Search Results (25)

The Beijiang River Basin is an important ecological security protection area and water source supply area in Guangdong Province. This study assesses the spatiotemporal distribution characteristics of watershed water quality based on on-site monitoring data and multivariate statistical analysis. The results indicate that PO₄³⁻P concentrations peak during the flood season, whereas pH, NO₃⁻-N, and total nitrogen (TN) reach their highest levels during the autumn normal-flow period. Spatially, water quality follows a gradient of upstream > downstream > midstream, with the midstream region identified as the primary zone of water quality degradation. Future non-point source (NPS) pollution characteristics in the Beijiang River Basin are influenced by land use/cover change (LUCC) and climate change, showing significant variation across Shared Socioeconomic Pathway (SSP) scenarios. Under SSP126, precipitation increases at the slowest rate, with a peak annual value of 1599.77 mm during 2031–2040 and an average basin temperature of 19.61 °C. In contrast, SSP245 exhibits a marked increase in precipitation, reaching 1802.92 mm by 2061–2070. Under SSP585, annual precipitation rises to 2200.04 mm, with temperatures approximately 0.5 °C higher than those under SSP126. Simulations based on the improved ESP-PLUS model indicate that, under the natural development scenario (NDS), expansion of construction land increases urban runoff pollution by 32.97%. Under the economic development scenario (EDS), 1023 km² of ecological land is lost, significantly weakening pollution interception capacity, while construction land increases by 26.01%. In contrast, the coordinated development scenario (CDS) reduces ecological land loss by more than 60% compared to EDS through balanced development and conservation, thereby maintaining the basin’s pollutant purification function. Overall, future nitrogen and phosphorus loads in the watershed are projected to first decrease and then increase. Accordingly, differentiated management strategies are recommended, emphasizing the coordinated development of economic growth and ecological protection, and providing a scientific basis for controlling NPS pollution under changing climatic conditions. Full article

This study investigated heavy metal bioaccumulation, pollution sources, and human health risks in the Wujiang and Zhenjiang rivers, the major headwaters of the Beijiang River in southern China. Concentrations of 11 heavy metals (Fe, Mn, Ba, Zn, As, Cu, Ni, Cr, Co, Cd, Pb) in surface water, sediments, and the soft tissues of four bivalve species (Corbicula fluminea, Limnoperna fortunei, Unio douglasiae, and Anodonta woodiana) were determined. Results showed that surface water remained below pollution thresholds, whereas sediments in the Wujiang River exhibited moderate-to-heavy Cd, As, Pb, and Zn contamination linked to historical Pb-Zn mining. Among the two better-represented species, C. fluminea showed relatively high As and Zn concentrations, whereas L. fortunei showed relatively high Cu accumulation. Data for U. douglasiae and A. woodiana were treated as preliminary observations because of their limited sample sizes. Biota-sediment accumulation factor (BSAF) values indicated that Mn, Cu, Cd, Zn, Ni, and Cr were actively accumulated beyond sediment concentrations, whereas As and Pb showed limited sediment-to-biota transfer. Multivariate analysis of the two better-represented species indicated species-related differences in tissue metal profiles, with patterns consistent with both natural geogenic inputs and historical Pb-Zn mining activities. All four species posed potential non-carcinogenic health risks to consumers, with As and Mn as the dominant risk contributors. These findings underscore the necessity of multi-species biomonitoring and consumption advisories in mining-impacted watersheds. Full article

This study explores flood forecasting in the Pa River basin, a major tributary of the Beijiang River in South China, by integrating the Xin’anjiang hydrological model with the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm for parameter calibration. Fifteen observed flood events from April to August 2024 were employed in this study, with twelve events used for model calibration and the remaining three for validation. Additionally, to assess model performance under extreme conditions, a 50-year return period flood event from June 2020 was incorporated as a supplementary validation case. The calibrated model reproduced flood hydrographs with high accuracy, achieving Nash–Sutcliffe Efficiency (NSE) values of up to 0.98, relative peak discharge errors generally within ±10%, and peak timing deviations under 3 h. The validation results demonstrated consistent performance across both typical and extreme events, indicating that the proposed framework provides a feasible and physically interpretable approach for flood forecasting in data-limited catchments. Full article

This study delves into the once-in-a-century extreme precipitation events in the northern region of the Pearl River Basin during the 2022 Dragon Boat Festival period. Through a comprehensive analysis spanning various temporal scales, from synoptic-scale systems to subseasonal oscillations, including the rare triple-peaked La Niña phenomenon, we illuminate the intricate interactions among these factors and their impact on extreme precipitation events. Specifically, we present a conceptual model of multiscale interaction systems contributing to extreme precipitation in the BeiJiang Basin. Our findings reveal that, during the 2022 Dragon Boat Festival period, precipitation in the BeiJiang Basin exhibited characteristics across multiple time scales, with the synoptic-scale environment proving highly conducive. Systems such as the South Asian High, Western Pacific Subtropical High, and South China Sea summer monsoon were identified as the direct influencing factors of precipitation. Importantly, our study highlight the pivotal role of subseasonal oscillation propagation stagnation in extreme precipitation in the BeiJiang Basin, with synoptic-scale systems playing a contributing role. We emphasize the indirect influence of ENSO signals, regulating not only monsoons but also the propagation of subseasonal oscillations. The interplay of these factors across different temporal scales significantly impacts flood hazards. Overall, our study significantly enhances the understanding of mechanisms driving extreme precipitation events in the Pearl River Basin, with profound implications for water resource management and disaster prevention. Full article

