Search Results (656)

Although dissolved rare earth elements (REEs) are increasingly utilized to explore riverine geochemical processes, their seasonal responses and source controls remain poorly constrained. This study investigated the sources of dissolved REEs in the Mun River and evaluated their responses to monsoon-driven hydrological processes. ΣREE ranged from 16.41 to 467.01 ng/L. Most river waters displayed negative Ce anomalies and positive Eu anomalies, whereas anthropogenic Gd enrichment was localized. ΣREE concentrations showed no significant seasonal difference in upper reaches but were significantly higher during wet season in the middle-lower reaches. Light REEs (LREE) and Middle REEs (MREE) generally exhibited stronger wet-season responses than Heavy REEs (HREE) in the middle and lower reaches. Positive matrix factorization (PMF) analysis identified three major sources: soil erosion and surface runoff (34.9% of ΣREE), bedrock weathering (16.6%), and mixed inputs influenced by agricultural runoff and wastewater discharge (48.5%). Cluster analysis further revealed a longitudinal transition from the upstream weathering-dominated conditions to enhanced material accumulation, organic complexation, and anthropogenic influences within the downstream region. These findings demonstrate how monsoonal precipitation regulates REE composition and sources by modifying catchment connectivity, surface runoff, and material transport, providing insights into seasonal mobility of trace elements in tropical agricultural basins. Full article

Conventional evaluations of rainfall prediction models rely on average accuracy, often masking the conditions, locations and causes of model failure and reduced reliability. This study proposes a paradigm shift from conventional average-accuracy benchmarking toward failure-aware forecast-error diagnosis in the Bengawan Solo River Basin, a tropical monsoon river basin in Indonesia with moderate topographic gradients (grid elevations span ≈ 300–650 m). Methodologically, forecasts from previously published models are treated as fixed inputs and their errors are modelled as the response variable, so the analysis diagnoses when and where models fail rather than retraining them. By treating forecast errors as response variables, rather than as random residuals, this study analyses 345,180 model–grid records–month records from ten individual models (RF, XGB, LGBM, SVR, MLP, LSTM, GRU, TCN, CNN, Transformer) and one best ensemble model (Ensemble_Q, a stacking of RF, XGB, SVR, MLP, LGBM, LSTM, GRU, TCN, CNN, Transformer) against observed CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, seasonal phase, ENSO and IOD regimes (El Niño–Southern Oscillation and Indian Ocean Dipole, respectively), the MJO index (Madden–Julian Oscillation) as an additional analysis, and elevation as a topographic control, using log-error models, high-error logistic regression, interaction tests, and block bootstrap validation (N = 1000), false discovery rate, and spatial statistics. Results indicate that prediction errors are not random but are systematically controlled: the Transition II phase increases log-error by 245% (pooled log-error model) and raises the odds of a high-error event roughly 40-fold relative to the dry season; La Niña conditions amplify errors by 41% and the odds of a high-error event by 3.3 times (though this ENSO signal is largely entangled with co-occurring Negative-IOD months), and every 100 m increase in elevation increases errors by 26%, with errors forming distinct spatial clusters (Moran’s I = 0.78; p = 0.001). Ensemble_Q outperforms the baseline on an aggregate basis (mean absolute error, MAE = 54.10 mm) but still experiences error amplification under these conditions, while spatial deep-learning architectures (TCN, CNN, Transformer) prove most vulnerable to elevation gradients. All major patterns persisted across variations in thresholds, model subsets, ENSO definitions, multiplicity corrections, and bootstrapping. These findings confirm that superior mean accuracy does not guarantee operational reliability, and that conditional failure diagnosis is an essential complement to benchmarking rainfall predictions in tropical monsoon regions. Full article

