Dongjiang River Research Articles

A framework for assessing compound drought events from a drought propagation perspective

Compared with a single drought event, compound drought (i.e., two or more droughts occurring simultaneously) has more substantial effects on economic growth, water resources, and the ecological environment. A useful framework for assessing compound drought events from a drought propagation perspective was proposed. The framework includes the definition, identification, and risk assessment of compound drought. We defined compound drought as a drought event from a drought propagation perspective when meteorological drought and hydrological drought occur simultaneously. The Standardized Streamflow Index (SSI) and Standardized Precipitation Index (SPI) were used to represent hydrological and meteorological drought, respectively. By applying a certain truncation threshold of run theory, the compound drought events in the time series were identified and characterized according to their duration and severity. Five univariate distribution functions (i.e., Log-normal, Exponential, Gamma, Weibull, and Pareto) and four bivariate copula models (i.e., Gaussian, Clayton, Frank, and Gumbel) were used for fitting both the duration and severity of compound drought and their joint return periods. A 40-year monthly streamflow and precipitation dataset from the Dongjiang River Basin, which is located on the southern coast of China, was used as a case study. The characteristics of compound drought, including duration and severity, were captured by considering the time continuity of meteorological and hydrological droughts from a drought propagation perspective based on run theory. The most severe compound droughts in the study area were observed in 1963 and 2004–2005. The characteristics of compound drought duration and severity were related to the timescale of the drought index (i.e., SSI and SPI). For the study basin, the SPI at a 2–3-month timescale better matched the SSI on the monthly timescale compared with other timescales. The Gaussian-copula function better characterized the joint return periods between the duration and severity of compound drought compared with three other copula models. Although reservoir regulation played an important role in decreasing the duration (lower by 8.25%) and severity (lower by 9.27%) of compound drought, it had little effect on extreme compound drought events. Overall, the results indicated that the proposed method provides a useful tool for statistical assessments of compound drought from a drought propagation perspective. This framework could also be applied to other regions.

Comprehensive assessment of water environmental carrying capacity for sustainable watershed development

Due to insufficient understanding of human-water interaction, many water-related problems arise in watersheds, posing severe threats to the sustainability of watershed development. Although water environmental carrying capacity (WECC) is a powerful tool to support sustainable development of watersheds, few studies considered aquatic ecological factors and uncertainty in indicator values, leading to losses of sample information in the evaluation of WECC. This paper developed a systematic framework for comprehensive WECC assessment that included the indicator system and a novel variable fuzzy pattern recognition (VFPR) approach. The WECC index system incorporated aquatic ecological factors, and addressed uncertainties associated with the indicator values. The proposed VFPR-based assessment model could realize successive evaluation to retain more original information of the sample and distinguish similar result values by treating the sample as having a continuous degree of membership instead of the traditional point form. In addition, it could be more adaptable to various circumstances including extreme cases, and closely reflect the impacts of indicator changes on the results. The established evaluation framework has been applied to Dongjiang River Basin in Guangdong Province. The spatial differences and main influencing factors of WECC in the study area were analyzed. Results show that 50% and 16.7% of the sub-regions in the study area would be subject to a poor level of WECC under pessimistic and optimistic circumstances, respectively. WECCs in the upper and lower reaches are the best and worst, respectively, which is in line with the levels of economic development in the Dongjiang River Basin. The proposed method can also be applicable to many other problems involving numerous indicators.

Spatio-Temporal Analysis of Ecological Vulnerability and Driving Factor Analysis in the Dongjiang River Basin, China, in the Recent 20 Years

The global ecological environment faces many challenges. Landsat thematic mapper time-series, digital elevation models, meteorology, soil types, net primary production data, socio-economic data, and auxiliary data were collected in order to construct a comprehensive evaluation system for ecological vulnerability (EV) using multi-source remote sensing data. EV was divided into five vulnerability levels: potential I, slight II, mild III, moderate IV, and severe V. Then, we analyzed and explored the spatio-temporal patterns and driving mechanisms of EV in the region over the past 20 years. Our research results showed that, from 2001 to 2019, the DRB was generally characterized as being in the severe vulnerability class, with higher upstream and downstream EV classes and a certain amount of reduction in the midstream EV classes. Moreover, EV in the DRB continues to decrease. The spatio-temporal EV patterns in the DRB were significantly influenced by the relative humidity, average annual temperature, and vegetation cover over the past 20 years. Our work can provide a basis for decision-making and technical support for ecosystem protection, ecological restoration, and ecological management in the DRB.

