven land-cover prediction, thereby presenting a scalable framework for regional drought risk assessment and climate-resilient planning.
Increased frequency and intensity of extreme weather events, driven by climate change, are expected to alter hydrological and water quality processes in the Connecticut River watershed. This study aims to model the temporal and spatial impacts of climate change on the watershed's hydrology and nutrient dynamics. We used the Hydrologic and Water Quality System (HAWQS), which incorporates the Soil and Water Assessment Tool (SWAT), to establish a baseline scenario and assess two climate scenarios: Coupled Model Intercomparison Project (CMIP5-) Representative Concentration Pathways (RCP) 4.5 and RCP 8.5. The model was validated using observed data from the United States Geological Survey (USGS) gage sites. Our results show that both climate scenarios will cause significant changes in hydrological processes, including a shift in precipitation seasonality, with more rainfall expected during winter and early spring. These changes will affect nutrient loading, shifting the seasonal peaks of nitrogen and phosphorus. Notably, the nitrogen-to-phosphorus ratio is projected to decrease across the entire watershed under both climate scenarios. These findings suggest that the altered hydrological regime and nutrient dynamics could have cascading effects on aquatic ecosystems, impacting phytoplankton and algal growth, with important implications for future nutrient management strategies.
Cascade dam development is transforming freshwater ecosystems worldwide, yet the long-term responses of fish beta diversity to progressive hydrological disturbances remain poorly understood. We analyzed data from the upper reaches of the Yellow River on the Qinghai-Tibet Plateau spanning five decades (pre-1975 to 2023), across a 1983-km river corridor influenced by sequential cascade dams. We quantified temporal shifts in taxonomic, phylogenetic, and functional beta diversity and determined the contributions of species extinctions and introductions to multidimensional beta-diversity change. Although damming is generally expected to promote biotic homogenization by simplifying habitats and favoring widespread generalist or non-native species, thereby making communities more similar, our results revealed pervasive biotic differentiation, with communities becoming increasingly distinct across all diversity dimensions. Beta diversity followed a nonlinear trajectory, increasing sharply during early dam construction and stabilizing in later stages. Both native species extinctions and non-native introductions contributed to these patterns, but their relative importance shifted over time, with introductions showing a greater contribution at later stages. Non-native species, particularly those introduced from outside the Yellow River basin, exerted disproportionate effects on phylogenetic and functional beta diversity owing to their distinct evolutionary histories and ecological traits. Environmental predictors such as reservoir age, cumulative upstream reservoir capacity, and individual reservoir capacity were strongly associated with patterns in beta diversity, reflecting the cumulative and spatially structured effects of long-term hydrological modification. Our findings provide quantitative, multidimensional evidence that non-native species introductions can outweigh native species extinctions in shaping beta diversity under sequential disturbance. We highlight the importance of proactive management of non-native species and long-term, multidimensional biodiversity monitoring to detect postdisturbance dynamics and guide timely conservation interventions. Collectively, our results offer a framework for identifying and anticipating the cumulative ecological consequences of cascade dam