This study investigates the relationship between key circulation factors and ensemble forecast uncertainty during an atypical extreme heavy-rainfall event under weak synoptic forcing that affected the Guangzhou–Foshan megacity cluster in the Pearl River Delta (PRD) on 8 September 2022. Here, “atypical” refers to a localized extreme event occurring over the low-elevation urban river network without strong synoptic-scale drivers. Framed as an event-specific retrospective diagnostic analysis, the study used the China Land Multi-source Precipitation Analysis System version 2.1 (CMPAS-V2.1), ERA5 reanalysis, and 3-km (R3) and 9-km (R9) ensemble forecasts from the CMA Tropical Regional Atmospheric Model Ensemble Prediction System (CMA-TRAMS EPS). Spearman rank correlation and Monte Carlo field-significance tests were first applied to identify environmental variables closely associated with hourly precipitation variations, pinpointing the 700-hPa U-wind, 500-hPa V-wind, and 850-hPa V-wind as the most significant circulation predictors. Spatial anomaly fields of these key predictors were then subjected to hierarchical clustering, with clustering robustness evaluated using the cophenetic correlation coefficient (CCC) and bootstrap resampling. Because the clustering structure of the 925-hPa V-wind was comparatively weak, it was excluded from the final member-selection procedure. Finally, circulation-consistent ensemble members were selected using a multi-predictor consensus criterion (retaining 13 R3 members and 7 R9 members), and their 24 h accumulated precipitation was averaged to obtain a circulation-conditioned subset mean. The results show that persistent high temperatures, abundant moisture in the middle and lower troposphere, and a favorable multilayer circulation configuration provided suitable conditions for convective instability accumulation and localized heavy rainfall development. The precipitation forecasts exhibited substantial member-to-member variability. Under a consistent evaluation threshold, the 9-km configuration demonstrated superior overall ensemble-mean spatial skill compared to the 3-km configuration, indicating that increasing horizontal resolution does not necessarily improve forecast skill for weakly forced extreme rainfall. The resulting circulation-conditioned subset means successfully shifted the predicted heavy-rainfall center toward the observed Guangzhou–Foshan region and reduced the overestimated heavy-rainfall magnitudes over northern Guangzhou. Rather than serving as a purely objective score-maximizing post-processing algorithm, this approach extracts physically indicative spatial scenarios from ensemble spread. These findings provide a practical diagnostic framework for conditional forecast correction of localized heavy rainfall under weak synoptic forcing, though validation across multiple independent cases and consistent quantitative verification are necessary to assess its operational generalizability. Full article

The application of sub-seasonal prediction error analysis and interpretation remains a key research frontier of climate prediction. Related studies are essential for deepening the understanding of model performance, investigating error sources, and improving model prediction accuracy. Based on four perspectives—basic characteristics, spatial and temporal consistency, and potential causes of prediction errors—this study systematically investigates the sub-seasonal hindcast errors of summer precipitation over China from the state-of-the-art model system BCC–CPS–S2Sv2 (S2Sv2). The findings reveal that (1) S2Sv2 shows significant prediction skill in the first three pentads and shows an obvious increase in RMSE and a decrease in SCC of the precipitation prediction. This feature is quite similar to other operational models. (2) The prediction error primarily presents typical modes such as the meridional dipole, triple or consistent patterns. Prediction errors maintain spatial consistency in South China, the middle and lower reaches of the Yangtze River, and North China. Temporal consistency of the prediction errors shows a significant correlation between neighboring pentads, which diminishes as the prediction period extends, with a better correlation lasting for 2–4 pentads in some areas. (3) The prediction errors of S2Sv2 are significantly correlated with major circulation patterns in East Asia and key area sea surface temperatures (SSTs). For instance, errors are larger when the West Pacific Subtropical High is stronger, but smaller when the East Asia Trough is stronger. Errors also decrease with higher SSTs in the central equatorial Pacific and increase with higher SSTs in the tropical North Atlantic. This study provides valuable insights into the limitations of the S2Sv2 model and offers important references for error correction and model application. Full article

This study investigates the length–weight relationship, growth patterns, and population structure of Prochilodus nigricans in a tropical river environment, providing biometric information relevant to ecological studies, fisheries assessment, and conservation planning. A total of 139 individuals were sampled across rainy and dry seasons, and biometric variables (BD—Body Depth, TL—Total Length, SL—Standard Length, TW—Total Weight, and GW—Gonad Weight) were compared using descriptive statistics and regression analyses. Significant differences in biometric parameters were observed between seasons and sexes. Among the sampled individuals, body depth, standard length, total length, and total weight were significantly higher during the rainy season for both sexes; however, these differences should be interpreted with caution because different gill-net mesh sizes were used between hydrological periods. Gonad weight and gonadosomatic index (GSI) differed significantly between males and females in both seasons. A significant overall effect of season was detected for gonad weight, although pairwise seasonal differences were observed only in males, whereas no significant seasonal effect was detected for GSI. Length–weight relationships indicated isometric growth in both sexes. These results provide baseline biometric information useful for future ecological studies and complementary fisheries assessments of P. nigricans in the Tocantins–Araguaia Basin. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