This study takes the Beijiang River Basin in Guangdong Province as an example, examining the changes in land usage throughout time and space between 1980 and 2020. Using multidimensional ecosystem service functions and the loop theory, this study constructs ecological constraints (ES) for the Beijiang River Basin. Based on these ecological constraints, an ES-PLUS model is developed to simulate future land cover changes under multiple scenarios in the Beijiang River Basin by 2050. The results indicate the following: (1) Currently, the major land use types in the Beijiang River Basin are forest, cropland, and grassland, accounting for over 95% of the area. Significant changes in land use were observed between 1980 and 2020, including the severe degradation of forests and grasslands, a notable expansion of construction land, intense human–land conflicts, and the highest single land use dynamic degree for unused land at 5.67%, with a comprehensive land use dynamic degree of 0.18%. (2) In the four development scenarios of the Beijiang River Basin in 2050, construction land increased by 32.97%, 74.75%, 26.01%, and 45.50%, respectively, suggesting that ecological constraints as flexible constraint spaces can effectively control the disorderly expansion of construction land. Therefore, formulating ecological protection policies, optimizing the land use structure in the Beijiang River Basin, and constructing ecological sources and corridors in line with the distribution of urban areas, roads, and railroads in the basin may offer direction for the best use of land resources, the preservation of the environment, and sustainable growth in the Beijiang River Basin. Full article

Human activities, such as reservoir construction and riverbed sand extraction, significantly influence the hydrological and sedimentary dynamics of natural rivers, thereby directly or indirectly affecting river landscape pattern distribution. This study primarily focused on the lower Beijiang River (LBR) in China, an area characterized by intensive human activity. River landscape patterns were studied using historical topographical data and time-series Landsat remote sensing images. Natural and anthropogenic factors were considered to explore the driving forces behind the evolution of landscape patterns. The results indicated that the topography of the LBR underwent significant downcutting from 1998 to 2020. The average elevation of the study area decreased by 3.6 m, and the minimum thalweg elevation decreased by 6.7 m. Over the past 30 years, the local vegetation showed a relatively stable spatial distribution, whereas the area of sand remained relatively stable before 2012, followed by a sudden decline, and tended to stabilize in the last decade. The water area exhibited a gradually increasing trend. The transition maps indicated that the spatial changes in sand were the most significant, with only 39.6% of the sand remaining unchanged from 1998 to 2009 and 32.3% from 2009 to 2020. The corresponding landscape patterns showed that the fragmentation degree of sand increased, with the mean patch size decreasing by 69.2%. The aggregation of water intensified, as its aggregation index increased from 93.31% to 95.41%, while the aggregation of vegetation remained relatively minor, ranging from 89.52% to 90.12%. The annual average temperature, annual average maximum temperature, and annual rainfall days had the strongest correlations with the vegetation landscape pattern indices. Additionally, human activities may have been the primary driver of the landscape pattern evolution of water and sand. The findings of this study have positive implications for the maintenance of the diversity and stability of river ecosystems. 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

In this study, the contents of eight heavy metal(loid)s (As, Pb, Zn, Cd, Cr, Cu, Sb and Tl) in 50 sediment samples from a headwater of Beijiang River were studied to understand their pollution, ecological risk and potential sources. Evaluation indexes including sediment quality guidelines (SDGs), enrichment factor (EF), geo-accumulation index (I_geo), risk assessment code (RAC) and bioavailable metal index (BMI) were used to evaluate the heavy metal(loid)s pollution and ecological risk in the sediments. Pearson’s correlation analysis and principal component analysis were used to identify the sources of heavy metal(loid)s. The results showed that the average concentration of heavy metal(loid)s obviously exceeded the background values, except Cr. Metal(loid)s speciation analysis indicated that Cd, Pb, Cu and Zn were dominated by non-residual fractions, which presented higher bioavailability. The S content in sediments could significantly influence the geochemical fractions of heavy metal(loid)s. As was expected, it had the most adverse biological effect to local aquatic organism, followed by Pb. The EF results demonstrated that As was the most enriched, while Cr showed no enrichment in the sediments. The assessment of I_geo suggested that Cd and As were the most serious threats to the river system, while Cr showed almost no contamination in the sediments. Heavy metal(loid)s in sediments in the mining- and smelting-affected area showed higher bioavailability. According to the results of the above research, the mining activities caused heavier heavy metal(loid)s pollution in the river sediment. Three potential sources of heavy metal(loid)s in sediment were distinguished based on the Pearson’s correlation analysis and PCA, of which Cd, Pb, As, Zn, Sb and Cu were mainly derived from mining activities, Cr was mainly derived from natural sources, Tl was mainly derived from smelting activities. 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

