Search Results (22)

In-depth analysis of the evolution of ecosystem services (ESs) in the basin at different spatial scales, scientific identification of ecosystem service clusters, and revelation of their spatial and temporal characteristics as well as coupling mechanisms of interactions are the key prerequisites for effective implementation of ES management. This paper assessed the spatial and temporal changes of six key ESs covering food provisioning (FP), water yield (WY), soil retention (SR), water conservation (WC), habitat quality (HQ), and carbon sequestration (CS) in the Xijiang River Basin (XRB), China, between 2000 and 2020. Given that the scale effects of ESs and their spatial heterogeneity in the XRB are still subject to large uncertainties, a combination of Spearman correlation analysis and geographically weighted regression (GWR) modelling systematically revealed the trade-offs and synergistic relationships between ESs and the scale effects from a grid, watershed, and county perspective. Additionally, we applied the self-organizing mapping (SOM) method to identify multiple ecosystem service bundles (ESBs) and propose corresponding sustainable spatial planning and management strategies for each cluster. The results reveal the following key findings: (1) Spatial distribution and heterogeneity: The six ESs demonstrated pronounced spatial variability across the study area during the two-decade period from 2000 to 2020. The downstream areas had higher levels of ESs, while the upstream regions showed comparatively lower levels. This trend was particularly evident in areas with extensive arable land, higher population density, and more developed economic activity, where ESs levels were lower. (2) Trade-offs/synergies: The analysis highlighted the prevalence of synergistic effects among ESs, with food provisioning-related services exhibiting notable trade-offs. Trade-off/Synergistic effects were weaker at the grid scale but more pronounced at the sub-basin and county scales, with significant spatial heterogeneity. (3) Identification of ESBs: We identified five distinct ESBs: the HQ-CS synergy bundle (HCSB), the integrated ecological bundle (IEB), the agricultural bundle (AB), the key synergetic bundle lacking HQ (KSB), and the supply service bundle (SSB). These clusters suggest that the overall ecological environment of the study area has significantly improved, the supply functions have strengthened, and ecosystem vulnerability has been effectively mitigated. Building upon the identified multi-scale spatiotemporal heterogeneity patterns of ESBs in the XRB, this study proposes an integrated framework for territorial spatial planning and adaptive land management, aiming to optimize regional ecosystem service provisioning and enhance socio-ecological sustainability. Full article

In recent decades, dams worldwide are increasingly constructed in a row along a single river or basin, thus forming reservoir cascades, and in turn producing cumulative ecological effects along these areas. The use of multimetric indices (MMI) based on fish assemblages to assess the ecological health status of rivers and lakes has also been extensively developed. However, to date, there are no studies that employ MMI for the identification of the cumulative effects of reservoir cascades. The aim of this study was to develop a new Fish-based Index of Biotic Integrity (F-IBI) that can effectively identify the cumulative effects of reservoir cascades on fish assemblages in two important habitats (the free-flowing reach between reservoirs and the transition zone in the reservoir). Fish assemblages from 12 sites were sampled along the cascade reservoirs in the Xijiang River, China. First, through screening for redundancy, precision, and responsiveness of the candidate metrics, a new F-IBI based on ecological trait information of fish species composition was developed to estimate the ecological status of all sites. F-IBI scores exhibited an obviously downward trend from upstream to downstream in a reservoir cascade, and the transition zones in the reservoir displayed significantly lower F-IBI scores than the free-flowing reaches between reservoirs. Secondly, using Random Forest models, it was shown that the F-IBI can effectively identify the cumulative effects of the reservoir cascades on fish assemblages. Furthermore, we also demonstrated metric-specific responses to different stressors, particularly the multiple reservoir cascades, which showed the following: (1) The F-IBI index system developed based on the Random Forest model can effectively identify the superimposed effects of cascade power stations on fish integrity changes, with the cumulative time effect and the GDP index of river segments playing a key role; (2) To effectively protect the fish resources in the main stream of the Xijiang River, where priority should be given to the habitat of the natural flowing river sections between the reservoirs. At the same time, environmental regulatory policies should be formulated accordingly based on the human development status of each river section. Full article