development and other chronic ecosystem modifications in the Anthropocene. Dinámicas no lineales de la diversidad beta en peces y la influencia desproporcionada de las especies no nativas en los ríos represados en cascada Resumen La construcción de presas en cascada está transformando los ecosistemas de agua dulce en todo el mundo; sin embargo, aún se conoce poco sobre las respuestas a largo plazo de la diversidad beta de los peces ante las perturbaciones hidrológicas progresivas. Analizamos datos de la cuenca alta del río Amarillo, en la meseta del Tíbet‐Qinghai, que abarcan cinco décadas (desde antes de 1975 hasta 2023), a lo largo de un corredor fluvial de 1,983 km afectado por una sucesión de presas en cascada. Cuantificamos los cambios temporales en la diversidad beta taxonómica, filogenética y funcional, y determinamos las contribuciones de las extinciones e introducciones de especies al cambio multidimensional de la diversidad beta. Aunque, en general, se espera que la construcción de presas promueva la homogeneización biótica al simplificar los hábitats y favorecer la expansión de especies generalistas o no autóctonas, haciendo así que las comunidades sean más similares, nuestros resultados revelaron una diferenciación biótica generalizada, con comunidades que se vuelven cada vez más distintas en todas las dimensiones de la diversidad. La diversidad beta siguió una trayectoria no lineal, aumentando bruscamente durante las primeras fases de la construcción de las presas y estabilizándose en etapas posteriores. Tanto las extinciones de especies nativas como las introducciones de especies no nativas contribuyeron a estos patrones, pero su importancia relativa cambió con el tiempo, y las introducciones mostraron una mayor contribución en etapas posteriores. Las especies no nativas, en particular las introducidas desde fuera de la cuenca del río Amarillo, ejercieron efectos desproporcionados sobre la diversidad beta filogenética y funcional debido a sus distintas historias evolutivas y rasgos ecológicos. Los predictores ambientales, como la antigüedad del embalse, la capacidad acumulada de los embalses situados aguas arriba y la capacidad individual de cada embalse, se asociaron estrechamente con los patrones de diversidad beta, lo que refleja los efectos acumulativos y espacialmente estructurados de la modificación hidrológica a largo plazo. Nuestros hallazgos brindan evidencia cuantitativa y multidimensional de que la introducción de especies no nativas puede superar la extinción de especies autóctonas a la hora de configurar la diversidad beta bajo perturbaciones secuenciales. Destacamos la importancia de la gestión proactiva de las especies no nativas y del seguimiento multidimensional y a largo plazo de la biodiversidad para detectar la dinámica posterior a las perturbaciones y orientar intervenciones de conservación oportunas. En conjunto, nuestros resultados ofrecen un marco para identificar y anticipar las consecuencias ecológicas acumulativas del desarrollo de presas en cascada y otras modificaciones crónicas de los ecosistemas en el Antropoceno. 梯级水坝建设正在全球范围内深刻改变淡水生态系统, 但鱼类β多样性对渐进性水文扰动的长期响应仍缺乏充分认识。本研究基于青藏高原黄河上游跨越五十年的鱼类群落数据(1975年前至2023年), 分析了受梯级水坝影响的1983 km河段中鱼类群落β多样性的长期变化。我们量化了分类、系统发育和功能β多样性的时间变化, 并评估了物种灭绝和引入对多维度β多样性变化的相对贡献。虽然通常认为筑坝会通过简化生境、促进广布性泛化种或外来物种扩散而推动生物同质化, 使群落组成趋于相似, 但我们的结果显示, 鱼类群落在所有多样性维度上均表现出普遍的生物分异, 即不同群落之间逐渐变得更加不同。β多样性呈现非线性变化轨迹, 在早期筑坝阶段快速增加, 并在后期阶段趋于稳定。土著物种灭绝和外来物种引入共同塑造了这些多样性格局变化, 但二者的相对重要性随时间发生转变, 其中外来物种引入在后期阶段的贡献更大。外来物种, 尤其是来自黄河流域以外的引入物种, 因其独特的进化历史和生态性状, 对系统发育和功能β多样性产生了不成比例的影响。水库年龄、上游累积水库库容以及单个水库库容等环境因素与β多样性格局显著相关, 反映了长期水文改造所产生的累积效应和空间结构化影响。本研究提供了定量的多维度证据, 表明在连续扰动背景下, 外来物种引入在塑造β多样性变化方面可能超过土著物种灭绝的影响。我们的结果强调, 应加强对外来物种的前瞻性管理, 并开展长期、多维度的生物多样性监测, 以识别扰动后的生态动态并指导及时的保护干预。总体而言, 本研究为识别和预判人类世背景下梯级水坝开发及其他长期生态系统改造所产生的累积生态后果提供了分析框架。 梯级开发河流中鱼类β多样性的非线性动态及外来物种对β多样性的不成比例影响.
