Search Results (2,192)

Charybdis (Gonioneptunus) bimaculata (Miers, 1886), C. (Charybdis) japonica (A. Milne-Edwards, 1861), and C. (Charybdis) miles (De Haan, 1835) are dominant target species harvested by small-scale artisanal fisheries in the coastal waters of China. Characterizing their spatial distribution and interannual population variability is critical for sustainable crustacean stock conservation. Previous investigations of these three Charybdis congeners have been limited to short-duration, single-season, or geographically restricted descriptive surveys, with no systematic cross-species comparison of seasonal habitat use. This has created major knowledge gaps regarding their long-term spatiotemporal dynamics and ecological niche partitioning. We conducted standardized bottom-trawl surveys aboard research vessels across the southern Yellow Sea and East China Sea from 2018 to 2021 to quantify numerical abundance and biomass of the three congeneric crabs. Two species distribution modeling frameworks—Generalized Additive Models (GAMs) and Boosted Regression Trees (BRTs)—were implemented to generate fine-resolution seasonal habitat suitability maps for each species. We identified minimum thermal thresholds for species presence: bottom water temperature > 8 °C for C. bimaculata and >10 °C for C. miles. Core aggregation zones of C. bimaculata and C. japonica occurred in waters adjacent to the Yangtze River Estuary (32–34° N), whereas C. miles predominated in offshore habitats of the southern East China Sea (27–31° N). In this study, C. bimaculata favors shallow seas with warm bottom water and euryhaline conditions, C. japonica prefers low-salinity coastal waters with cold bottom water, and C. miles thrives in deep, high-salinity offshore waters with eurythermal tolerance. The findings of this study can contribute to the sustainable fisheries management and conservation of these species. Full article

In crisis times, traditional models of water quality assessment and water resources management lose their effectiveness. In the current conditions of local climate change, accidental pollution, emergencies or military operations, there is an urgent need to transition from traditional descriptive hydrochemical monitoring toward intelligent analysis of spatial-temporal datasets. In this paper, the integrated approach combining spatial cluster analysis, GIS-based visualization, and machine learning is proposed for assessing the surface water quality under conditions of limited and incomplete hydrochemical monitoring data. A geospatial assessment of nutrient impacts on surface water quality was conducted using 192 hydrochemical observations collected during the 2024–2025 monitoring period at eight state monitoring stations located in the basins of the Teteriv, Uzh, Irsha, Ubort, Sluch, Hnylopiat, and Voznia rivers, Polissia, Ukraine. Permutation feature importance analysis based on the Random Forest model showed that nitrate concentration accounted for approximately 75% of the total relative importance, whereas phosphate concentration contributed approximately 14%, indicating that these variables were the most informative predictors among the investigated hydrochemical parameters. The latter parameters are associated with dissolved oxygen variability among the analyzed hydrochemical parameters. According to the results of this study, three interpretable groups of monitoring stations were formed: Cluster 1, representing moderate water quality with increased nutrient pressure, Cluster 2, representing comparatively favourable background conditions, and Cluster 3, representing a nitrate-dominated hydrochemical type. The Random Forest model demonstrated limited predictive performance (R² = 0.154), indicating that nutrient-related variables alone explain only a small proportion of dissolved oxygen variability. Hence, additional factors, including hydrological conditions, water temperature, organic matter decomposition, biological productivity, and catchment-specific characteristics, also play an important role in shaping oxygen dynamics. The spatial visualization of cluster membership showed that geographical location alone does not fully determine the surface water quality patterns in Ukrainian Polissia. Instead, the local catchment characteristics and land-use conditions appear to exert a stronger influence on the formation of nutrient-related water quality differences. The authors propose to employ the spatial cluster analysis and machine learning as a basic supporting tool for the transition from retrospective interpretation of hydrochemical monitoring data to predictive and adaptive water resources management. The integration of geospatial analysis and machine learning provides a practical decision-support framework for the early detection of anomalies, identification of potential pollution sources, and prioritization of river sub-basins for implementing nature-based solutions. Full article