During the dry season, river flow gradually diminishes, and surface water flow dries up. Therefore, the investigation of runoff during the dry season is of great practical significance for rational water allocation and water resource management. Based on hydrological station data from the Xijiang River Basin (XRB) from 1961 to 2020, this study examines the trend and periodic characteristics of dry-season runoff, identifies fluctuation and variability in dry-season runoff, and investigates the main circulation factor combinations influencing dynamic changes in dry-season runoff. The results indicate the following: (1) the characteristics of dry-season runoff variations are basically consistent across sub-basins in the XRB during the study period, with the minimum (21.96 × 10⁸ m³) and maximum (54.67 × 10⁸ m³) average monthly runoff occurring in February and October, respectively; (2) interannual-scale dry-season runoff exhibits periodicity of 3.53 years and 7.5 years; (3) using the Bayesian estimator of abrupt seasonal and trend change algorithm (BEAST), a seasonal abrupt point with a probability of 20.5% occurs in 1983, and the confidence interval for this abrupt point is from 1980 to 1986; (4) based on the cross wavelet approach, solar sunspots are identified as the primary circulation factor contributing to dry-season runoff in the XRB, exhibiting a significant 8–14 years resonance cycle of negative correlation with runoff during the high-energy phase from 1972 to 2006. These findings offer a new perspective on understanding the evolution of dry-season runoff and circulation factor variations, which are crucial for accurate prediction, early warning, and rational allocation of water resources during the dry season. Full article

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R²) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R² and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R² and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction. 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

The ecosystem service value (ESV) gradient-evolution pattern of a river basin’s land and water-intertwined zones has a variety of ecosystem service values, such as biodiversity conservation, water conservation, water purification, etc. The study of the ecosystem service value (ESV) gradient-evolution pattern of a river basin’s land and water-intertwined zones will provide a scientific basis for the construction and protection of the ecological security pattern of the river basins. In this study, we combined the unit area equivalent factor method and geographically weighted regression (GWR) model to classify and analyze the gradient change pattern of ESV upstream, downstream, and along the river of the Guangdong mainstream section of the Xijiang River in China, and the conclusions are as follows: (1) The corresponding ESV share of each land use type was in the following order: water bodies > broad-leaved forest > artificial wetland > scrub > paddy field > coniferous forest > natural wetland > grassland. The level of each type of ESV does not depend entirely on the size of the area but is determined by the ecosystem service functions it can provide and the level of ESV per unit area; (2) the relationship between land use types along both sides of the river in the Guangdong section of the Xijiang River Basin shows a tendency to shift from water ecosystems to terrestrial ecosystems, and the ESV gradually decreases with the increase in distance from the water. (3) The upstream to the downstream area showed a trend of changing from terrestrial ecosystems to aquatic ecosystems, such as broad-leaved forests, scrublands, water bodies, artificial wetlands, etc., and the mean land ESV showed a general trend of undulating change and decline with the reduction in the distance from the downstream area. (4) Natural factors, such as the topography and geomorphology of the basin and the socio-economic factors of power consumption, influence the spatial distribution characteristics of the ESV in the region; among them, socio-economic factors, such as total power consumption, industrial exhaust gas emissions, industrial wastewater emissions, etc., in the economically developed areas of the Xijiang River Basin are the determinants of the changes in ESV, which are generated by human living and production activities, and these indirectly affect the magnitude of the ESV by influencing the factors of temperature and gas. Full article

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin. Full article

The Long Short-Term Memory (LSTM) neural network model is an effective deep learning approach for predicting streamflow, and the investigation of the interpretability of deep learning models in streamflow prediction is of great significance for model transfer and improvement. In this study, four key hydrological stations in the Xijiang River Basin (XJB) in South China are taken as examples, and the performance of the LSTM model and its variant models in runoff prediction were evaluated under the same foresight period, and the impacts of different foresight periods on the prediction results were investigated based on the SHapley Additive exPlanations (SHAP) method to explore the interpretability of the LSTM model in runoff prediction. The results showed that (1) LSTM was the optimal model among the four models in the XJB; (2) the predicted results of the LSTM model decreased with the increase in foresight period, with the Nash–Sutcliffe efficiency coefficient (NSE) decreasing by 4.7% when the foresight period increased from one month to two months, and decreasing by 3.9% when the foresight period increased from two months to three months; (3) historical runoff had the greatest impact on streamflow prediction, followed by precipitation, evaporation, and the North Pacific Index (NPI); except evaporation, all the others were positively correlated. The results can provide a reference for monthly runoff prediction in the XJB. 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

The temperature changes in the middle-late Eocene had a profound impact on various ecosystems around the world. This has been confirmed not only in marine sediments but also in lake ecosystems, which have provided more detailed isochronous continental sedimentary records. Based on systematic palynological and element analyses of fine-grained lacustrine sediments from the Xijiang main subsag in the Pearl River Mouth Basin, southern China, we reconstructed the climate evolution of the middle-late Eocene. A total of 73 genera and 115 species of sporopollen fossils were identified from the middle-late Eocene in the study area. Three pollen zones comprising Quercoidites–Polypodiaceaesporites–Pinuspollenites, Pinuspollenites–Ulmipollenites–Cedripites, and Pinuspollenites–Abietineaepollenites–Juglanspollenites were established from bottom to top. The analysis of the vegetation types, climatic zones, and dry–humid types of the sporopollen showed that, in the study area, the Eocene was dominated by a subtropical–warm temperate climate: the early-late Eocene was dominated by a temperate climate, and the late Eocene was characterized by the prevalence of a warm temperate climate, which was consistent with the palaeoclimate reconstruction results for element geochemical indices (Fe/Mn, Sr/Cu, CIA, PIA, etc.). In addition, the comparative study showed that the middle-late Eocene in the study area was characterized by a warm and humid climate, which transitioned to a warm and cool semihumid–semiarid climate and then a warm and cool semihumid climate. These findings demonstrated a good coupling relationship with the trend for the changes in the global palaeotemperature and can be used as an isochronous continental sedimentary response. Full article

