Chinese Watershed Research Articles

Spatiotemporal variations and the ecological risks of microplastics in the watersheds of China: Implying the impacts of the COVID-19 pandemic

China is not only the first reported place of the COVID-19 pandemic but also is the biggest microplastic emitter in the world. Nevertheless, the impact of the COVID-19 pandemic on microplastic pollution in the watersheds of China remains poorly understood. To address this, the present study conducted a data mining and multivariate statistical analysis based on 8898 microplastic samples from 23 Chinese watershed systems before and during the COVID-19 pandemic. The results showed that the COVID-19 pandemic extensively affected the abundance, colors, shapes, polymer types, and particle sizes of microplastic in Chinese watershed systems. Before and during the COVID-19 pandemic, 77.27 % of the Chinese watershed systems observed increased microplastic abundance. Moreover, the COVID-19 pandemic itself, natural conditions (such as altitude and weather), and anthropogenic factors (such as civil aviation throughput) are highly intertwined, jointly impacting the microplastic in the watersheds of China. From the perspective of ecological risks, the COVID-19 pandemic was more likely to aggravate the microplastic pollution in the middle and down reaches of the Yangtze River Watersheds. Overall, whether before or during the COVID-19 pandemic, the main watershed systems of China still stayed at a high pollution level, which rang the alarm bell that watershed systems of China had been at serious ecological risk accused of microplastic contamination.

Influence of climate and land use on watershed anthropogenic phosphorus inputs and riverine phosphorus export dynamics: A global analysis

Many studies have found predictive relationships between riverine phosphorus (P) export and net anthropogenic P inputs (NAPI) at the watershed scale, but the global or regional extent of these relationships has not been empirically quantified. Herein, we present a data-driven global assessment of the response of riverine total P (TP) fluxes to NAPI based on 358 watersheds. NAPI exhibited high spatial heterogeneity (2–12,085 kg P km−2 yr−1) and was well correlated with riverine TP fluxes. Riverine TP export fractions of NAPI were primarily regulated by NAPI components, hydroclimate factors, and land-use as determined through a random-forest meta-analysis. In watersheds dominated by disturbed land-use (e.g., agricultural and developed lands), runoff emerged as pivotal climate-related factors influencing riverine export fractions of NAPI. In watersheds dominated by natural land-use, runoff, precipitation and temperature were identified as the most critical factors. We developed a mixed-effects meta-regression model (R2 = 0.63–0.70, RMSE = 19–78 %, n = 87–202) to examine the quantitative relationship between riverine TP fluxes and NAPI, which avoids subjectivity in selecting influencing factors and regression forms. The model estimated that legacy P contributed 14–17 % of annual riverine TP fluxes in Chinese watersheds, 25 % in North American watersheds and 11–27 % in European watersheds. Annual NAPI contributions to annual riverine TP flux were 83–86 % in China, 75 % in North America and 73–89 % in Europe. The model forecasted 52–67 %, 69–71 % and 74–77 % reductions in riverine TP fluxes across Chinese, North American, and European watersheds by 2050 under five shared socio-economic pathway scenarios compared to 2010 baseline conditions, respectively. This study provides a straightforward and reliable method for quantifying anthropogenic P input and riverine P export dynamics within an acceptable error range. It provides guidance for developing phosphorus pollution control strategies to counter potential increases in phosphorus inputs due to expected changes in climate and land use.

A spatially explicit interpretable machine-learning method to track dissolved inorganic nitrogen pollution in a coastal watershed

Sustainable water management requires an in-depth understanding of dissolved inorganic nitrogen (DIN) in watersheds. However, it remains challenging in the context of intensifying human-induced and naturally impacted stresses in a changing environment. A spatially explicit interpretable machine-learning method was proposed based on the annual variation in land use/cover, Catchment Land Surface Model, Noah land surface model, population dynamics, and nightlight in a coastal Chinese watershed. The proposed model was effective for delineating DIN processes and sources in watersheds. The d (index of agreement), R2 (coefficient of determination), |PBIAS| (percent bias), and KGE (Kling-Gupta efficiency)during the training period were 0.91–0.98, 0.76–0.96, 0.38–3.12, and 0.73–0.87, respectively. The d, R2, |PBIAS|, and KGE during the testing period were 0.85–0.94, 0.56–0.79, 3.59–6.18, and 0.65–0.86, respectively. NH4+-N in the watersheds may be strongly related to the effects of urbanization within the watersheds while the agricultural activities may modify the patterns of NO3–-N in the watersheds. The NH4+-N was highly related to the urbanization effects, which contributed 20 %–30 % of the riverine NH4+-N in the North River. In contrast, agricultural activities may modify patterns of NO3–-N in watersheds, and agricultural activities in the West River contributed 50 %–70 % riverine NO3–-N in the watershed. Moreover, urbanization may alter the water content, soil properties, and regional climate patterns within the watersheds, while repeated DIN input through agricultural activities in the agricultural watersheds may change the nitrogen processes in the soil and groundwater. Paired with agricultural activities, dams or reservoirs may amplify NH4+-N to watersheds with modifying the advection and diffusion of DIN from agricultural activities, and increasing sediments in the watersheds. This study demonstrated the potentials of the proposed method in tracking DIN pollution and provided new insights into nutrient management in the watersheds under intensifying human-nature interactions.