The Paraopeba River basin has been exposed to long-term anthropogenic pressures, including urbanization, wastewater discharge, land-use changes, and mining. These impacts were intensified by the Brumadinho tailings dam collapse in 2019, which released approximately 1.6 million m³ of iron-mining tailings into the Paraopeba River. This study evaluated spatial and temporal dynamics of zooplankton communities and their relationships with environmental gradients. Eighteen sampling campaigns were conducted at nineteen monitoring sites, integrating physicochemical variables, nutrients, and metals with biological indicators. Sampling began approximately one year after the collapse and therefore does not capture the acute response to the initial tailings pulse. During the monitoring period, zooplankton assemblages showed no marked differences between upstream and downstream areas. Community structure was more strongly associated with hydrological seasonality and ongoing anthropogenic pressures, particularly nutrient enrichment and urban effluents. Turbidity, metals, and nutrients were important environmental correlates of richness, diversity, and density. Rotifers and copepods dominated, while the invasive Kellicottia bostoniensis persisted across sites and seasons. Tributaries supported higher densities and more exclusive species, potentially reflecting differences in nutrient availability, hydrodynamic conditions, and habitat retention. Overall, hydrological dynamics and contemporary anthropogenic stressors were more closely associated with community patterns than the upstream–downstream spatial gradient. Full article

This study presents a screening-level multitemporal assessment of surface water quality in the Atrato River (Chocó, Colombia), a tropical river system heavily influenced by artisanal and illegal gold mining, elevated sediment loads, and untreated domestic wastewater. Using records from 20 monitoring stations (2020–2025) operated by the regional environmental authority (CODECHOCÓ), six water quality indices (ICA, ICOMO, ICOMI, ICOSUS, ICOMINERÍA, ICOTRO) were analyzed through a framework combining Theil–Sen trend estimation, Kendall’s tau correlation, inter-period median comparison, and an index orientation normalization procedure. Results suggest spatially heterogeneous patterns: organic contamination improved in 11 of 17 evaluable stations, while mining contamination (ICOMINERÍA) showed positive directional slopes in 14 of 17 evaluable stations. Of these, four middle-reach stations reached statistical significance (p < 0.05; Kendall’s τ = 0.618–0.667). At the network level, the mean ICOMINERÍA value increased from 0.191 in 2020 to 0.502 in 2025 (+163%), representing a directional signal that should be interpreted as screening-level evidence requiring confirmation through denser temporal sampling. The proposed framework provides support for potential applicability for detecting environmental change in data-scarce monitoring networks and provide screening-level evidence relevant to the enforcement monitoring of environmental rights granted under Colombia’s landmark Sentencia T-622 (2016). Full article

This study investigates the influence of rainfall variability on runoff generation in the Upper Ing River Basin and inflow to the Mae Tum Reservoir in northern Thailand using the physically based Soil and Water Assessment Tool (SWAT) version 2012. In small mountainous watersheds, water supply reliability for irrigation and domestic use—particularly for unmonitored royal initiated projects like the Mae Tum Reservoir—has become a critical concern due to shifting climatic extremes. A SWAT model was developed using detailed spatial data on topography, land use, and soil characteristics together with long-term daily climate and streamflow records. The model performance at Station I.17 was evaluated through calibration and validation using the R², Nash–Sutcliffe Efficiency (NSE), and percent bias indices. Rainfall regimes were classified into dry, normal, and wet years based on the mean and standard deviation of 25-year gauge records to drive scenario simulations. The calibrated model reproduced seasonal runoff patterns satisfactorily (monthly NSE up to 0.685 and R² up to 0.712). The simulations demonstrated the strong sensitivity of both the runoff at Station I.17 and reservoir inflow to interannual rainfall differences, with the annual runoff ranging from 71.5 to 379.7 million m³ and the annual inflow to Mae Tum Reservoir ranging from 28.84 to 48.33 million m³. These findings demonstrate that physically based spatial modeling can effectively replace traditional empirical operating rules, providing a highly transferable framework for runoff forecasting, reservoir inflow assessment, and climate responsive water resources planning in data-scarce tropical mountainous basins. Full article