Reservoirs regulate the relationship between hydrological drought recovery water and drought characteristics

Regulation of streamflow by large reservoirs alters the characteristics of hydrological drought. However, few studies have focused on the impacts of regulation on the water required (WR) for hydrological drought recovery, nor on the response relationship between WR and drought characteristics (i.e., duration and severity). Therefore, we proposed a comparative approach that integrates reconstructing unregulated streamflow and a drought identification method using variable threshold levels (VTLs) to evaluate the impacts. Long-term (>40 years) observational datasets for streamflow at a hydrometric station and inflow and outflow at multiple large reservoirs in the Dongjiang River Basin were used. Using VTLs that accounted for seasonal differences in hydrological processes, our method performed well in identifying historical hydrological droughts and their characteristics in the study area. The WR mainly depended on drought severity rather than drought duration and there was a clear nonlinear response relationship (i.e., power function) between WR and drought severity. The performance indices (i.e., R2 = 0.92, NSE = 0.97, PBIAS = 13.57%) indicated that the optimal nonlinear function model could accurately simulate the WR of hydrological drought events. Large reservoirs reduce the frequency of hydrological droughts, shorten drought duration, and reduce drought severity by storing water in the flood season and releasing it in the dry season. The presence of reservoirs shifted the relationship between WR and drought severity from linear to nonlinear. These findings showed that the proposed methodology can help us to optimize the management of water resources for drought prevention and disaster reduction under changing environments.

Impacts of rainfall spatial and temporal variabilities on runoff quality and quantity at the watershed scale

Due to climate change and human disturbances, rainfall spatial and temporal variabilities at the watershed have become increasingly prominent, significantly impacting the patterns of runoff generation and pollutant transportation. Originally, rainfall variabilities in space and time were frequently neglected or idealized in the traditional applications of hydrology and water quality models. This research attempted to investigate the responses of runoff quality and quantity simulation to rainfall spatial and temporal variabilities. We used Rainfall Hazard Modeling System (RainyDay model) combined with TRMM (Tropical Rainfall Measuring Mission) rainfall products to generate rainfall events for 500 return periods. Such events were resampled into eight rainfall scenarios with multiple levels of spatial and temporal variabilities. They were used to drive the Soil and Water Assessment Tool (SWAT) to simulate runoff quality (TP as the index) and quantity under multiple watershed scales and return periods at the Dongjiang River watershed. Analysis of Variance was employed to analyze the relative contributions of rainfall spatial and temporal variabilities to the total variabilities of runoff quality and quantity. The results highlighted that rainfall spatial and temporal variabilities would have notable impacts on runoff quality and quantity under multiple watershed scales and event magnitudes. For headwater at small watershed scales, rainfall spatial variability would have a relatively significant impact on runoff quality and quantity. For relatively larger watershed scales, rainfall temporal variability showed an increasingly important impact than spatial variability for smaller return periods, whereas the opposite results can be obtained for relatively larger return periods. The results can be caused by the following reasons:1) rainfall was concentrated in the middle and lower reaches of the watershed; 2) it was based on the daily scale. Our research suggested that attention should be paid to spatial and temporal variabilities of rainfall inputs in hydrological and water quality models.

