Search Results (29)

To explore the genetic diversity and evolutionary differentiation of Mastacembelus armatus from the headwaters of the Dongjiang and Ganjiang Rivers, we performed whole-genome resequencing on three populations, including Xunyushui (XW) and Jiuqu River (DN) from the Dongjiang River source as well as Taojiang River (XF) from the Ganjiang River source. We analyzed population structure, genetic differentiation, nucleotide diversity (π), pairwise FST, linkage disequilibrium, kinship, and neutrality tests (Tajima’s D, Fu and Li’s D). A total of 209.05 Gbp of clean data were obtained, with high quality and reliable alignment. Average nucleotide diversity (π) was higher in XW (0.00490 ± 0.00248) and DN (0.00478 ± 0.00312) and lower in XF (0.00463 ± 0.00158). Pairwise FST values revealed moderate differentiation between XW and DN (FST = 0.12) and strong divergence between XF and the other two populations (FST = 0.19 and 0.17). Neutrality tests showed no significant deviation from neutrality. XW and DN exhibited positive values, indicating stable demography, while XF showed negative values, suggesting a tendency of population expansion. Phylogenetic, admixture, and PCA analyses supported that all three populations belonged to one evolutionary clade with two ancestral components. XF showed the slowest linkage disequilibrium decay and distant kinship, indicating a small effective population size. Significant genetic divergence was primarily driven by geographic isolation and limited gene flow. This study reveals the genetic diversity and differentiation pattern of M. armatus and provides a genomic basis for its conservation and management. Full article

Under global climate change, frequent droughts threaten ecosystem functions, but how drought characteristics affect ecosystem resilience remains unclear. Focusing on the Dongjiang River Basin, China, we identified drought events at an 8-day scale from 2000–2024 using multi-source remote sensing and reanalysis data. The water use efficiency-based resilience index (R_de) was calculated, and a random forest model quantified the contributions of 21 potential driving factors. The model explained 68% of R_de variance (R² = 0.68, RMSE = 0.12). Downward shortwave radiation was the primary factor, followed by antecedent water use efficiency and soil moisture anomaly, with drought intensity and air temperature ranking fourth and fifth. All dominant factors exhibited nonlinear threshold effects: R_de decreased significantly after radiation exceeded ~110 W·m⁻²·(8d)⁻¹; R_de declined when standardized soil moisture anomaly fell below −2.0; and R_de increased sharply when drought intensity exceeded 12%. Drought intensity far outweighed duration and severity, establishing it as the key drought attribute. This study reveals the dominant drivers and their thresholds governing ecosystem resilience in the Dongjiang River Basin, providing quantifiable indicators for ecological drought early warning. Full article

Direct retrieval of Total Phosphorus (TP) from remote sensing is not possible because TP is not optically active. Unlike optically active parameters, TP does not exhibit spectral signals and relies on indirect correlations with Optically Active Constituents (OACs) such as Chl-a and suspended solids. Existing approaches often rely solely on spectral reflectance while neglecting the environmental variables, such as temperature, that can affect the correlations between OACs such as Chl-a and temperature. To address this, this study integrates satellite-derived Land Surface Temperature (LST) with Landsat 8/9 spectral features, utilizing LST as a spatial proxy for the aquatic thermodynamic environment. Focusing on the Dongjiang River, a subtropical river in China, a machine learning framework was constructed based on in situ measurements collected from 2020 to 2023. Feature selection using Pearson’s correlation and Random Forest importance identified the optimal combination of spectral bands and thermal inputs. The results from the model revealed the following: (1) annual mean TP concentrations in the delta were higher than in the main channel, with more pronounced seasonal fluctuations; (2) statistical verification (Wilcoxon signed-rank test, p < 0.01) confirmed that incorporating LST yielded a certain reduction in retrieval error compared to the spectral-only model; (3) the most influential predictors for TP estimation were a combination of the blue, green, and red spectral bands along with LST; (4) models incorporating LST achieved significantly higher accuracy than those based solely on spectral reflectance, with improved R² and RMSE values across most TP concentration ranges (except for 0.04–0.06 mg/L). These findings demonstrate that integrating LST with spectral features enhances the accuracy of remote sensing-based TP retrieval in rivers, offering new opportunities for improved large-scale water quality monitoring. Full article

Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates SVM, LSSVM, LSTM, and BiLSTM models to leverage their complementary strengths in capturing nonlinear and non-stationary hydrological dynamics. SAEF employs a seasonal segmentation mechanism to divide annual runoff data into four seasons (spring, summer, autumn, winter), enhancing model responsiveness to seasonal hydrological drivers. An Improved Arctic Puffin Optimization (IAPO) algorithm optimizes the model weights, improving prediction accuracy. Beyond numerical gains, the framework also reflects seasonal runoff generation processes—such as rapid rainfall–runoff in wet seasons and baseflow contributions in dry periods—providing a physically interpretable perspective on runoff dynamics. The effectiveness of SAEF was validated through case studies in the Dongjiang Hydrological Station (China), the Elbe River (Germany), and the Quinebaug River basin (USA), using four performance metrics (MAE, RMSE, NSEC, KGE). Results indicate that SAEF achieves average Nash–Sutcliffe Efficiency Coefficient (NSEC) and Kling–Gupta efficiency (KGE) coefficients of over 0.92, and 0.90, respectively, significantly outperforming individual models (SVM, LSSVM, LSTM, BiLSTM) with RMSE reductions of up to 58.54%, 55.62%, 51.99%, and 48.14%. Overall, SAEF not only strengthens predictive accuracy across diverse climates but also advances hydrological understanding by linking data-driven ensembles with seasonal process mechanisms, thereby contributing a robust and interpretable tool for runoff forecasting. Full article

Based on 0.1° × 0.1° soil moisture reanalysis data from 1950 to 2024, combined with remote sensing ecological products such as Enhanced Vegetation Index (EVI) and gross primary productivity (GPP), this study systematically investigates the spatiotemporal evolution, transition process, and ecological responses of flash droughts and slowly evolving droughts (including seasonal and cross-seasonal droughts) in the Dongjiang River Basin of China. The results indicate the following: (1) The average occurrence frequencies of flash droughts, seasonal droughts, and cross-seasonal droughts within the basin were 4.1%, 7.8%, and 8.4%, respectively. (2) The vast majority of flash droughts (approximately 90.1%) further developed into longer-lasting, slowly evolving droughts, indicating that flash droughts serve as a critical precursor to persistent drought events. Moreover, winter was identified as the key season for the occurrence of flash droughts and their transition to slowly evolving droughts. (3) In terms of ecological response, droughts significantly suppressed vegetation growth, but ecosystem resilience exhibited notable differences: although flash droughts caused relatively mild initial suppression, they were accompanied by a severe lack of ecosystem resilience; in contrast, cross-seasonal droughts, despite inducing stronger suppression, were met with higher ecosystem resilience. This study underscores the importance of the early monitoring and warning of flash droughts, and the findings provide a scientific basis for drought risk management in humid basins. Full article

This study quantified the Habitat Connectivity Index (DCI) of cascade dams in the mainstream of the Dongjiang River, revealing the non-linear relationship between dam passability (p) and connectivity restoration. Results showed that DCI increased slowly when p < 0.6 (with the magnitude of increase not exceeding 10.41), whereas an exponential response emerged when p > 0.8 (specifically, DCI rose by 16.53 as p increased from 0.8 to 0.9). Time-series analysis indicated that the number of dams increased from 3 to 16 between 1970 and 2020, which plunged the natural-state DCI (set at 100) to 9.01 (representing a 90.99% decrease); notably, 78.14% of the total connectivity loss occurred during the 2000–2010 period. Spatial heterogeneity analysis demonstrated that enhancing the passability of Jiantan Dam increased DCI by 4.68 (under the baseline condition of p = 0.8), whereas the same intervention on Sulei Dam only led to a 0.58 increase in DCI. This finding highlights the importance of key nodes for connectivity restoration and provides a scientific basis for prioritizing the enhancement of connectivity at such nodes in subsequent ecological governance. A 2024 fish community survey found that 84.2% of the recorded species were native (64 out of 76), while only 18.8% of the total individuals (617 individuals) were migratory; the dominant species were identified as generalist residents, including Oreochromis zillii, Cirrhinus molitorella, and Hemiculter leucisculus. This study identifies 0.8 as a critical threshold for connectivity restoration and provides a spatial decision-making framework for prioritizing the restoration of key dams. Full article