Microplastics (MPs), defined as plastic particles smaller than 5 mm in diameter, are an emerging class of environmental contaminants of global concern. As the “Water Tower of Asia” and a critical global ecological barrier, the environmental condition of the Qinghai–Tibet Plateau has a direct influence on the ecological security of major river systems and the well-being of populations downstream. MPs have now been detected across multiple environmental compartments on the plateau, including soils, water bodies, and glaciers. Given the fragility and ecological uniqueness of the region, systematic investigation of plastic pollution here is essential for safeguarding its ecological security. Based on current research, existing data on MP pollution across the Qinghai–Tibet Plateau are reviewed and synthesized. Evidence suggests that the abundance of MP varies significantly across different environmental media in various regions and is influenced by multiple factors. Two major potential sources are identified: local anthropogenic activities and transboundary inputs via atmospheric transport and other pathways. The unique environmental conditions of the region, such as intense ultraviolet radiation, large day–night temperature variation, and frequent high-wind events, provide a distinctive setting for the migration, dispersion, transformation, and degradation of MPs across environmental matrices. Understanding the distribution, sources, and migration patterns of microplastics on the Qinghai–Tibet Plateau will help facilitate sustainable environmental management, ecosystem conservation, and pollution control in these fragile high-altitude regions. Full article

Soil moisture is a key indicator of agricultural and hydrological drought, but its meteorological controls vary across temporal scales. Using ESA CCI soil moisture products from 2010 to 2022, this study investigated soil moisture variability in the Yangtze River Basin at weekly, monthly, and annual scales. The product was first validated using ground observations and ERA5 reanalysis data, and a generalized additive model (GAM) was then applied to quantify the relative contributions of precipitation, temperature, wind speed, and VPD under normal conditions and during three extreme drought events. The validation showed that ESA CCI soil moisture captured basin-scale variations well, with mean absolute deviations of 0.0460, 0.0434, and 0.0072 m³/m³ in the upper, middle, and lower reaches, respectively, and a basin-wide RMSE of 0.0127 m³/m³ against ERA5. The attribution results revealed a clear scale-dependent shift in soil moisture controls. At the weekly scale, VPD dominated soil moisture variability, with a basin-wide average contribution of 47.04% and a maximum contribution of 55.92% in the lower reaches, indicating that short-term soil drying is mainly driven by VPD. At the monthly scale, precipitation became the primary control, with a basin-wide average contribution of 36.62% and a maximum contribution of 44.65% in the upper reaches, reflecting the role of accumulated rainfall recharge in maintaining soil moisture storage. During extreme drought events, the monthly-scale dominance of precipitation weakened, and precipitation, VPD, temperature, and wind speed each contributed approximately 20–30%, suggesting that drought development results from the combined effects of reduced water input and enhanced atmospheric water loss. These findings indicate that precipitation-based drought monitoring may underestimate rapid soil drying risks, whereas incorporating atmospheric demand indicators such as VPD can improve drought early warning and water resource management under a warming climate. Full article

Gulf sturgeon are federally listed as “threatened” and are native to seven rivers along the northern Gulf of Mexico. Populations natal to the Pascagoula River have one verified spawning reach in the lower Bouie River, near Hattiesburg, MS. This reach is modified by four aggregate mining areas, referred to as pits. Researchers observed post-spawn Gulf sturgeon over-summering in pits rather than migrating to downstream holding areas in the Pascagoula River. We leverage acoustic telemetry and environmental data to assess drivers of arrival and departure from the Bouie River, describe vertical stratification in the water column in pits, and compare pit temperatures to those in holding areas. Generalized mixed linear modeling demonstrated that arrival was associated with lower discharge rates, whereas departure was associated with higher surface water temperatures and greater daily temperature extremes. Sturgeon had varying occupancy rates in pits, with four over-summering events over 3 years. Stratification formed in the pits in May and persisted through October, with bottom and middle strata exhibiting cooler temperatures than holding habitats. Cooler bottom temperatures indicate potential for thermal refuge in the pits. However, persistent stratification leads to low dissolved oxygen in bottom waters, warranting further research into the environmental stressors of this system. Full article