This study evaluates the potential and limitations of open/model-derived hydroclimatic data for multi-horizon river discharge forecasting in Babahoyo, Ecuador. A quantitative, applied, retrospective longitudinal design used daily data for 2017–2026. The discharge target is the GloFAS v4 seamless product—reanalysis until July 2022, archived operational forecast thereafter; meteorological predictors are ERA5/ERA5-Land/IFS reanalysis, not forecasts. The framework combined leakage-aware feature engineering, temporal validation, rolling-origin backtesting, naïve baselines, machine-learning regression, conformal prediction intervals, and high-flow classification. Performance was strongest at one day, where models reproduced the signal closely (R² = 0.909), although persistence remained highly competitive. Skill deteriorated at t + 7 and t + 14, where peak timing and magnitude became unreliable. Interval coverage was near-nominal at t + 1 but unreliable at longer horizons. The high-flow classifier identified most q90 cases, yet moderate precision and the absence of gauge validation prevent operational warning claims. Because the target is a 5 km grid simulation of a channel 100–150 m wide, the metrics quantify agreement with GloFAS, not with the physical river, and are reported to three significant digits. Overall, the study is a conservative benchmark for open hydroclimatic data in data-limited tropical floodplains: useful for exploratory monitoring and uncertainty diagnosis, but not a substitute for local hydrometric validation. Full article

Urban rivers face increasing degradation from wastewater, runoff, and land-use change, yet multi-season tropical estuarine datasets remain scarce. This study compared physicochemical water quality, metals, and salinity dynamics over one year in the Juan Díaz and Pacora rivers, two contrastingly urbanized Panama Bay watersheds. Juan Díaz showed higher nutrient concentrations, lower dissolved oxygen, and greater variability than Pacora. Dissolved oxygen in Juan Díaz fell below 5 mg/L in five of nine campaigns and ammonia nitrogen exceeded 3.7 mg/L in all three dry-season campaigns, versus two of seven campaigns below 5 mg/L in Pacora. Total dissolved solids exceeded 500 mg/L in four of nine Juan Díaz campaigns, all in the wet season, but stayed compliant in Pacora. Total nitrogen and total phosphorus were up to 10-fold and seven-fold higher, respectively, in Juan Díaz during the dry season. Cu exceeded its USEPA criterion in two of three detections; Cd was detected once, in Juan Díaz, below its saltwater criterion (0.0079 mg/L). Salinity confirmed stronger tidal influence in Juan Díaz (up to 24.7 g/kg) than Pacora (0.03–2.74 g/kg). PCA separated the two rivers along a nutrient–oxygen gradient explaining 53.6% of variance. Overall, Juan Díaz shows greater degradation, while Pacora remains less contaminated but requires continued monitoring amid rapid urbanization. Full article

Macroplastic pollution in rivers is an increasing environmental concern because rivers function simultaneously as active transport pathways and temporary storage compartments for land-based plastic waste. This study investigated the spatial distribution, composition, and size characteristics of riverbank and floating macroplastic debris in the Can Tho River, a tidal tributary of the Hau River in the Mekong Delta, Vietnam, to improve understanding of macroplastic transport, selective retention, and environmental partitioning between active transport and temporary storage compartments. Riverbank debris was surveyed at twelve sites spanning urban, peri-urban, and rural sections, while floating debris was quantified using a net-based sampling system. Riverbank accumulations exhibited pronounced local spatial heterogeneity, although litter density and mass density did not differ significantly among river sections. Plastics dominated both environmental compartments, accounting for 53–60% of accumulated debris and more than 95% of floating debris by abundance. Riverbank accumulations were dominated by plastic bags, food packaging, and beverage containers, whereas floating debris was dominated by expanded polystyrene foam products. Significant differences were also observed in material composition, plastic-product composition, and size distribution. Riverbank accumulations contained proportionally larger macroplastics (100–500 mm), whereas floating debris was dominated by smaller macroplastics (50–200 mm), supporting the role of size-dependent transport and selective retention in environmental partitioning. These findings show that floating debris and riverbank accumulations represent complementary components of the riverine plastic continuum, linking active transport and temporary storage through selective environmental partitioning. Integrating floating and riverbank monitoring provides a more comprehensive framework for understanding macroplastic transport and environmental fate while informing management strategies to reduce downstream plastic transport to the Hau River and ultimately estuarine and coastal ecosystems. Full article

