Search Results (48)

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

Accurate forecasting of river siltation is essential for ensuring inland waterway navigability and guiding sustainable sediment management. This study investigates the downstream reach of the Shihutang navigation power hub along the Ganjiang River in Jiangxi Province, China, an area characterized by pronounced seasonal sedimentation and hydrological variability. To enable fine-scale prediction, we developed a data-driven framework using a random forest regression model that integrates high-resolution bathymetric surveys with hydrological and meteorological observations. Based on the field data from April to July 2024, the model was trained to forecast monthly siltation volumes at a 30 m grid scale over a six-month horizon (July–December 2024). The results revealed a marked increase in siltation from July to September, followed by a decline during the winter months. The accumulation of sediment, combined with falling water levels, was found to significantly reduce the channel depth and width, particularly in the upstream sections, posing a potential risk to navigation safety. This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. The proposed framework provides a practical tool for early warning, targeted dredging, and adaptive channel management. Full article

Situated in the southern hilly-mountain belt of China’s “Three Zones and Four Belts Strategy”, Gannan region is a critical ecological shelter belt for the Ganjiang River. Decades of intensive mineral extraction and irrational agricultural development have rendered it into an ecologically fragile area. Consequently, multiple restoration initiatives have been implemented in the region over recent decades. However, it remains unclear how relationships among ecosystem services have evolved under these interventions and how future ecosystem management should be optimized based on these changes. Thus, in this study, we simulated and assessed the spatiotemporal dynamics of five key ESs in Gannan region from 1990 to 2020. Through integrated correlation, clustering, and redundancy analyses, we quantified ES interactions, tracked the evolution of ecosystem service bundles (ESBs), and identified their socio-ecological drivers. Despite a 31% decline in water yield, ecological restoration initiatives drove substantial improvements in key regulating services: carbon storage increased by 6.9 × 10¹² gC while soil conservation rose by 4.8 × 10⁸ t. Concurrently, regional habitat quality surged by 45% in mean scores, and food production increased by 2.1 × 10⁵ t. Critically, synergistic relationships between habitat quality, soil retention, and carbon storage were progressively strengthened, whereas trade-offs between food production and habitat quality intensified. Further analysis revealed that four distinct ESBs—the Agricultural Production Bundle (APB), Urban Development Bundle (UDB), Eco-Agriculture Transition Bundle (ETB), and Ecological Protection Bundle (EPB)—were shaped by slope, forest cover ratio, population density, and GDP. Notably, 38% of the ETB transformed into the EPB, with frequent spatial interactions observed between the APB and UDB. These findings underscore that future ecological restoration and conservation efforts should implement coordinated, multi-service management mechanisms. Full article

As an important component of the global carbon cycle, the variation patterns and driving mechanisms of the productivity and carbon sink capacity of subtropical forest ecosystems urgently need in-depth research. In this study, taking the forest ecosystem in the Ganjiang River Basin as the research object, the Biome-BGC model was used to simulate the forest productivity at different time scales (annual, seasonal, and monthly) from 1970 to 2021, and its spatio-temporal distribution characteristics and responses to climate change were analyzed. The results showed that the interannual net primary productivity (NPP) of evergreen broad-leaved forests was 771.4 g C m⁻² year⁻¹, that of evergreen coniferous forests was 631.6 g C m⁻² year⁻¹, that of deciduous coniferous forests was 610.5 g C m⁻² year⁻¹, and that of shrub forests was 262.8 g C m⁻² year⁻¹. Evergreen broad-leaved forests have greater carbon sink potential under the background of climate change. The forest productivity in the Ganjiang River Basin generally showed an upward trend, but there were obvious differences in spatial distribution, characterized by being higher in the surrounding mountainous areas and lower in the central and northern plains. The methodological framework proposed in this study is beneficial for productivity evaluation and spatio-temporal analysis of carbon balance in subtropical forest ecosystems and provides a scientific reference for model simulation and the application of forest productivity at the regional scale. Full article

Owing to climate change and increasing resource competition, elucidating the control mechanism of cultivated land productivity stability is essential. Previous research has focused on anthropogenic or climatic factors individually, overlooking their combined effects; therefore, the “climate–anthropogenic” framework was constructed. Net primary productivity (NPP) was employed to measure the cultivated land productivity and investigate the impact of climate change and anthropogenic factors on cultivated land productivity stability in Poyang Lake from 2001 to 2022. Results revealed that NPP increased but fluctuated significantly and was higher in southern Poyang Lake than in the north. The low spatial stability distribution fluctuation area was concentrated in the periphery of Poyang Lake, the periphery and riverbank comprised the middle and high fluctuation areas, and the Ganjiang River Delta exhibited high fluctuation. Multiple linear regression analysis indicated that the stability of cultivated land productivity was positively impacted by farmland and river proximity and average patch area and that fractal dimension was positively affected and negatively impacted by low farmland proximity and average annual precipitation. Stable cultivated land production and improved utilization efficiency requires irrigation and drainage system optimization and improved adaptability to climate change. Moreover, cultivated land fragmentation should be reduced, and the resilience of cultivated land to external disturbances should be enhanced. Full article