Machine learning approaches for improving near-real-time IMERG rainfall estimates by integrating Cloud Properties from NOAA CDR PATMOSX

AbstractThe Global Precipitation Measurement (GPM) mission provides satellite precipitation products with an unprecedented spatio-temporal resolution and spatial coverage. However, its near-real-time (NRT) product still suffers from low accuracy. This study aims to improve the early run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) by using four machine learning approaches, i.e., support vector machine (SVM), random forest (RF), artificial neural network (ANN), and Extreme Gradient Boosting (XGB). The cloud properties are selected as the predictors in addition to the original IMERG in these approaches. All the four approaches show similar improvement, with 53%-60% reduction of root-mean-square error (RMSE) compared with the original IMERG in a humid area, i.e., the Dongjiang River Basin (DJR) in southeastern China. The improvements are even greater in a semi-arid area, i.e., the Fenhe River Basin (FHR) in central China, the RMSE reduction ranges from 63%-66%. The products generated by the machine learning methods performs similarly to or even outperform than the final run of IMERG. Feature importance analysis, a technique to evaluate input features based on how useful they are in predicting a target variable, indicates that the cloud height and the brightness temperature are the most useful information in improving satellite precipitation products, followed by the atmospheric reflectivity and the surface temperature. This study shows that a more accurate NRT precipitation product can be produced by combining machine learning approaches and cloud information, which is of importance for hydrological applications that requires NRT precipitation information including flood monitoring.

Quantifying land use heterogeneity on drought conditions for mitigation strategies development in the Dongjiang River Basin, China

Spatially-invariant land use and cover changes (LUCC) are not suitable for managing non-stationary drought conditions. Therefore, developing a spatially varying framework for managing land resources is necessary. In this study, the Dongjiang River Basin in South China is used to exemplify the significance of spatial heterogeneity in land planning optimization for mitigating drought risks. Using ERA5 that is the 5th major atmospheric reanalysis from the European Centre for Medium-Range Weather Forecast, we computed the Standardized Runoff Index (SRI) to quantify the hydrologic drought during 1992 to 2018. Also, based on Climate Change Initiative land use product, The Geographically Weighted Principal Component Analysis was used to identify the most dominant land types in the same period. Then, we used the Emerging Hot Spots Analysis to characterize the spatiotemporal evolution of historical LUCC and SRI. The spatially varying coefficients of Geographically and Temporally Weighted Regression models were used to reveal the empirical relationships between land types and the SRI. Results indicated that rainfed cropland with herbaceous cover, mosaic tress and shrub, shrubland, and grassland were four land types having statistical correlations with drought conditions over 27 years. Moreover, since 2003, the DRB was becoming drier, and the northern areas generally experienced severer hydrologic drought than the south. More importantly, we proposed region-specific land-use strategies for drought risk reductions. At a basin scale, we recommended to 1) increase rainfed herbaceous cropland and 2) reduce mosaic tree and shrub. At a sub-basin scale, the extents of shrub and grassland were suggested to increase in the northern DRB but to reduce in the south. Region-specific land use planning, including suitable locations, scales, and strategies, will contribute to handling current ‘one-size-fits-all’ LUCC. Planners are suggested to integrate spatial characteristics into future LUCC for regional hydrologic management.

Predicting river dissolved oxygen time series based on stand-alone models and hybrid wavelet-based models

Accurate prediction of dissolved oxygen time series is important for improving the water environment and aiding water resource management. In this study, four stand-alone models including multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN) and random forest (RF), and four hybrid models based on wavelet transform (WT) including WT-MLR, WT-SVM, WT-ANN and WT-RF were used to predict the daily dissolved oxygen (DO) at 1–5-day lead times in the Dongjiang River Basin, China. To make the prediction robust, the maximal information coefficient (MIC) was used to capture comprehensive information between DO and explanatory variables. The 5-fold cross validation grid search approach was used to optimize parameters of machine learning tools. Two types of frameworks of WT: direct framework (i.e., only the explanatory variables were decomposed) and multicomponent framework (i.e., both explanatory variables and target variables were decomposed) were used to construct hybrid models. The results show that MIC extracts four optimal explanatory variables: previous DO, water temperature, air temperature and air pressure. Four evaluation parameters including correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and root mean square error (RMSE) indicate that the prediction accuracy decreases as the lead time changes from 1 to 5 days. In terms of the stand-alone models, MLR model outperforms the other three models with higher NSE values of 0.616–0.921, and lower RMSE values of 0.503–1.111. With regard to the hybrid models, WT-ANN and WT-MLR models exhibit higher performance, and multicomponent framework performs better than direct framework in all hybrid models. In general, the multicomponent framework of WT can improve the prediction accuracy of stand-alone models at a certain degree, while the direct framework shows no obvious advantage.