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin. 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

Extreme precipitation events have caused serious impacts on natural ecosystem and human society and have attracted increasing attention in recent years. IPCC AR6 WG I report highlighted a lack of conclusive consensus on the change trend of extreme precipitation in some basins and variation (increase or decrease) between regions. Based on seven precipitation indexes defined by ETCCDI, using daily precipitation data observed by 18 national reference meteorological stations in China during 1959–2018, this study analysed spatiotemporal variation trend of extreme precipitation in the Beijiang River Basin, Southern Coastal China, in recent 60 years, using Mann–Kendall (M-K) trend test, coefficient of variation, and continuous wavelet transformation. M-K test results showed that there were mutations in all seven precipitation indexes, and mutation points were mainly concentrated in two periods (1986–1991 and 2005–2010). The change range of each index after mutation was generally greater than that before mutation. Continuous wavelet transformation showed that each indicator had a significant oscillation period of 2–4 year in most time domains. The southeastern part of the basin (Fogang and Qingyuan) was the center of extremely heavy precipitation, and most precipitation indexes decreased from this area to the surrounding area. As far as the basin as a whole was concerned, consecutive wet days (CWD) declined significantly (passing 0.05 of confidence test), and there was a significantly positive correlation between annual distribution of R95ds and monthly precipitation (p < 0.001). The research results expand our understanding of regional water cycle and extreme climate change, guide the allocation and management of water resources related to regional industrial and agricultural activities, and provide reference for disaster prevention and mitigation. Full article

Pb and Cd accumulation in riparian soils and river sediments in river basins is a challenging pollution issue due to the persistence and bioaccumulation of these two trace metals. Understanding the migration characteristics and input sources of these metals is the key to preventing metal pollution. This study was conducted to explore the contents, geochemical fractionation, and input sources of Pb and Cd in riparian soils and river sediments from three lower reaches of the Pearl River Delta located in the Guangdong–Hong Kong–Macao Greater Bay Area. The total concentration of all Pb and Cd values exceeded the background values to varying degrees, and the exchangeable fraction of Cd in riparian soils and river sediments accounted for the largest proportion, while that of Pb was dominated by the residual fraction. Geoaccumulation index calculations showed that in the riparian soils, the average accumulation degree of Pb (0.52) in the Beijiang River (BJR) was the highest, while that of Cd (2.04) in the Xijiang River (XJR) was the highest. Unlike that in riparian soils, the maximum accumulation of Pb (0.76) and Cd (3.01) in river sediments both occurred in the BJR. Furthermore, the enrichment factor results also showed that Pb and Cd in the riparian soils and river sediments along the BJR were higher than those in the XJR and Dongjiang River (DJR). The relationship between enrichment factors and nonresidual fractions further proved that the enrichment factors of Cd were significantly correlated with the nonresidual fractions of Cd, which may imply various anthropogenic sources of Cd in the three reaches. Moreover, source identification based on principal component analysis (PCA) and Pb isotope ratio analysis indicated that riparian soils and river sediments have inconsistent pollution source structures. The PCA results showed that Pb and Cd were homologous inputs in the DJR, and there were significant differences only in the riparian soils and river sediments. Pb isotope tracing results further showed that the bedrock of high geological background from upstream may be the main reason for Cd accumulation in the XJR. However, the ultrahigh accumulation of Cd in the BJR is mainly caused by the input of the upstream mining and metallurgy industry. The control of upstream input sources will be the key to the prevention of trace metal pollution in these regions. Full article

Despite the marked influence of relative humidity (RH) on ecosystems and human society, the spatiotemporal pattern of RH is far from clearly understood. This study analyzed the spatiotemporal variation in RH in Guangdong Province, South China, in the period of 1959–2017. The RH data were collected from 74 national standard meteorological stations. The spatiotemporal variation in RH was evaluated using rotate empirical orthogonal function (REOF) zoning, Mann–Kendall test, and wavelet transform methods. Based on the REOF decomposition situation of monthly RH field, Guangdong was divided into six subareas. The annual mean of RH in the whole province was 78.90%. In terms of spatial variation, overall annual mean RH decreased from southwest to northeast in the province. Temporally, annual mean RH showed a declining trend in the last six decades. Particularly, the RH in the Pearl River Delta area declined at the rate of 1.349%/10a. Mann–Kendall tests showed that mutation points of annual mean RH mostly appeared in the 1990s, especially in the early 1990s. Continuous wavelet transforms of annual mean RH displayed that inland subareas have similar cycle characteristics, and the east coast and Pearl River Delta have no significant period in most time domains. The results provide new understanding of RH variation in the last six decades in South China, which is valuable for detecting climate change, monitoring hazardous weather, and predicting future environmental change. Full article

Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales. Full article