Tropical mining landscapes require plot-based structural and carbon baselines to support restoration planning and environmental monitoring. This study estimated aboveground, root, and total tree carbon stocks across six mining concessions located in the Inambari River basin, Madre de Dios, southeastern Peruvian Amazon. Field inventories were conducted in 39 plots of 0.1 ha, where all trees with DBH ≥ 10 cm were measured. Aboveground biomass was estimated using a pantropical allometric equation based on wood density, diameter, and height; root biomass was estimated using a baseline root-to-shoot ratio of 0.24; and biomass was converted to carbon using a baseline carbon fraction of 0.47. Structural attributes, biomass stocks, carbon stocks, diameter-size profiles, basal-area contribution by diameter class, and multivariate structural-carbon patterns were evaluated among concessions. The concessions differed significantly in measured structural attributes, and these differences translated into contrasting derived biomass and tree carbon estimates. Edmilot I showed the highest basal area per hectare, total biomass, and total tree carbon, reaching 118.34 Mg C ha⁻¹, while Yesica recorded the lowest total tree carbon, with 58.54 Mg C ha⁻¹. Most trees were concentrated in the 10–20 cm and 20–30 cm DBH classes, but intermediate and larger trees contributed disproportionately to basal area. Principal component analysis summarized the concessions according to a reduced set of non-redundant structural and carbon variables, with the first two components explaining 99.21% of the total variation. These findings show that mining concessions retain contrasting forest conditions and should not be treated as homogeneous units. Structural and carbon baselines can help identify internal differences among concessions, prioritize restoration actions, and improve environmental monitoring in tropical mining landscapes. Full article

Riverine plastic waste is difficult to detect and map accurately because debris ranges from small items to large floating clusters, and tropical rivers present challenging conditions, such as murky water, floating vegetation, and variable lighting. This study develops and benchmarks a deep learning method for pixel-level, multi-scale mapping of riverine waste from five-band UAV multispectral imagery. We deployed a drone equipped with a five-band multispectral sensor over the Brantas River in Surabaya, East Java, Indonesia, and introduce the Multi-Scale Hierarchical Attention Ensemble (MHAE), which combines three backbone networks across three image resolutions through learned scale- and backbone-attention weighting. MHAE was evaluated against 12 CNN-, transformer-, state-space-, and traditional-machine-learning-based baselines (including Random Forest, U-Net, UNet++, and DeepLabV3+) on the accompanying BrantasRiverWaste-UAV dataset (882 image tiles from a single-site, single-season orthomosaic covering approximately 0.54 km² of river surface, labelled as water, land, organic waste, or inorganic waste), with pairwise comparisons assessed using Wilcoxon signed-rank tests with Bonferroni correction. MHAE achieved the highest pixel-level waste detection rate among the 12 evaluated models (86.76%), with a mean intersection-over-union of 78.33% (third-highest, behind UNet++ and U-Net). This work provides an initial, single-site benchmark and reference architecture for near-real-time riverine waste monitoring, and introduces the BrantasRiverWaste-UAV as, to our knowledge, one of the first five-band multispectral UAV datasets for tropical riverine waste mapping. Balanced sampling and multi-scale attention fusion can substantially improve waste-pixel detection under severe class imbalance; because the benchmark derives from a single site and season, future work should extend evaluation across additional seasons and river systems before the approach is generalised operationally. Full article

This study evaluated seasonal variations in mesozooplankton carbon biomass in Laguna de Terminos, a tropical coastal lagoon of the Southern Gulf of Mexico and a designated Ramsar site. We compared data from two contrasting 2022 sampling periods, the dry season (April) and the rainy season (October), coinciding with a strong La Niña event. Through systematic expeditions, we collected hydrographic and biological data to estimate zooplankton carbon biomass and assess its relationship with prevailing climatic conditions. Our results revealed distinct seasonal shifts. While surface water temperatures were higher in October (30 °C) than in April (28 °C), salinity and total dissolved solids exhibited the inverse trend. Chlorophyll-a peaked in April (>5 mg m⁻³) near river discharges and the lagoon’s connection to the Gulf of Mexico. Consistent with this, zooplankton carbon biomass was higher in April (up to 77.2 mg C m⁻³) than in October (up to 48.4 mg C m⁻³), with maximum concentrations observed in the eastern section of the lagoon. Given the scarcity of published reports for this system, these findings establish a critical baseline for future environmental monitoring. Furthermore, this data is essential for evaluating the lagoon’s primary and secondary productivity potential and for estimating the carbon pool held within lower-trophic-level organisms that support higher-level consumer groups. Full article