Pilot-scale constructed bypass channel for urban river restoration: the remedial efficiency and the variance in biodiversity.

Like the blood vessels of the cities, urban rivers play a significant role on maintaining the cities' sustainable development. In addition to the prevention of pollutants discharge and improving the river water quality, the restoration of the urban rivers' ecosystem should be well concerned. To fill this gap, a pilot-scale study with constructed bypass channel (CBC) was conducted. The pollutants reduction by the aquatic plants of the CBC was evaluated, and the similarities/differences of the aquatic biodiversity between the CBC and the natural rivers were analyzed. The results indicated that the mean removal efficiency of TP, NH3-N, TN, and COD by the CBC was 66%, 60%, 52%, and 36%, respectively. Chlorophyta, Bacillariophyta, and Cyanophyta were the dominant phytoplankton phyla in the CBC which was in accordance with the studies conducted in the Dongjiang River and the Pearl River. During the study period of about 6 months, the population density and the biomass of the phytoplankton and the zooplankton increased over time. The quality of the influent dominated the aquatic organisms' diversity of the CBC. N-element dominated not only the phytoplankton variability, but also the bacterial species of the CBC. The timber pile and the submerged plant root soil were more suitable for the growth of the functional bacteria; thus, the construction of the river restoration infrastructures should avoid using large-scale cement materials. Overall, the study would improve the understanding of urban river restoration practice and provide guidance for future restoration practice especially from the aquatic ecological perspectives.

A dual-randomness bi-level interval multi-objective programming model for regional water resources management

In this research, a dual-randomness bi-level interval multi-objective programming (DR-BIMP) model was developed for supporting water resources management among multiple water sectors under complexities and uncertainties. Techniques of bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP) were incorporated into an integrated modeling framework to achieve comprehensive consideration of the complexities and uncertainties of water resources management systems. The DR-BIMP model can not only effectively deal with the interactive effects between multiple decision-makers in complex water management systems through the bi-level hierarchical strategies, but also can characterize the multiple uncertainties information expressed as interval format and probability density functions. It could thus improve upon the existing bi-level multi-objective programming through addressing discrete interval parameters and dual-randomness problems in optimization processes simultaneously. Then, the developed model was applied to a real-world case to optimally allocate water resources among three different water sectors in five sub-regions in the Dongjiang River basin, south China. The results of the model include determining values, interval values, and stochastic distribution information, which can assist bi-level decision-makers to plan future resources effectively to some extent. After comparing the variations of results, it is found that an increasing probability level can lead to higher system benefits, which is increased from [20,786.00, 26,425.92] × 108 CNY to [22,290.84, 27,492.57] × 108 CNY, while the Gini value is reduced from [0.365, 0.446] to [0.345, 0.405]. A set of increased probability levels gives rise to the lower-level objectives. Furthermore, the advantages of the DR-BIMP model were highlighted by comparing with the other models originated from the developed model. The comparison results indicated that the DR-BIMP model was a valuable tool for generating a range of decision alternatives and thus assists the bi-level decision-makers to identify the desired water resources allocation schemes under multiple scenarios.