The matching degree between agricultural water and land resources directly determines the sustainable development of regional agriculture. Based on climate data corrected by delta statistical downscaling from five global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a multi-model ensemble, this study simulated the runoff used by the Variable Infiltration Capacity (VIC-3L) model under four emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and analyzed the land use changing trend to obtain the matching degree between agricultural water and land resources. The results demonstrate that annual climate factors exhibit an increasing trend, and the average annual runoff was 2128.08–2247.73 × 10⁸ m³, during 2015–2100 under the four scenarios. The area of farmland changed with an increased area of 4201 km² from 1980 to 2020. The agricultural water and land resources would be well matched under the SSP1-2.6 and SSP2-4.5 scenarios in 2021–2100. However, the risks of mismatch would occur in the 2030–2040 and 2050–2060 periods under the SSP3-7.0 scenario, and the 2030–2040 and 2080–2090 periods under the SSP5-8.5 scenario. This study can provide insight into the scientific decision support for government departments to address the challenges of mismatching risks of agricultural water and land resources. Full article

Healthy soil is the key foundation of the world’s agriculture and an essential resource to ensure the world’s food security. Soil erosion is one of the serious forms of soil degradation and a major threat to sustainable terrestrial ecosystems. In this study, we utilized a continuous Landsat satellite image dataset to map soil erosion changes (1990–2020) based on the RUSLE model across the Pearl River Basin. The study results indicated that: (1) The multi-year area-specific soil erosion average in the Pearl River Basin is approximately 538.95 t/(km²·a) with an annual soil loss of approximately 353 million tons; (2) The overall soil erosion displayed a decreasing trend over the past 30 years with an annual decreasing rate of −13.44(±1.53) t/(km²·a); (3) Soil erosion, dominated by low- and moderate-level erosion, primarily occurred in the tributary basin of Xijiang River, especially in the areas with slopes > 15°, low vegetation coverage, or poorly managed forests; (4) the NDVI and land cover were the dominant factors regulating soil erosion dynamics versus the insignificant role of precipitation played in the erosion procedure. The study results are valuable for soil erosion management and water conservation in the Pearl River Basin. 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

As a typical basin area in China, the Pearl River-Xijiang River Economic Belt (PRXREB) faces multiple types of environmental problems caused by the different development conditions of basins. To identify the situations of environmental pollution in the PRXREB, this paper constructed the Environment Pollution Composite Index (EPCI) by using four environmental pollutant emission indicators based on the entropy weight method, and explored the spatial effects and driving factors of environmental pollution by using the Spatial Error Model (SEM). The results showed that: (1) EPCI of the PRXREB decreased significantly from 2012 to 2016, and the spatial patterns were relatively stable. Wherein, the midstream and downstream were always the critical areas of environmental pollution. (2) Spatial spillover effects were significant in the PRXREB, which revealed that the local environmental pollution degree was affected by adjacent areas. (3) Industrial structure, infrastructure construction, and regulatory measures were the main driving factors of environmental pollution in the PRXREB. (4) To balance economic development and environmental protection in basin areas, environmental regulations such as environmental access, pollution payment, and cross-border early warning should be jointly established. Full article

Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability of flow predictions in the Upper Xijiang River Basin. This study evaluated the potential impacts of climate change on the streamflow and water yield of the Upper Xijiang River Basin using Arc-SWAT. The model was calibrated (1991–1997) and validated (1998–2001) using the Sequential Uncertainty Fitting Algorithm (SUFI-2). Model calibration and validation suggest a good match between the measured and simulated monthly streamflow, indicating the applicability of the model for future daily streamflow predictions. Large negative changes of low flows are projected under future climate scenarios, exhibiting a 10% and 30% decrease in water yield over the watershed on a monthly scale. Overall, findings generally indicated that winter flows are expected to be affected the most, with a maximum impact during the January–April period, followed by the wet monsoon season in the May–September period. Water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, will seriously affect the water resources and streamflow patterns in the future. Thus, critical problems, such as ground water shortages, drops in agricultural crop yield, and increases in domestic water demand are expected at the Xijiang River Basin. Full article