This study aims to identify key ecological indicator in fragmented river systems and to evaluate the application of the serial discontinuity concept (SDC) in this context. Focusing on a cascade of 11 dams on the West River, we assessed key hydrological and environmental variables, with a parallel analysis centered on the size-fractionated chlorophyll-a (Chl-a) across dry and wet period. The results showed that total Chl-a concentration exhibited no significant seasonal difference (dry period: 2.47 ± 1.70 μg/L; wet period: 2.35 ± 2.42 μg/L), suggesting that dam regulation buffers seasonal hydrological fluctuations. Nano-phytoplankton (2-20 μm) dominated the community in both periods (50-70% of total Chl-a), while the proportion of micro-phytoplankton (>20 μm) increased by approximately 10% during the wet period. The spatial patterns of total Chl-a during the wet period aligned with SDC predictions, a consistency observed both at individual dams and across the entire dam system. Redundancy analysis revealed that water transparency was the primary factor governing phytoplankton size structure. Furthermore, the pattern of phytoplankton size structure tracked the distribution of the dominant species, Ulothrix sp. Overall, this study quantitatively confirms the predictive utility of SDC for cascade dam systems and demonstrates that phytoplankton size structure is a sensitive and effective ecological indicator in fragmented rivers.
Accurate multi-step runoff forecasting over China is important for flood control, water-resource management, and regional hydrological assessment. However, existing data-driven methods often struggle to jointly capture temporal variations at different time scales and the directional hydrological dependencies imposed by river networks, which limits forecasting accuracy and spatial structural consistency. To address this issue, this paper proposes a spatiotemporal forecasting framework that combines cross-scale temporal fusion with basin-topology-guided spatial modeling. Specifically, a multi-scale temporal module with cross-scale gating is introduced to adaptively integrate short-, medium-, and long-term runoff variations, while a basin-topology attention module incorporates upstream-downstream connectivity into spatial dependency learning. Experiments are conducted on a China-scale gridded runoff forecasting benchmark derived from the publicly available GloFAS Historical dataset through spatial filtering, valid-region masking, and forecasting-oriented sample construction. The proposed method achieves better overall performance than representative baselines in terms of MAE, RMSE, PSNR, and SSIM. In the overall comparison, it reaches MAE 0.0269 and RMSE 0.0603 in normalized log-scale runoff units, PSNR 24.39, and SSIM 0.9273, while maintaining a moderate parameter size and practical inference efficiency. The results demonstrate that the proposed framework reduces numerical errors and better preserves the spatial patterns of runoff fields. Ablation studies further confirm that both cross-scale gating and basin-topology attention contribute consistently to the overall improvement.
Antimicrobial resistance (AMR) in aquatic ecosystems is an escalating One Health concern. However, viable antibiotic-resistant bacteria (ARB)-particularly pathogenic strains-and their mobility remain poorly characterized at hydrological interfaces such as river confluences. Here, we integrated culture enrichment, high-throughput 16S rRNA gene sequencing, isolate phenotyping and whole-genome analysis to profile ARB and antibiotic-resistant bacterial pathogens (ARBPs) in sediments from the Fenhe River-Yellow River confluence. Non-selective enrichment reduced community complexity yet uniquely recovered dozens of rare taxa absent from direct sequencing. Antibiotic enrichment induced pronounced, drug-specific ARB shifts; antibiotic type explained more variance (19.3%) than hydrological region (11.2%). Pathogen signals were strongly amplified by enrichment, and ARBP communities retained significant regional clustering. Notably, the confluence hydrodynamic region (CHR) consistently exhibited the highest ARBP richness. Of the 121 recovered isolates, 94.2% were phenotypically resistant and 73.5% were multidrug-resistant; 89.3% matched in situ ASVs, bridging community profiles and cultivable strains. We recovered seven high-risk multidrug-resistant pathogens (belonging to Pseudomonas, Acinetobacter, Aeromonas) as viable isolates, even though they were rare or undetected by direct sequencing. Whole-genome sequencing revealed 658 virulence factors and 312 antibiotic-resistance genes (ARGs). Clinically relevant determinants (e.g., AAC(6')-Iaa, OXA-917, OprN) were embedded within mobile genetic elements, including transposons, plasmid-like contigs, and integrative and conjugative elements (ICEs). The edeine acetyltransferase gene edeQ showed 100% nucleotide identity to alleles from clinical sources, indicating overlap between environmental and clinical resistomes. Collectively, our findings highlight river confluences as priority surveillance nodes and demonstrate that culture-enriched sequencing more effectively quantifies viable AMR hazards than sequencing alone.