Assessment of epigenotoxic profiles of Dongjiang River: A comprehensive of chemical analysis, in vitro bioassay and in silico approach

This research explored the occurrence, epigenetic toxic profiling and main toxic pollutants of POPs in surface water of Dongjiang River, southern China. The concentrations of selected POPs including polycyclic aromatic hydrocarbons (PAHs), endocrine disrupting chemicals (EDCs), phthalate esters (PAEs) and polybrominated diphenyl ethers (PBDEs) of surface water from 18 sites were investigated. ∑16PAHs and ∑4EDCs were at a moderate level, while ∑6PAEs and ∑6PBDEs had low pollution levels. PAHs, EDCs and PAEs showed higher concentrations in dry season than those in wet season, and the loading of selected POPs in tributaries was higher than those in mainstream due to intensive manufactures and lower runoff volume. Moreover, activities of DNA methyltransferase (DNMT)1, histone deacetylase (HDAC2, HDAC8) were confirmed to be sensitive indicators for epigenetic toxicity. The DNMT1-mediated epigenetic equivalency toxicity of organic extracts in Dongjiang River were more serious than those of HDAC2 and HDAC8. Correlation analysis shown binding affinity between POPs and DNMT1, HDAC2 and HDAC8 could be regarded as toxic equivalency factors. Risk assessment suggested that 4-nonylphenol and bisphenol A were the largest contributors to epigenetic risk. This study is the first attempt to quantify epigenetic toxicity and epigenetic risk evaluation of river water.

A year-long cyclic pattern of dissolved organic matter in the tap water of a metropolitan city revealed by fluorescence spectroscopy

Delivering drinking water with stable quality in metropolitan cities is a big challenge. This study investigated the year-long dynamics of dissolved organic matter (DOM) in the tap water and source water of a metropolitan city in southern China using fluorescence spectroscopy. The DOM detected in the tap water, and source water of Shenzhen city was season and location-dependent. A year-long cyclic trend of DOM was found with predominate protein-like fluorescence in the dry season compared to the humic-like enriched DOM in the wet season. A general DOM pattern was estimated by measuring the shift in dominant fluorescence regions on the excitation-emission matrix (EEM). The difference in fluorescent DOM (FDOM) composition (in terms of the ratio of protein-like to humic-like fluorescence) was above 200% between wet and dry seasons. The taps associated with reservoirs receiving water from the eastern tributary of Dongjiang River showed significant changes in protein-like contents than the taps with source water originating from the western part of the river. This study highlights the importance of optimizing drinking water treatment plants' operational conditions after considering seasonal changes and source water characteristics.

Emission Estimation and Fate Simulation of Dichlorvos in the Dongjiang River Watershed

The application of pesticides and their constant inputs have resulted in the universal contamination of the environments within watersheds, and it is essential to assess the level of contamination. Compared with traditional monitoring methods, models coupled with GIS show more obvious advantages. However, the difficult access to application scenarios and emission data of pesticides hinder the modeling and assessment of the pesticides used in watersheds. We established an emission estimation method of pesticides, which was based on the planting scenarios and pesticide application standards of different crops in administered regions and by situational analysis and mathematical deduction, the usage of each sub-basin was able to be derived. Then, by using the aforementioned results as source input data, a semi-distributed watershed hydrological, SWAT (soil and water assessment tool) model, was used to simulate the fate of dichlorvos in the Dongjiang River watershed as an example. The verification results show that the comparisons of magnitude between the simulation and the observations were almost within 1, indicating that the SWAT model, which is based on a source input estimation, was able to assess the environmental fate of pesticides in watershed effectively. Also, the modeling results indicated that the annual emission load of the dichlorvos in the watershed accounted for 3.72% of the usage. Moreover, the losses due to degradation and other reactions within the channels reach 2.35%. Most reaches of the Dongjiang River watershed have mass concentrations over 0.1 μg·L-1, demonstrating that certain safety concerns exist.