Poyang Lake is a typical floodplain lake hydrologically linked to the middle-lower Yangtze River. Its peripheral saucer-shaped sub-lakes undergo seasonal hydrological alternation between isolation and connectivity driven by annual water level fluctuations. To date, the coupling between seasonal hydrological rhythms and zooplankton metacommunity assembly in these saucer floodplain lakes remains poorly understood. In this study, four representative saucer sub-lakes within the Poyang Lake National Nature Reserve (Banghu, Zhonghuchi, Shahu and Dahuchi) were selected as research objects. Seasonal quantitative zooplankton surveys and synchronous hydro-physicochemical monitoring were conducted across four hydrological phases from 2012 to 2013: low-water isolation (spring), high-water connectivity (summer), water recession transition (autumn), and extreme low-water closure (winter). We systematically explored how water level fluctuations regulate zooplankton metacommunity assembly processes. In total, 95 zooplankton species were identified, among which rotifers (68 species) constituted the absolute dominant taxon, and 15 species were identified as year-round absolute dominants exhibiting regular seasonal succession in response to hydrological shifts. Our results demonstrate that hydrological connectivity plays a strong role in regulating the relative strength of species dispersal and environmental filtering. During spring and winter, complete hydrological isolation blocked inter-lake species exchange, making local environmental filtering the main driver of metacommunity assembly. Isolated sub-lakes harbored fewer total species but abundant endemic taxa, accompanied by pronounced spatial community heterogeneity. When water levels exceeded the 17.3 m threshold in summer, full water exchange between sub-lakes and the main lake coincided with widespread species dispersal, which appeared to represent the primary assembly process. The four sub-lakes shared 23 co-occurring species during this period, with highly homogenized community structures and minimal spatial differentiation (Global test: R = 0.47, p = 0.001). In autumn, receding water levels weakened inter-lake connectivity, such that environmental filtering and dispersal jointly structured zooplankton metacommunities; significant spatiotemporal differences were detected in zooplankton density, biomass and α-diversity (p < 0.05). Critical hydro-physicochemical factors of environmental filtering included water temperature, chlorophyll a, pH, electrical conductivity and turbidity, yet the dominant limiting factor varied spatially across sub-lakes due to divergent basin topography and water residence time. Species co-occurrence network analysis revealed that zooplankton communities achieved the highest stability during the fully connected summer phase. In early autumn water drawdown, species association networks became fragmented, and community stability reached its annual minimum. This study elucidates the mechanistic link between floodplain lake hydrological rhythms and zooplankton metacommunity assembly, providing theoretical support and baseline data for wetland biodiversity conservation and ecosystem management of Poyang Lake floodplains. Full article

Balancing food production, ecosystem service value, and agricultural irrigation water consumption is a major challenge for sustainable agricultural development in China. However, quantitative evidence at the national scale remains limited on whether crop planting structure optimization derived from models can effectively achieve coordination among these three objectives. Existing studies mainly focus on individual crops or localized regions and rarely integrate the spatiotemporal evolution, influencing factors, and multi-objective optimization of major staple crops within a unified framework. This study developed a progressive framework integrating spatiotemporal evolution analysis, influencing factor identification, and planting structure optimization for wheat, rice, and maize. Spatial autocorrelation analysis, center-of-gravity shift analysis, and pixel-based image differencing were applied to reveal crop evolution patterns across China from 2000 to 2025. A five-dimensional indicator system comprising 17 quantitative indicators was developed through multiple experiments using four large language models, and the Random Forest algorithm was employed to identify key influencing factors and their relative importance. Based on these factors, optimization constraints were constructed, and a multi-objective fuzzy linear programming model combined with the NSGA-II algorithm was used to determine optimal crop area allocation across 28 provincial-level regions. The three staple crops exhibited a significant pattern of northward shift, eastward expansion, and southern contraction. Precipitation and market accessibility were common core influencing factors, ranking among the top five factors in all nine Random Forest models. Crop-specific factors, including soil available phosphorus for rice, accumulated active temperature for wheat, and soil pH for maize, explained differences in spatial responses among crops and provided a scientific basis for optimization modeling and coordinated improvement of food production, ecosystem service value, and irrigation water consumption. The optimized scheme increased total grain output by 3.6%, improved ecosystem service value by 11.0%, and reduced irrigation water consumption by 45.7% compared with the actual planting structure, all 28 provinces achieved improvement or stability in the three indicators simultaneously. Based on optimized crop allocation patterns, national planting structures were summarized into regional models, including a rice–maize dual-core system in Northeast China, wheat–maize rotation in the Huang-Huai-Hai Plain, rice-dominated systems in the middle and lower Yangtze River Basin and South China, water-efficient dryland farming in Northwest China, and a diversified balanced system in Southwest China. These findings provide a quantitative reference for optimizing China’s staple crop planting structure. Full article