Global forest wildfires are increasing in both frequency and intensity, resulting in significant ecological degradation and posing substantial threats to human health. This study focused on the Dongjiang River Basin in southern China and investigated the seasonal and spatial distribution patterns of forest wildfires in the research region from 2003 to 2023 using geographic information system technology. This study employed the random forest (RF) model, a machine learning algorithm, to predict the danger level of wildfire across different seasons and quantitatively interpret the seasonal wildfire driving mechanisms using the SHapley Additive exPlanations (SHAP) values. The results indicated that forest wildfires in the Dongjiang Basin were predominantly concentrated in the eastern region of the Dongjiang Basin, with significant seasonal variation in the spatial distribution. The frequency of fire events exhibited distinct seasonal patterns, with higher incidence in spring and winter and relatively lower frequency in summer and autumn. The random forest model demonstrated high predictive accuracy for the wildfire danger in all the seasons. Furthermore, the analysis of the driving factors showed that, despite some seasonal variability, the underlying mechanisms of wildfire occurrence could be effectively quantified using the SHAP values. Notably, the Normalized Difference Vegetation Index and anthropogenic disturbances consistently emerged as the dominant driving forces behind forest wildfires across all the seasons. Full article

Reservoir construction has profoundly altered natural runoff evolution in river basins. Dynamic conflicts among multi-objective operational strategies—such as flood control, water supply, and ecological compensation—across varying temporal scales exacerbate uncertainties in runoff prediction, primarily due to the complex interplay between hydrological rhythm variations and anthropogenic regulation. To address these challenges, this study proposes a hierarchical multi-scale coupling framework. Long short-term memory (LSTM) networks are employed to extract implicit operational patterns from long-term reservoir records at monthly and weekly scales, while short-term decision dynamics are captured through deviations from these established long-term rules. The proposed framework is validated in the Dongjiang River Basin, a key water source for the Guangdong–Hong Kong–Macao Greater Bay Area. Compared to single-scale models, the hierarchical approach improves prediction accuracy with an average Nash–Sutcliffe Efficiency (NSE) increase of 9.4% and reductions in the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of 13.2% and 9.6%, respectively. When coupled with a hydrological model, the framework enhances simulation accuracy in reservoir-regulated basins by up to 37.8%. By integrating multi-source decision variables, the framework captures the feedback mechanisms between natural flow variability and human interventions across temporal scales, providing a transferable strategy to reconcile operational conflicts with ecological flow requirements. Its flexibility supports optimized water allocation in regulated river basins, contributing to enhanced water security for downstream urban agglomerations. Full article

Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks to aquatic ecosystems and human health. Accurate real-time TN prediction is crucial for effective water quality management. This study presents an innovative approach that combines the distance correlation coefficient (DCC) for feature selection with a coupled Attention-Convolutional Neural Network-Bidirectional Long Short-Term Memory (At-CBiLSTM) model to predict TN concentrations in the Dongjiang River in China. A dataset of 28,922 time-series data points was collected from seven sampling sites along the Dongjiang River, spanning from November 2020 to February 2023. The DCC method identified conductivity, Permanganate Index (COD_Mn), and total phosphorus as the most significant predictors for TN levels. The At-CBiLSTM model, optimized with a time step of three, outperformed other models, including standalone Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Convolutional Neural Network LSTM (CNN-LSTM), and Attention-LSTM variants, achieving excellent performance with the following metrics: mean absolute error (MAE) = 0.032, mean squared error (MSE) = 0.005, mean absolute percentage error (MAPE) = 0.218, and root mean squared error (RMSE) = 0.045. Importantly, increasing the number of input features beyond three variables led to a decline in model accuracy, underscoring the importance of DCC-driven feature selection. The results highlight that combining DCC with deep learning models, particularly At-CBiLSTM, effectively captures nonlinear temporal dependencies and improves prediction accuracy. This approach provides a solid foundation for real-time water quality monitoring and can inform targeted pollution control strategies in river ecosystems. Full article