Glacial ecosystems on the Tibetan Plateau undergo pronounced hydrological shifts across the glacial ablation cycle, driven by the onset and retreat of the Indian summer monsoon. To elucidate how transitions between four distinct hydrological ablation stages (pre-ablation, early ablation, late ablation, and frozen) shape microbial community structures and antibiotic resistance gene (ARG) profiles, we analyzed 112 samples collected across four stages from multiple glacier catchments on the southeastern Tibetan Plateau using metagenomic sequencing. Our results indicated that warmer stages favored thermotolerant Proteobacteria and reduced overall community diversity and evenness. ARG abundances exhibited ablation-dependent fluctuations, with Betaproteobacteria identified as predominant potential hosts. Furthermore, ARGs and virulence factors associated with mobile genetic elements were enriched during early and late ablation stages relative to the frozen stage, suggesting elevated potential for horizontal gene transfer coinciding with peak meltwater discharge. Notably, while upstream meltwaters generally exhibited higher ARG abundances, the upstream-downstream disparity tended to diminish from the pre-ablation to the late ablation stage, likely reflecting enhanced microbial mixing driven by glacier melt. Together, these findings reveal that glacier meltwater microbiomes are primarily shaped by ablation dynamics rather than spatial heterogeneity. More importantly, dynamics across the glacial ablation cycle drive shifts in meltwater hydrology that facilitate the downstream environmental mobility of glacial resistomes, posing growing antimicrobial resistance risks within the One Health framework.
This study investigates the combined effects of precipitation and landfill age on leachate characteristics using one-way ANOVA to identify statistically significant differences among impact groups. The primary objective is to evaluate how climatic conditions and landfill age jointly influence both organic and inorganic leachate constituents. The results indicate that precipitation acts as a dominant controlling factor, although its influence is strongly modulated by landfill age and site-specific conditions. In low-precipitation climates, statistically significant temporal variations were observed in organic parameters (COD, BOD, and BOD/COD), particularly during the early stages of landfilling. In contrast, no statistically significant age-related differences were detected in high-precipitation regions, suggesting that dilution and wash-out mechanisms dominate over time-dependent biological processes. For inorganic constituents, chloride and ammonia exhibit weak or inconsistent statistical differences, while calcium and selected metals show more complex, non-linear behavior. These findings demonstrate that one-way ANOVA is an effective tool for identifying parameters sensitive to climatic and temporal factors, while also revealing parameters that are statistically unaffected by such influences. Overall, the results confirm that landfill age plays a significant role in shaping leachate composition in dry climates, whereas hydrological processes are the dominant controlling factors in wet climates.
Karst aquifers are a crucial source of water, supplying approximately 10% of the global population and often serving as the sole water resource in certain regions. These aquifers are characterized by highly heterogeneous flow dynamics and exhibit significant temporal variability in both hydrodynamic and physico-chemical conditions. Continuous monitoring of these parameters is essential for advancing our understanding of karst aquifer functioning; however, comprehensive, high-frequency datasets remain limited. We present a comprehensive dataset covering 13 karst springs monitored across nine observatories of the French Karst National Observatory Service (SNO KARST), spanning various hydroclimatic regions (oceanic, mountainous, Mediterranean). The SNO KARST aims to strengthen knowledge-sharing and to promote cross-disciplinary research on karst systems at the national scale. The dataset includes: (1) hydrodynamic data (water level, discharge), and (2) physico-chemical data (water temperature, electric conductivity, pH, dissolved oxygen, turbidity, Total Organic Carbon (TOC), Dissolved Organic Carbon (DOC), nitrate, and organic matter fluorescence). Spanning over a decade of continuous monitoring, such a dataset is required for the analysis of the hydrological and physico-chemical dynamics of karst aquifers, the assessment of their vulnerability to pollution and climate change, and the modeling of hydrodynamic and hydrochemical variables, ultimately aiming to improve the management and preservation of these critical water resources in contrasted contexts.