The formation and disappearance of paleolakes are sensitive indicators of environmental evolution in arid and semi-arid regions. Their disappearance records comprehensive information on regional hydrological conditions, climatic changes, and tectonic activities, thereby offering unique research value for elucidating the environmental driving mechanisms behind water resource changes in historical human settlements and the concomitant rise and fall of civilizations. This study analyzed the contact interface between extensively distributed lacustrine deposits and the overlying aeolian dune sands around Tongwan City using optically stimulated luminescence (OSL), and combined with relevant documentary evidence to elucidate the relationship between paleolake disappearance and the abandonment of the ancient city. The OSL dating results demonstrated that the disappearance of paleolakes and the initiation of desertification around Tongwan City were mainly concentrated between about 1200 and 900 years before present, closely corresponding to the abandonment of Tongwan City in AD 994. Field investigations additionally revealed that well-developed fluvial erosion surfaces are pervasively present at the top of lacustrine deposits around Tongwan City, while multiple fluvial terraces are exposed along the Wuding River. These features collectively indicate that regional crustal uplift event led to fluvial incision. The results indicated that the abandonment of Tongwan City was not attributable solely to climatic aridification, but instead resulted from the combined influences of favorable hydrothermal conditions during the High-Temperature Period of the Northern Song (HTNS, AD 994–1094) and regional tectonic uplift. Tectonic uplift facilitated deep incision of the Wuding River valley, while increased precipitation during the warm period enhanced surface water infiltration and drainage, resulting in a significant decline in groundwater levels, the disappearance of paleolakes, and ultimately the depletion of water resources upon which the ancient city relied. These findings provide new evidence from an environmental geological perspective and present a key scientific explanation for the paradox that Tongwan City was abandoned during a relatively warm climatic interval. Furthermore, the coupled mechanism of tectonic uplift, fluvial incision, sharp groundwater decline, and societal collapse revealed in this study not only provides a valuable reference for investigating the abandonment of ancient cities in arid and semi-arid regions during historical periods, but also offers important implications for assessing water resource vulnerability of ancient settlements on analogous geomorphic units under global climate change, and may serve as a geological–historical warning and reference for water security management in human habitations under current and future warm-climate conditions. Full article

The spring floods that occurred in 2024 in western and northern Kazakhstan caused extensive damage. Our understanding of runoff formation processes under frozen-soil conditions remains limited. In this study, we apply a coupled hydrothermal model as a case study to explicitly simulate vertical heat and water transport, phase transitions, snow dynamics, and reduced infiltration capacity due to cryogenic pore blockage (ice-filled pores). The model is based on regular meteorological data from 65 stations in five regions covering the full hydrological cycle (August–May) of 2021 and 2024. A multilevel diagnostic check showed that soil temperature is reproduced with a median $R^2$ of 0.962 and NSE of 0.888, the frozen/thawed surface condition corresponds to WMO (World Meteorological Organization) standards on approximately 91% of days, and water balance agreement reaches 86.2% (56 out of 65 stations). The model reflects the regional variability of the 2024 flood. In the northern regions (Kostanay, North Kazakhstan), snowfall was above average, and modeled runoff increased compared to 2021 (for example, at the Sergeevka station, it increased by a factor of four). In the western regions, the trends were mixed: the strongest relative increase in runoff was recorded in the Atyrau region (+119%), whilst in the West Kazakhstan region, the increase was more modest (+19%), and in the Aktobe region, runoff increased by 60%. The key mechanism—the time lag between rapid snowmelt and delayed soil thaw—is clearly evident: peaks in snowmelt occur when the soil remains frozen, infiltration capacity decreases, and the runoff potential index (RPI) exceeds 1 for extended periods. Although the model does not simulate the river channel, its ability to diagnose runoff generation conditions at the slope scale offers a diagnostic framework for identifying runoff-conducive conditions in regions with limited data, rather than a physically validated tool for flood-prone area identification. The results show that the 2024 flood period was characterized by abnormally high water inflow and hydrothermal conditions consistent with a temporal mismatch between water supply and the recovery of soil infiltration capacity. Because the RPI is a diagnostic indicator constructed from water input and infiltration capacity, these results should be interpreted as evidence of conditions conducive to runoff generation rather than as an independent causal verification of the flood mechanism. Full article