Recently, 7-diethylamino-4-methylcoumarin (DEAMC) has been identified as a potent antiandrogenic compound in the surface water; however, little is known about the antiandrogenic potentials of other synthetic coumarins and their occurrence in the aquatic ecosystem. In this study, for the first time, we observed that 7-dimethylamino-4-methylcoumarin (DAMC) elicited androgen receptor (AR) antagonistic activity with a 50% inhibitory concentration (IC₅₀) of 1.46 µM, which is 14.3 times more potent than that observed for DEAMC (IC₅₀ = 20.92 µM). We further collected abiotic (water and sediment) and biotic (plant, plankton, and fish) samples (n = 208) from a subtropical freshwater ecosystem, the Dongjiang River basin, in southern China, and determined the concentrations of the two coumarins in these samples. Overall, DAMC was the predominant compound found in the sediment, plant, algae, zooplankton, and fish muscle samples, with median concentrations at 0.189, 0.421, 0.832, 0.798, and 0.335 ng/g dry wt. (DW), respectively, although it was not detected in any surface water sample. For DEAMC, the median concentrations observed in the surface water, sediment, plant, algae, zooplankton, and fish muscle samples were 0.105 ng/L, 0.012, 0.051, 0.009, 0.008, and 0.181 ng/g DW, respectively. The bioaccumulation factor (BAF) values of DAMC and DEAMC in the algae, zooplankton, and fish muscle exceeded 5000 L/kg, suggesting that the two coumarins may have significant bioaccumulation potentials in aquatic biota. Additionally, the mean daily intake (EDI) of coumarins through fish consumption was estimated as 0.19 ng/kg BW/day for male toddlers. This is the first field study to illustrate the antiandrogenic potential of DAMC and document the widespread occurrence of the two synthetic coumarins in aquatic ecosystems. Full article

To improve the water resources carrying capacity of the Xiangjiang River Basin and achieve sustainable development, this article evaluates and predicts the Xiangjiang River Basin’s water resources carrying capacity level based on county-level units. This article takes 44 county-level units in the Xiangjiang River Basin as the evaluation target, selects TOPSIS and the entropy weight method to determine weights, calculates the water resources carrying capacity level of the evaluation sample, uses a BP neural network model to calculate the predicted water resources carrying capacity level for the next 5 years, and adds the GIS method for spatiotemporal analysis.(1) The water resources carrying capacity of the Xiangjiang River Basin has remained relatively stable for a long period, with overloaded areas being the majority. (2) There are relatively significant spatial differences in the carrying capacity of water resources: Zixing City, located upstream of the tributary, is far ahead due to its possession of the Dongjiang Reservoir; the water resources carrying capacity in the middle and lower reaches (northern region) is generally higher than that in the upper reaches (southern region). (3) According to the BP neural network model prediction, the water resources carrying capacity of the Xiangjiang River Basin will maintain a stable development trend in 2022, while areas such as Changsha and Zixing City will be in a critical state, and other counties and cities will be in an overloaded state.This study has important references value for the evaluation and early warning work of the Xiangjiang River Basin and related research, providing a scientific and systematic evaluation method and providing strong support for water resource management and planning in Hunan Province and other regions. Full article

Land use/cover change (LUCC) is a vital aspect representing global change and humans’ impact on Earth’s surface. This study utilized the ESRI Land Cover 2020 and China Land Cover Dataset (CLCD), along with historical imagery from Google Earth, to develop a method for the assessment of land use data quality. Based on the assessment, the CLCD was updated to generate an improved Re-CLCD for the Pan-Pearl River Basin (PPRB) from 1985 to 2020, and to analyze LUCC in the PPRB over the past 35 years. The results indicate the following: (1) Among the seven land uses, built-up land experienced the most dramatic change, followed by cropland, forestland, grassland, shrubland, waterbody, and bare land, with notable increases in built-up land and forestland, and rapid decreases in cropland, grassland, and shrubland. (2) The magnitude of land use changed very widely, with the highest change in the Pearl River Delta, followed by small coastal river basins in southern Guangdong and western Guangxi, the Dongjiang River Basin, the Hanjiang River Basin, the Xijiang River Basin, the Beijiang River Basin, and lastly, Hainan Island. (3) The largest increase happened in built-up land, with a total increase of 12,184 km², mainly due to the occupation of cropland and forestland, corresponding to the highest decrease in cropland, with a net loss of 10,435 km², which was primarily converted to forestland and built-up land. The study results are valuable in providing a scientific basis for policy overhaul regarding land resources and management to safeguard ecological balance and promote sustainable development in the Pan-Pearl River Basin. Full article