Harmful cyanobacteria proliferation in drinking water reservoirs presents considerable risks to water quality and ecosystem stability. This research evaluated the effectiveness of natural, artificial, and induced natural mixing processes in controlling cyanobacterial overgrowth in a warm mono-mictic reservoir, using an interpretable machine learning (ML) algorithm. Throughout a 28-month monitoring period, cyanobacterial abundance and essential environmental variables were examined across four separate hydrological periods. The dominant cyanobacterial species (genera) detected included Pseudanabaena sp., Cylindrospermopsis raciborskii, Raphidiopsis curvata, and Microcystis sp.. The results indicated that all three mixing strategies significantly reduced surface cyanobacterial abundance, with artificial mixing process demonstrating the most rapid suppression effect. Explainable ML models, XGBoost enhanced with SHAP analysis, identified mixing depth (Zmix) and light availability (Zeu/Zmix) as the key factors controlling cyanobacterial overgrowth, rather than water temperature (WT) and nutrient levels. Additionally, the study observed a shift in filamentous cyanobacteria toward smaller morphological forms under mixed conditions. These outcomes offered a basis for improving the operational management of water-lifting aerators (WLAs) in reservoirs. Furthermore, the work highlights the utility of interpretable ML in clarifying intricate ecological processes and supporting sustainable water quality management strategies.
Large-scale restoration in the Brazilian Atlantic Forest is expected to recover biodiversity, carbon stocks, and other ecosystem functions while also supporting rural livelihoods. Yet the evidence supporting these claimed outcomes remains uneven, especially when distinguishing reported outcomes from causal evidence based on baselines, controls, counterfactuals, or long-term monitoring. We assessed peer-reviewed studies and a contextual set of documented initiatives to synthesize reported environmental, social, and economic findings together with governance and financing conditions associated with restoration in the biome. We also distinguished between direct field measurements, model-based estimates, descriptive reporting, and perception-based evidence. Across the reviewed sample, direct evidence was strongest for carbon and biodiversity, weaker for soil, and much more limited for hydrological and social effects. On-site studies often reported partial recovery relative to mature forests, especially in naturally regenerated areas, while model-based studies were more common in identifying where restoration might deliver multiple gains or lower opportunity costs. Social and financial evidence focused mainly on jobs, income, participation, opportunity costs, and implementation costs, but remained heterogeneous and rarely relied on standardized indicators. Governance was reported mainly through implementation conditions, including technical support, incentives, local participation, and coordination across actors and scales. The reviewed literature now covers more topics, but evidence remains uneven across ecological, social, financial, and governance dimensions. These gaps still limit comparison across cases and complicate restoration planning, monitoring, and policy alignment in the Atlantic Forest.
Polycyclic aromatic hydrocarbons (PAHs) in urban estuaries exhibit sharp concentration shifts during rainfall events, yet their transient redistribution and compositional restructuring remain poorly resolved due to the mismatch between laboratory specificity and field-scale monitoring frequency. Conventional chromatography provides chemical resolution but lacks temporal coverage, whereas autonomous underwater drones deliver high-frequency measurements without molecular specificity. Here we bridge this monitoring gap by transferring laboratory-derived spectral information into sensor-based field models using knowledge distillation, enabling process-resolving PAHs assessment at scale. To overcome limited sample availability under rainfall conditions, a variational autoencoder expanded 142 observations thirtyfold, stabilizing model transfer. The integrated framework achieved an R² of 0.92 for ΣPAHs, improving predictive performance by 28%. Large-scale deployment across 59,392 drone measurements revealed rainfall-triggered surges dominated by high-molecular-weight PAHs and dynamic hotspot migration within the estuary. Interpretable analyses further indicate how spectral signatures reorganize along specific sensor pathways under hydrological perturbation. By coupling laboratory specificity with autonomous sensing, this approach establishes a scalable strategy for resolving pollutant dynamics in rainfall-impacted urban waters.