Alternanthera philoxeroides (A. philoxeroides) is one of the worst invasive alien species in the world and poses serious threats to both ecological security and agricultural production in China. However, the effects of environmental factors on its growth across different habitat types remain insufficiently understood. To reveal the determining natural factors influencing the growth of A. philoxeroides, fieldwork, including 50 sample quadrats in six field sites, was conducted in the Yangtze River Basin of Hubei Province. In every quadrat, soil properties, soil moisture, pH, soil temperature, soil nutrients (N, P, and K), light intensity, plant cover, and aboveground plant fresh biomass were measured. Cluster analysis showed that A. philoxeroides habitats can be divided into five cluster groups: wetland, grassland, forest understory, farmland and aquatic communities. The results showed that both total community biomass and A. philoxeroides biomass were positively correlated with water content, whereas no significant relationships were found between biomass and soil nutrients, including N, P, and K. The biomass of A. philoxeroides was significantly higher in aquatic habitats, while no significant differences were observed among the other four terrestrial habitats. These findings suggest that hydrological management, together with early control in aquatic habitats, may be an effective strategy to limit the spread of A. philoxeroides. Full article

Accurately depicting the spatiotemporal evolution patterns and driving mechanisms of soil drought is of great significance for regional agricultural drought warning and adaptive management of water resources. There are still shortcomings in the existing research in terms of indicator applicability, mutation detection and trend persistence collaborative diagnosis, as well as the quantification of multi-scale meteorological driving factors. In response to the above issues, this study constructs the Standardized Soil Moisture Index (SSMI) based on the principle of soil moisture supply and demand balance, and comprehensively uses BFAST structure mutation detection, autocorrelation correction Mann–Kendall (MMK) trend test, Hurst persistence analysis, and cross-wavelet transform methods to systematically analyze soil drought in the Haihe River Basin (HRB) from 2000 to 2022. Using the FLDAS reanalysis dataset and multi-source meteorological observation data, this study revealed the stage changes, seasonal evolution characteristics, and dominant meteorological driving factors of soil drought in the watershed. Key findings include: (1) the most significant structural breakpoint occurred in May 2005 (confidence interval: March–November 2005); (2) spring exhibited the strongest drying trend (mean Z_s = −0.51), while autumn showed the strongest anti-persistence (mean Hurst = 0.41), making it the most vulnerable season for future soil moisture state shifts; (3) evapotranspiration was the dominant meteorological driver, with the highest significant coherence area percentage (SCAP), followed by air humidity, soil moisture, soil temperature, air temperature, and precipitation in descending order of influence. Full article

Understanding how the spatiotemporal variability of precipitation affects ecosystem respiration (RE) is central to carbon–climate feedback in climate-smart agriculture, yet remains unresolved for the alpine agricultural region of the southern Qinghai–Tibet Plateau, where flux observations are sparse. Using 25 years (2000–2024) of monthly gridded climate and remote sensing data for the Yarlung Zangbo River Basin and Its Two Tributaries Basin, we developed a flux tower-constrained reference–respiration (R_ref) environment-matching model in which R_ref varies with the enhanced vegetation index (EVI) and land surface temperature (LST) to correct the Lloyd–Taylor parameterization. The correction reduced the RE root mean square error by 54.8% (1.04 → 0.47 gC·m⁻²·month⁻¹) and eliminated systematic bias (+0.80 → −0.001) relative to an independent gridded RECO product. We then constructed a multidimensional index of precipitation variability (intra-annual concentration, interannual variability, long-term trend, spatial clustering) and combined random forest, spatial regression, lag analysis, and structural equation modeling (SEM) to disentangle direct and indirect pathways from precipitation variability to RE. The central finding is an indirect-conduction mechanism: precipitation concentration (PCI) affects RE almost entirely through vegetation productivity (PCI → GPP → RE, indirect effect −0.676) rather than directly (direct effect +0.076, opposite in sign), because low temperature and high soil water holding capacity buffer the immediate soil moisture response. The basin functions as a net carbon source (mean NEP = −0.550 gC·m⁻²·month⁻¹) with a significant warming-driven interannual RE increase (Sen’s slope = 0.0025 yr⁻¹, p = 0.022) that is independent of the stable precipitation total. The framework offers a transferable paradigm for carbon flux attribution in alpine regions under sparse observation. Full article