Rapid flood modelling using HAND-FFA-SRC coupled approach and social media-based geodata in a coastal Chinese watershed

Flooding has catastrophic effects worldwide. Rapid flood models, such as the height above nearest drainage (HAND) model, have lower complexity and data requirements than traditional models. However, input stage height data are often lacking because most gauged sites only provide estimates of discharge. In addition, performance is difficult to evaluate because flood extent data during extreme periods are often unavailable. Here, we developed a HAND-flood frequency analysis (FFA)-synthetic rating curve (SRC) approach and applied it to a coastal watershed in China. The HAND-FFA-SRC approach demonstrated effective and efficient flood forecasting, and the C values were 1.19, 1.20, and 1.16, respectively for the floods under moderate rainfall, heavy rainfall, and storm. Meanwhile, the accuracy of the model was highly impacted by the topographic characteristics of the watersheds. The C values were improved as the slope increased from 3° to 20° during the floods under different scenarios. Additionally, the effects of floods were evaluated under different return periods which indicated that the cropland is the most affected land use type but the risk for impervious surfaces is increasing. The proposed approach is viable for forecasting flood susceptibility and can improve resilience planning and flood management.

Mathematical analyses of ecological and economic tradeoffs in irrigated agriculture based on inexact optimization principles and hierarchical crop projections

An entropy-based multi-objective interval stochastic programming (EMISP) approach based on interval hierarchical projection, interval information entropy, interval analysis and chance-constrained programming was developed for supporting agricultural water management. EMISP improved upon existing methods through measuring and balancing ecological and economic benefits of irrigated croplands under uncertainty. To demonstrate its applicability, the developed method has been applied to an arid Chinese watershed. The results suggested that grain crops would produce higher ecological benefits than economic crops. A series of benefit- and risk-explicit plans for agricultural water use were generated, which indicated that the relaxation of water availability constraints would generate higher comprehensive benefits, but increase the risks of system infeasibility. The results from the EMISP model were also compared to those from four potential alternative models. These comparisons revealed that EMISP could achieve higher water productivity and comprehensive benefits with controllable water-shortage risks.

Spatially Structured Environmental Variation Plays a Prominent Role on the Biodiversity of Freshwater Macrophytes Across China.

Different non-mutually exclusive mechanisms interactively shape large-scale diversity patterns. However, our understanding of multi-faceted diversity and their determinants in aquatic ecosystems is far from complete compared to terrestrial ones. Here, we use variation partitioning based on redundancy analysis to analyze the relative contribution of environmental and spatial variables to the patterns of phylogenetic, taxonomic, and functional diversity in macrophyte assemblages across 214 Chinese watersheds. We found extremely high spatial congruence among most aspects of biodiversity, with some important exceptions. We then used variation partitioning to estimate the proportions of variation in macrophyte biodiversity explained by environmental and spatial variables. All diversity facets were optimally explained by spatially structured environmental variables, not the pure environment effect, implying that macrophyte are taxonomically, phylogenetically, and functionally clustered in space, which might be the result of the interaction of environmental and/or evolutionary drives. We demonstrate that macrophytes might face extensive dispersal limitations across watersheds such as topography and habitat fragmentation and availability.

Nitrogen Budget and Surface Water Nitrogen Load in a Chinese Watershed

Nitrogen Budget and Surface Water Nitrogen Load in a Chinese Watershed

Risk Assessment of Nitrogen and Phosphorus Loss in a Hilly-Plain Watershed Based on the Different Hydrological Period: A Case Study in Tiaoxi Watershed

Non-point source pollution is widely considered a serious threat to drinking water. Eutrophication in Chinese watershed is mainly due to nitrogen and phosphorus output from agricultural source. Taihu Lake is a typical eutrophic lake in China, a basin representative for the study of the temporal-spatial characteristics of pollution loading of nitrogen and phosphorus to provide scientific basis for reasonable estimation and targeted control measures of nitrogen and phosphorus loss. Based on data from nitrogen and phosphorus loss in agricultural land, livestock breeding, domestic discharge and aquaculture, this study calculated the levels of nitrogen and phosphorus comprehensive loss risk for each pollution source. Using the superposition of ArcGIS raster data, we also described the spatial distribution of nitrogen and phosphorus comprehensive loss risk by the formula of comprehensive loss risk. The results showed that critical risk areas of nitrogen and phosphorus loss mainly originated from livestock breeding and agricultural land during flood period in Tiaoxi watershed. Agricultural land and livestock breeding sources formed major parts of nitrogen loss, accounting for 30.85% and 36.18%, respectively, while phosphorus loss mainly originated from livestock breeding (56.28%). During non-flood period, integrated management of livestock breeding and domestic discharge requires much attention to control nitrogen and phosphorus loss in the critical risk area. Finally, it is of great practical significance to propose spatial-temporal targeted measurements to control nitrogen and phosphorus pollution in watershed for various periods and different areas.

Assessing the environmental impacts of freshwater thermal pollution from global power generation in LCA.