Land-use configuration has significant impacts on water-related ecosystem services

The impacts of land-use change on ecosystem services (ESs) are currently a high profile issue, but the impacts of land-use configuration change on ESs have not been well studied so far. Taking the headwaters' region of the Dongjiang River (HRDR) as an example, the current study designed a specific framework for exploring the impacts of land-use configuration change on ESs independently. The Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model was integrated with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to simulate water conservation services (WCS), nutrient retention services (NRS), and sediment retention services (SRS) in HRDR for nine planning scenarios. The main conclusions are: (1) land-use configuration change has significant impacts on the provision of WCS, NRS, and SRS; (2) mean patch area of landscape is the main factor affecting the provision of WCS, while mean slope of woodland is the main factor affecting the provision of NRS and SRS; and (3) none of the nine projected land-use configurations could simultaneously maximize the provision of WCS, NRS, and SRS; overall, the ninth land-use configuration was the optimal configuration. The current study bridges the gaps between land-use configuration change and the provision of ESs, and emphasizes the urgency of taking land-use configuration into consideration in future land-use planning to improve the provision of water-related ESs.

东江流域景观格局对氮、磷输出的影响

东江流域景观格局对氮、磷输出的影响

Forest effects on runoff under climate change in the Upper Dongjiang River Basin: insights from annual to intra-annual scales

Climate change and large-scale afforestation characterize the conditions in the Upper Dongjiang River Basin (UDRB), which is one of the most important headwater basins in southern China. It is important to understand whether, and to what extent, the observed runoff change can be attributed to forest and/or climate change. Using process- and relation-based methods, we found precipitation in spring (March–May) decreased notably, while precipitation in summer (June–August) showed an increase from the reference period (1961–1990) to the afforestation period (1991–2010). In comparison, annual averaged potential evapotranspiration did not change much. Both of the methods indicated forest had a positive effect while climate change exerted a negative impact on annual averaged runoff in the UDRB. As a result, the observed annual averaged runoff only showed a little decrease from the reference period to the afforestation period. The climate change impact on monthly averaged runoff basically followed the pattern of precipitation change. Except in July and August, climate change exerted negative or little impact on runoff in most of other months. In comparison, the forest effects on monthly averaged runoff change showed a totally different pattern. Except in May and June, forest exerted positive impact on runoff in other months. As a result, the observed monthly averaged runoff in May and June experienced notable reduction, while those in other months experienced increase or no change. The UDRB provides evidence that additional forest cover would not injure but even increase runoff, especially dry season runoff. The study has important implications for sustainable water management and afforestation in this subtropical region and for similar river basins.

Multi-timescale assessment of propagation thresholds from meteorological to hydrological drought

Hydrological drought usually lags behind meteorological drought. Obtaining the propagation threshold (PT) from meteorological drought to hydrological drought is important for providing early warnings of hydrological drought. Previous studies have only used single timescales to characterize PT; however, a single timescale cannot accurately describe the propagation attributes from meteorological to hydrological drought because drought has multi-timescale features. In addition, several methods can be used to obtain PT, such as run theory, correlation analysis, and non-linear response methods. However, these methods might produce different estimates of PT. Here, multi-timescale drought indices, namely the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI), were used to represent meteorological drought and hydrological drought. PT estimates at multiple timescales (e.g., 1-month, 3-month, and 12-month) obtained from run theory, correlation analysis, and non-linear response methods were compared, and the possible reasons for differences in the PT estimates are discussed. We conducted a case study of three sub-basins (Xinfengjiang River, Qiuxiangjiang River, and Andunshui River) with low levels of human activity in the Dongjiang River Basin, which is located in a humid region in southern China. We found that estimates of PT differed at different timescales of drought indices and with different methods at the same timescales. Longer timescales of hydrological drought corresponded to larger PT and vice versa. The major cause of this pattern was the fact that different timescales of drought indices showed different response sensitivities to drought events. The PT obtained from run theory was the shortest; thus, run theory can provide conservative warnings to aid drought prevention and mitigation. Our findings can help drought managers select effective tools to manage the early stages of hydrological drought based on meteorological forecasts and thus minimize the negative impacts of hazards posed by drought.

Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks

The accidental leakage of industrial wastewater containing heavy metals from enterprises poses great risks to resident health, social instability, and ecological safety. During 2005–2018, heavy metal mixed pollution accidents comprised approximately 33% of the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed to quantitatively predict ecological and human health risks for heavy metal mixed pollution accidents at the watershed scale. To estimate the probability distributions of joint ecological exposure once a heavy metal mixed pollution accident occurs, a Copula-based joint exposure calculation method, comprised of a hydro-dynamic model, emergent heavy metal pollution transport model, and the Copula functions, is embedded. This approach was applied to the risk assessment of acute Cr6+-Hg2+ mixed pollution accidents at 76 electroplating enterprises in 24 risk sub-watersheds of the Dongjiang River downstream watershed. The results indicated that nine sub-watersheds created high ecological risks, while only five created high human health risks. In addition, the ecological and human health risk levels were highest in the tributary (the Xizhijiang River), while the ecological risk was more critical in the river network, and the human health risk was more serious in the mainstream of the Dongjiang River. The quantitative risk assessment provides a substantial support to incident prevention and control, risk management, as well as regulatory decision making for electroplating enterprises.

Relationship between dissolved organic matter and phytoplankton community dynamics in a human-impacted subtropical river

Abstract Dissolved organic matter (DOM) is closely related to phytoplankton blooms, one of the most concerning environmental problems globally. DOM originates from phytoplankton production and metabolic activity and, in turn, can influence phytoplankton biomass and community structure. Therefore, clarifying the relationship between phytoplankton community dynamics and DOM has implications for bloom prevention and control as well as for aquatic ecosystem management. To understand this relationship further, we investigated phytoplankton communities at 110 sites in the dry and rainy seasons within the Dongjiang River basin, China, which is known for high rates of urbanization, industrialization, and agricultural intensification. The interaction between DOM (two humic-like DOM [C1 and C2] and two protein-like DOM [C3 and C4]) and phytoplankton community dynamics was investigated. Phytoplankton biomass and taxonomic composition were significantly related to C2, C3, and C4. The effect of C3 and C4 on phytoplankton taxonomic composition was prominent. C2 originating from agricultural wastewater increased phytoplankton biomass and changed phytoplankton taxonomic composition, whereas C1, originating from terrestrial soils and plants, did not stimulate phytoplankton biomass in rivers. There were clear change points, where phytoplankton abundance rapidly increased or decreased with changes in the concentrations of DOM components. Results suggested that DOM controls phytoplankton community dynamics and their relationship changes with variations in DOM chemical composition. DOM generated by anthropogenic activities may act as a potential warning of phytoplankton blooms.

Copula-based exposure risk dynamic simulation of dual heavy metal mixed pollution accidents at the watershed scale

Most heavy metal exposure and pollution results from multiple industrial activities, including metal processing in refineries, and microelectronics. These issues pose a great threat to human health, ecological balance, and even societal stability. During 2012–2017, China, in particular, faced the challenge of 23 heavy metals accidents, six of which were extraordinarily serious accidents. Accidental environmental pollution is rarely caused by a single heavy metal, but rather by heavy metal mixtures. To address the need for a joint exposure risk assessment for heavy metal mixed pollution accidents at the watershed scale, a Copula-based exposure risk dynamic simulation model was proposed. A coupled hydrodynamic and accidental heavy metal exposure model is constructed for an hourly simulation of the exposure fate of heavy metals from each risk source once accidental leakage has occurred. The Copula analysis was introduced to calculate the dual heavy metal joint exposure probability in real time. This method was applied to an acute Cr6+-Hg2+ joint exposure risk assessment for 43 electroplating plants in nine sub-watersheds within the Dongjiang River downstream basin. The results indicated seven risk sources (i.e., S1, S4, H18, H23, H27–H28, and H34) that presented relatively high exposure risk to their surrounding sub-watersheds. Spatially, the acute exposure risk level was highest in the tributary basin (sub-watershed XW) than in the mainstream (sub-watershed DW2) and the river network (sub-watershed RW) of the lower reaches of the Dongjiang River. This research highlights an effective probabilistic approach for performing a joint exposure risk analysis of heavy metal mixed pollution accidents at the watershed scale.