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins. Full article

This study reports on the impact of rainfall patterns and land surface changes on flood dynamics in the Meijiang River Basin, located in the upper reaches of the Ganjiang River. We formulated a range of rainfall patterns and spatial distribution scenarios and employed the MIKE SHE model to evaluate variations in flood volume, flood peak, and the timing of flood peaks. We found that under comparable areal rainfall conditions, flood volumes fluctuated by up to 6.22% among the different rainfall patterns, whereas flood peaks exhibited differences of up to 36.23%. When the rainfall center moved from upstream to downstream, both flood volume and flood peak initially increased before decreasing, with maximum values of 4.2 billion m³ and 4900 m³/s, respectively. We selected three basin scales (i.e., 10,000, 1000, and 100 km²) for comparative analysis. In the period between 1985 and 2020, the changes in land surface conditions resulted in decreases in the flood peaks of the three basins by 7.61, 11.53, and 15.79%, respectively; a reduction in the flood volumes of the three basins by 6.58, 9.60, and 10.48%, respectively; and delayed peak times by 3, 2, and 2 h, respectively. The results of this study show the significant influence exerted by rainfall patterns, the location of the rainfall centers, and the impact of changes in land surface conditions on flood processes. In particular, when the area of the basin was reduced, the influence of the underlying surface was more obvious. These results also show that flood prediction needs to consider the complex interaction of multiple factors. Full article

Vegetation is a fundamental component of terrestrial ecosystems, and accurately assessing the effects of seasonal climate variations, extreme weather events, and land use changes on vegetation dynamics is crucial. The Ganjiang River Basin (GRB), a key region for water conservation and recharge in southeastern China, has experienced significant land use changes and variable climate in the past. However, comprehensive evaluations of how these changes have impacted vegetation remain limited. To address this gap, we used machine learning models (random forest and XGBoost) to assess the impact of seasonal and extreme climate variables, land cover, topography, soil properties, atmospheric CO₂, and night-time light intensity on vegetation dynamics. We found that the annual mean NDVI showed a slight increase from 1990 to 1999 but has decreased significantly over the last 8 years. XGBoost was better than the RF model in simulating the NDVI when using all five types of data source (R² = 0.85; RMSE = 0.04). The most critical factors influencing the NDVI were forest and cropland ratio, followed by soil organic carbon content, elevation, cation exchange capacity, night-time light intensity, and CO₂ concentration. Spring minimum temperature was the most important seasonal climate variable. Both linear and nonlinear relationships were identified between these variables and the NDVI, with most variables exhibiting threshold effects. These findings underscore the need to develop and implement effective land management strategies to enhance vegetation health and promote ecological balance in the region. Full article

This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5’s overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation. Full article

Water yield is a critical component of hydrological ecosystem services, influenced by both natural environments and human activities. Changes in land use and land cover (LULC) are particularly pivotal in causing water yield variations at the basin level, particularly for the ecologically fragile Ganjiang River Basin (GRB) in southern Jiangxi province, China. Over the last 33 years, the GRB has undergone substantial LULC changes that have significantly affected its water yield. Initially, this study assessed water yield from 1990 to 2022 using the InVEST model, then predicted future LULC scenarios using the PLUS model, including natural development (ND), cropland protection (CP), ecological protection (EP), and urban development (UD). The Geodetector model was then employed to analyze the influence of various factors on water yield changes. Key findings include the following: (1) Significant landscape changes were observed, including increases in impervious surfaces, cropland, and water areas, accompanied by substantial reductions in forest and other natural lands. The most pronounced decline occurred in forested regions. (2) The total water yield decreased by 0.44 × 10¹⁰ m³ over the study period, exhibiting fluctuations until 2016 and stabilizing afterward. Water yield was generally higher in the northeast and lower in the southwest, primarily influenced by actual evapotranspiration, LULC, and precipitation. (3) The impact of LULC changes on water yield varied by scenario, with the scenarios ranked from most to least impactful as follows: UD, ND, CP, EP. This variation is mainly due to the different rates of evapotranspiration and infiltration associated with land cover. These insights are crucial for guiding policymakers in developing effective LULC strategies that promote ecological restoration and sustainable water management in the basin. Full article

With global climate change and irrational human activities, regional water resource conflicts are becoming more and more pronounced. The availability of water resource in watersheds can be indicated by the water yield. Exploring the factors that influence the water yield is crucial in responding to climate change and protecting water resource. Previous research on the factors influencing the water yield has frequently adopted a macro-level perspective, which has failed to reflect the influencing mechanisms of changes at the local scale adequately. Therefore, this study proposes a novel framework for integrally evaluating the impacts of climate change and human activities on water yield services from both local and global perspectives. Taking Ganzhou City, the source of the Ganjiang River, as an example, the results show the following: (1) Ganzhou City had the largest water yield of 1307.29 mm in 2016, and the lowest was only 375.32 mm in 2011. The spatial distribution pattern was mainly affected by the surface environment, and the high-value water yield regions in the study area were predominantly located in urban areas with flat terrain. (2) At the local scale, regions where human activities contribute more than 80% accounted for 25% of the area. In comparison, the impact of climate change accounted for 0.95%. The contribution rate of human activities to the water yield in Ganzhou City was significantly greater than that of climate change. (3) At the global scale, the simulation results of four scenarios show that climate change contributed (>98%) to the water yield, which is significantly higher than human activities (<2%). This study puts forward pioneering views on the research of water yield driving forces and provides a valuable theoretical basis for water resource protection and ecological environment construction. Full article