Intercropping radiation interception model is a promising tool for quantifying solar energy utilization in the intercropping system. However, few models have been proposed that can simulate intercropping radiation interception accurately and with simplicity. This study proposed a new statistical model (DRT model), which enables the simulation of daily radiation distribution by considering the geometric light transmission in the intercropping system. To evaluate model performance, the radiation interception and distribution in two wheat/maize strip intercropping experiments (A and B) were simulated with the DRT model and other two statistical models, including the horizontal homogeneous canopy model (HHC model) and the Gou Fang model (GF model). Experiment A was conducted in different intercropping configurations, while Experiment B was conducted in soils with different salinity levels. In both experiments, the HHC model exhibited the poorest performance (0.120 < RMSE < 0.172), while the DRT model obtained a higher simulation accuracy in the fraction of photosynthetically active radiation (PAR) interception, with RMSE lower by 0.008–0.022 and 0.022–0.125 than the GF and the HHC models, respectively. Especially, the DRT model showed stronger stability than the other two models under soil salinity stress, with R² higher by 0.129–0.354 and RMSE lower by 0.011–0.094. Moreover, the DRT model demonstrated a relatively ideal simulation of the daily radiation distribution in Experiment A (0.840 < R² < 0.893, 0.105 < RMSE < 0.140) and Experiment B (0.683 < R² < 0.772, 0.111 < RMSE < 0.143), especially when the continuous canopy formed during the later crop growth stages. These results indicate the superiority of the DRT model and could improve our understanding of radiation utilization in the intercropping system. Full article

Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7, TMPA-3B42RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijiang and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE’, and NSCE values) than the uncorrected ones, particularly in the Beijiang River, which has a larger drainage area; (2) after re-calibration under Scenario II, the two daily merged precipitation products (NSCE values: 0.73–0.87 and 0.69–0.82 over the Beijiang and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data. Full article

Precipitation is a key component of the hydrological cycle and one of the important indicators of climate change. Due to climate change, extreme precipitation events have globally and regionally increased in frequency and intensity, leading to a higher probability of natural disasters. This study, using the long-term APHRODITE dataset, employed six precipitation indices to analyze the spatiotemporal changes in extreme precipitation in the Pearl River Basin during 1951–2015. The Mann–Kendall (M–K) test was used to verify the significance of the observed trends. The results indicate that: (1) the interannual PRCPTOT showed a trend with an average positive increase of 0.019 mm/yr, which was followed by an increase in SDII, R95P, and RX1day, and a decrease in R95D and CWD; seasonal PRCPTOT also displayed an increase in summer and winter and a decrease in spring and autumn, corresponding to increases in R95P and SDII in all seasons. (2) The annual precipitation increases from the west to east of the basin, similar to the gradient distribution of SDII, R95P and RX1day, with the high R95D happening in the middle and lower reaches of the Xijiang River, but the CWD increased from the north to south of the basin. The seasonal spatial distributions of PRCPTOT, SDII, and R95P are relatively similar except in autumn, showing an increase from the west to east of the basin in spring and winter and a gradual increase from the north to south of the basin in summer, indicating that the Beijiang and Dongjiang tributary basins are more vulnerable to floods. (3) The MK test results exhibited that the Yunnan–Guizhou Plateau region in the upper reaches of the Xijiang River Basin became drier, and there was an increase in extreme precipitation in the Beijiang and Dongjiang river basins. The study results facilitate valuable flood mitigation, natural hazard control and water resources management in the Pearl River Basin. Full article