Dissolved oxygen (DO) dynamics in river systems exhibit complex nonlinear and non-stationary characteristics driven by interactions among meteorological and water-quality factors, posing challenges for accurate prediction and identification of dominant drivers. To address these challenges, this study establishes a VMD-Bayes-LSTM model by integrating Variational Mode Decomposition (VMD), Bayesian optimization, and Long Short-Term Memory (LSTM) networks. The datasets used in this study were collected from monitoring data at eight sites in the Yangtze River Basin, including water-quality and meteorological factors. Compared with several benchmark models, the VMD-Bayes-LSTM model achieves the best performance in daily DO concentration prediction, with mean R², KGE, MSE, and MAE values of 0.876, 0.900, 0.208, and 0.247, respectively. Meanwhile, this model supports multi-step prediction and achieves five-day-ahead forecasting of DO concentrations. Based on this, a SHAP value-interpretable VMD-Bayes-LSTM model is constructed. Analysis indicates that water temperature and average air temperature are the primary predictive factors at the CC, PT, DQ, LS, NJG, and ZT sites with a cumulative contribution rate exceeding 60%, reaching a maximum of 90% at the PT site; notably, at the LD and LJG sites, turbidity emerges as the dominant water-quality parameter, accounting for 59.6% and 36.2% of the predictive contribution, respectively. Full article

Water pollution can compromise the ecological integrity of freshwater habitats, thereby exposing freshwater communities to sources of pollution such as wastewater and agricultural and industrial discharges. Diatoms are key organisms because of their role as primary producers; thus, they have been regularly used as bioindicators to assess water quality. The diatom assemblages were collected from the surface of small stones in four rivers (El Carmen, Jipiro, San Simón, and Curitroje) across three zones defined according to a land-use gradient (high, medium, and low). A total of 54 diatom species were recorded. The El Carmen stream showed the highest total richness (29 species), followed by Curitroje (21), Jipiro (19), and San Simón (11). The results revealed significant changes in diatom richness, abundance, diversity indices, trophic index (IT) and community structure associated with both stream and zone. Following a similar pattern, temperature, conductivity, TDS (total dissolved solids), and pH also strongly influenced community composition. Achnanthidium subatomus, Sellaphora lanceolata, Odontidium mesodon were indicators of the high zone and show a general preference for zones characterized by conserved riparian vegetation, fast, clear and oxygenated water. On the other hand, Gomphonema reichardtii, Rhopalodia musculus, Gomphonema clavatulum, Gomphonema subclavatum, Gomphonema variostriatum, Eunotia incisa were indicators of a low zone with heavy organic pollution, water pollution, and environmental changes in temperature, conductivity, TDS and pH. Full article

Freshwater phytoplankton communities are currently being influenced by ongoing climate change. This study aimed to investigate the effects of warming and dissolved inorganic carbon (DIC) on chlorophyll-a concentration and physicochemical variables in a freshwater ecosystem in mesocosm conditions. A 21-day experiment was conducted using freshwater collected from the Tigris River. Four treatments were established: control, warming (+3 °C), DIC, and DIC + 3 °C, with three replicates for each treatment. Chlorophyll-a, DIC, water temperature, pH, dissolved oxygen (DO), nitrate (NO₃⁻), and phosphate (PO₄³⁻) were measured throughout the experiment. The results showed that chlorophyll-a concentration increased under warming and DIC treatments. The combined treatment (DIC + 3 °C) showed the highest chlorophyll-a concentration. Water temperature was significantly higher in the warming treatment compared to DIC treatment. In addition, pH, DO, nitrate, and phosphate varied among treatments and over time. Overall, the combined effects of warming and DIC were greater than those of the individual treatments. These findings indicate that warming and increased inorganic carbon availability may play an important role in influencing chlorophyll-a concentration, carbon cycling, nutrient dynamics, and water quality in freshwater ecosystems. Full article