Ecological restoration programs (ERPs) can lead to dramatic land use change, thereby affecting ecosystem services and their interaction. Determining the optimal ERPs is a crucial issue for ecological restoration in ecologically fragile regions. This study analyzed the impacts of land use change on four water-related ecosystem services (WESs), namely water yield, soil retention, water purification, and food production in the Ganjiang River basin, China during the past two decades. Then, trade-off and synergy between WESs were detected based on correlation analysis. Finally, to quantify the effect of ERPs on WESs, we comprehensively considered the types and intensity of ERPs and designed four categories of scenarios: returning farmland to forest (RFF) scenarios; planting forest (PF) scenarios; riparian forestland buffer (RFB) scenarios; and riparian grassland buffer (RGB) scenarios. Each category contains five scenarios of different intensities. The results showed that water yield, soil retention, and food production increased while water purification decreased from 2000 to 2020. The deterioration of water quality was mainly due to transitions from forestland to farmland and built-up land. Trade-offs only occurred between regulating services and provisioning services. Among all ecological restoration scenarios, only the RFF scenarios can significantly improve soil retention and water purification at the same time, although food production will decrease. Considering food security, returning farmland with a slope greater than 10 degrees to forestland was the optimal scenario in the study area. This study highlighted that both the type and intensity of ERPs should be considered in ecological restoration. This study can contribute to ecological restoration in the Ganjiang River basin and other subtropical mountainous regions. Full article

The Yangtze River floodplain is an area with an extremely rich diversity of fish species. Poyang Lake, as an important part of this river–floodplain system, is a crucial habitat for the survival of fish. However, prolonged human activities, such as environmental pollution and hydroelectric development, have degraded the habitat in the Poyang Lake Basin, posing threats to fish populations. Understanding genetic diversity is crucial for maintaining fish populations and understanding their dynamics. The genetic diversity of Hemiculterella wui, an economically endemic species in China of subfamily Cultrinae (Cypriniformes: Cyprinidae), has been understudied. This study investigated the genetic diversity and structure of H. wui populations from the Ganjiang, Xinjiang, Fuhe, and Raohe rivers in the Poyang Lake Basin using mitochondrial Cytb gene analysis. Results showed high haplotype diversity but low nucleotide diversity in H. wui’s Cytb sequences. Analysis of molecular variance (AMOVA) showed no significant geographic genetic structure among populations. Haplotype network analysis revealed no clear geographical clustering. Neutrality tests and haplotype nucleotide mismatch distribution indicated that all populations had experienced expansion events. These findings suggest that H. wui in Poyang Lake does not show a distinct geographic structure. However, it is still necessary to monitor the genetic characteristics of H. wui to maintain the genetic diversity of fishes in Poyang Basin, considering the threat of habitat loss and fragmentation to the population. Full article

The natural hydrological cycle of basins has been significantly altered by climate change and human activities, leading to considerable uncertainties in attributing runoff. In this study, the impact of climate change and human activities on runoff of the Ganjiang River Basin was analyzed, and a variety of models with different spatio-temporal scales and complexities were used to evaluate the influence of model choice on runoff attribution and to reduce the uncertainties. The results show the following: (1) The potential evapotranspiration in the Ganjiang River Basin showed a significant downward trend, precipitation showed a significant upward trend, runoff showed a nonsignificant upward trend, and an abrupt change was detected in 1968; (2) The three hydrological models used with different temporal scales and complexity, GR1A, ABCD, DTVGM, can simulate the natural distribution of water resources in the Ganjiang River Basin; and (3) The impact of climate change on runoff change ranges from 60.07% to 82.88%, while human activities account for approximately 17.12% to 39.93%. The results show that climate change is the main driving factor leading to runoff variation in the Ganjiang River Basin. Full article

Accurate forecasting of monthly runoff is essential for efficient management, allocation, and utilization of water resources. To improve the prediction accuracy of monthly runoff, the long and short memory neural networks (LSTM) coupled with variational mode decomposition (VMD) and principal component analysis (PCA), namely VMD-PCA-LSTM, was developed and applied at the Waizhou station in the Ganjiang River Basin. The process begins with identifying the main forecasting factors from 130 atmospheric circulation indexes using the PCA method and extracting the stationary components from the original monthly runoff series using the VMD method. Then, the correlation coefficient method is used to determine the lag of the above factors. Lastly, the monthly runoff is simulated by combining the stationary components and key forecasting factors via the LSTM model. Results show that the VMD-PCA-LSTM model effectively addresses the issue of low prediction accuracy at high flows caused by a limited number of samples. Compared to the single LSTM and VMD-LSTM models, this comprehensive approach significantly enhances the model’s predictive accuracy, particularly during the flood season. Full article