Spatial Distribution Characteristics and Risk Assessment of Nutrient Elements and Heavy Metals in the Ganjiang River Basin

The pollution of water bodies by nutrients and heavy metals can lead to a loss of biodiversity, environmental degradation, and harm to human health. During the two-month monitoring period (e.g., December 2019 to January 2020), variables such as trace metals (e.g., Cu, Zn, As, and Cr), nutrients (e.g., NH4+-N, TN, and TP), water temperature, pH value, dissolved oxygen (DO), chemical oxygen demand (COD) and five-day biochemical oxygen demand (BOD5) were measured at 102 monitoring points in the main stream and tributaries of the Ganjiang River in the Poyang Lake Basin. A variety of multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA), and correlation analysis, were used to conduct risk assessments and source analyses of the nutrient elements and heavy metals in the Ganjiang River system. The results show that although the Ganjiang River Basin is polluted by human activities, its water chemistry characteristics and trace metal and nutrient elements concentrations were better than the national standards. Through principal component analysis, the water pollution sources could be divided into urban sewage, agricultural activities, industrial activities, and the sources of industrial activities and transportation activities. The comprehensive risks of noncarcinogens (Hc) and comprehensive risks of carcinogens (Rc) for adults and children due to drinking water indicated that the risk from drinking water for the children in the basin was greater than that for adults, and that the Hc for adults and children was acceptable. However, the Rc for adults and children was slightly higher than the acceptable values. This study provides a reference for the fine control of the environmental water pollution sources in the Ganjiang river basin and health risk assessments in the basin, which are of great significance for improving the environmental water quality standards in the river basin and for reducing the risk of carcinogenesis.

Interactive influence of climate variability and land-use change on blue and green water resources: a case study from the Ganjiang River Basin, China

The response of blue and green water to climate and land-use change in the Ganjiang River Basin (GRB) is evaluated, via the SWAT model that combines three scenarios (the land-use/land-cover (LULC), climate change, and integrated climate and LULC change scenarios) in the 2040s (2031–2050) and 2060s (2051–2070). The results indicate that, for the GRB, cropland, woodland, and grassland show a decreasing trend, while build-up and water areas show an increasing trend in terms of future land-use change. The climatic conditions projected using NORESM1-M model data under the RCP4.5 and RCP8.5 scenarios suggest, respectively, increases in precipitation (31.17 and 27.24 mm), maximum temperature (2.25 and 2.69 °C), and minimum temperature (1.96 and 2.58 °C). Under climate change conditions, blue water is estimated to decrease by up to 16.89 and 21.4 mm under RCP4.5 and RCP8.5, while green water is estimated to increase up to 19.14 and 20.22 mm, respectively. Under the LULC changes, blue water is projected to increase by up to 5.50 and 7.57 mm, while green water shows decreases of 4.05 and 7.80 mm for the LULC2035 and LULC2055 scenarios, respectively. Under the four combined LULC and climate change conditions (RCP4.5_2040s, RCP4.5_2060s, RCP8.5_2040s, and RCP8.5_2060s), blue water tends to decrease by 0.67, 7.47, 7.28, and 9.99 mm, while green water increases by 19.24, 20.8, 13.87, and 22.30 mm. The influence of climate variation on blue and green water resources is comparatively higher than that of the integrated impacts of climate and land-use changes. The results of this study offer a scientific reference for the water resources management and planning department responsible for scheduling water resource management plan in the GRB.

Shang dynasty bronzes and society in southern China: an illustration of complexity and diversity

The production and use of bronzes had significant influences on the social developments even the formation of the early states. However, in different areas, the bronzes played different roles. By observing the different characteristics of the bronzes in the Central Plains, the Ganjiang River basin, the Xiangjiang River basin, and the Chengdu Plains, various developments of societies can be revealed. The case studies of these areas can further explain the complex relationships between the bronzes and their societies, as well as the diversity of the patterns of the developments of the ancient societies.

Multi-Scenario Integration Comparison of CMADS and TMPA Datasets for Hydro-Climatic Simulation over Ganjiang River Basin, China

The lack of meteorological observation data limits the hydro-climatic analysis and modeling, especially for the ungauged or data-limited regions, while satellite and reanalysis products can provide potential data sources in these regions. In this study, three daily products, including two satellite products (Tropic Rainfall Measuring Mission Multi-Satellite Precipitation Analysis, TMPA 3B42 and 3B42RT) and one reanalysis product (China Meteorological Assimilation Driving Datasets for the SWAT Model, CMADS), were used to assess the capacity of hydro-climatic simulation based on the statistical method and hydrological model in Ganjiang River Basin (GRB), a humid basin of southern China. CAMDS, TMPA 3B42 and 3B42RT precipitation were evaluated against ground-based observation based on multiple statistical metrics at different temporal scales. The similar evaluation was carried out for CMADS temperature. Then, eight scenarios were constructed into calibrating the Soil and Water Assessment Tool (SWAT) model and simulating streamflow, to assess their capacity in hydrological simulation. The results showed that CMADS data performed better in precipitation estimation than TMPA 3B42 and 3B42RT at daily and monthly scales, while worse at the annual scale. In addition, CMADS can capture the spatial distribution of precipitation well. Moreover, the CMADS daily temperature data agreed well with observations at meteorological stations. For hydrological simulations, streamflow simulation results driven by eight input scenarios obtained acceptable performance according to model evaluation criteria. Compared with the simulation results, the models driven by ground-based observation precipitation obtained the most accurate streamflow simulation results, followed by CMADS, TMPA 3B42 and 3B42RT precipitation. Besides, CMADS temperature can capture the spatial distribution characteristics well and improve the streamflow simulations. This study provides valuable insights for hydro-climatic application of satellite and reanalysis meteorological products in the ungauged or data-limited regions.

Impacts of Climate and Land-Use Change on Blue and Green Water: A Case Study of the Upper Ganjiang River Basin, China

Quantitatively figuring out the effects of climate and land-use change on water resources and their components is essential for water resource management. This study investigates the effects of climate and land-use change on blue and green water and their components in the upper Ganjiang River basin from the 1980s to the 2010s by comparing the simulated changes in blue and green water resources by using a Soil and Water Assessment Tool (SWAT) model forced by five climate and land-use scenarios. The results suggest that the blue water flow (BWF) decreased by 86.03 mm year−1, while green water flow (GWF) and green water storage (GWS) increased by 8.61 mm year−1 and 12.51 mm year−1, respectively. The spatial distribution of blue and green water was impacted by climate, wind direction, topography, and elevation. Climate change was the main factor affecting blue and green water resources in the basin; land-use change had strong effects only locally. Precipitation changes significantly amplified the BWF changes. The proportion of surface runoff in BWF was positively correlated with precipitation changes; lateral flow showed the opposite tendency. Higher temperatures resulted in increased GWF and decreased BWF, both of which were most sensitive to temperature increases up to 1 °C. All agricultural land and forestland conversion scenarios resulted in decreased BWF and increased GWF in the watershed. GWS was less affected by climate and land-use change than GWF and BWF, and the trends in GWS were not significant. The study provides a reference for blue and green water resource management in humid areas.

Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin

Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.