Freshwater heat emissions from power plants with once-through cooling systems constitute one of many environmental pressures related to the thermoelectric power industry. The objective of this work was to obtain high resolution, operational characterization factors (CF) for the impact of heat emissions on ecosystem quality, and carry out a comprehensive, spatially, temporally and technologically differentiated damage-based environmental assessment of global freshwater thermal pollution. The aggregation of CFs on a watershed level results in 12.5% lower annual impacts globally and even smaller differences for the most crucial watersheds and months, so watershed level CFs are recommended when the exact emission site within the basin is unknown. Long-range impacts account for almost 90% of the total global impacts. The Great Lakes, several Mississippi subbasins, the Danube, and the Yangtze are among the most thermally impacted watersheds globally, receiving heat emissions from predominantly coal-fuelled and nuclear power plants. Globally, over 80% of the global annual impacts come from power plants constructed during or before the 1980s. While the impact-weighted mean age of the power plants in the Mississippi ranges from 38 to 51years, in Chinese watersheds including the Yangtze, the equivalent range is only 15 to 22years, reflecting a stark contrast in thermal pollution mitigation approaches. With relatively high shares of total capacity from power plants with once-through freshwater cooling, and tracing a large part of the Danube, 1kWh of net electricity mix is the most impactful in Hungary, Bulgaria and Serbia. Monthly CFs are provided on a grid cell level and on a watershed level for use in Life Cycle Assessment. The impacts per generating unit are also provided, as part of our effort to make available a global dataset of thermoelectric power plant emissions and impacts.

Temporal variations of runoff in a rapidly urbanizing semi-arid Chinese watershed

Rapid urbanization on streamflows may directly affect or be restricted by the sustainability of local water resources. This is particularly true for arid/semiarid areas such as the Wulanmulun River watershed in the rapidly-developing Ordos region of north central China. From 1997 to 2012, the gross domestic product (GDP) of the region increased fifty-fold, while the urban area grew by a factor of ten. This study fused multiple-source data on land use, hydrometeorology, and socioeconomics to examine temporal variations in the runoff due to climate change and urbanization. The results revealed that for the Wulanmulun River watershed, the runoff decreased consistently over the study period, with an inflection point around 2005. The average runoff from 2006 to 2012 was much smaller than that from 1997 to 2005, regardless of time scale; although the precipitation also fluctuated from 1997 to 2012, it exhibited no significant trend. From 1997–2005 to 2006–2012, both the urbanized area and GDP increased eight-fold while the population increased by 20%. Thus, urbanization rather than climate change is likely the major reason for the decrease in runoff after 2005. For the study watershed, low impact development practices (e.g. rain barrels) may need to be implemented during urbanization to achieve sustainable management of water resources.

A non-probabilistic programming approach enabling risk-aversion analysis for supporting sustainable watershed development

Due to insufficiency and ineffectiveness of monitoring facilities, variations in natural conditions and changes of human behaviors, watershed planning is interwoven with imprecise data, resulting in a multitude of complexities. A robust interval fuzzy programming approach with superiority–inferiority and risk-aversion analyses (RIFP-SIRA) was developed for identifying sustainable agricultural and industrial production strategies at the watershed scale in a highly uncertain environment. RIFP-SIRA represented a novel attempt to tackle synergies of uncertain information in the forms of intervals with non-statistical bounds and conventional intervals. The developed method was applied to a Chinese watershed. A RIFP-SIRA model was formulated and analyzed under three scenarios to investigate the effects of different policies and standards on watershed development plans. Sensitivity of the model's solutions to varied risk-aversion and input-fluctuation levels were analyzed, verifying the superiority of RIFP-SIRA over the existing inexact approaches. This research revealed that RIFP-SIRA could provide local authorities with robust, yet flexible decision plans. It could enable a quantitative analysis over the trade-offs between the economic benefits and environmental risks in the absence of probabilistic information.

Levels and distributions of polycyclic aromatic hydrocarbons (PAHs) in middle reach of Huaihe River, China: anthropogenic influences and ecological risks

Polycyclic aromatic hydrocarbons (PAHs) are a group of aromatic hydrocarbons with high toxicity to human health. PAH emissions from industrial activities have become the primary sources of PAH contamination in Chinese watersheds. Here, we analyzed 10 individual priority PAHs in 120 water samples taken from middle reach of Huaihe River, China. The results show that the PAH levels in studied watershed are significantly lower as compared to other Chinese watersheds, approaching or slightly exceeding the PAH levels in watersheds from selected European and North American countries. We observe rather large variation in spatial and vertical PAH distributions, pointing to PAH inputs from local industrial emissions, and PAH cycle among atmosphere, water and sediment. Individual PAH ratios (i.e., phenanthrene/anthracene and fluorene/pyrene) and principal components analysis suggest a primarily pyrolytic PAH sources (combustion of coal and coke) in water column. Other accompanying PAH sources include emissions from steel industry and gasoline. Total toxic benzo[a]pyrene equivalent of PAHs in studied water column indicates that PAHs in watershed of middle reach of Huaihe River pose limited toxicity to the environment.

Application of a Bayesian Watershed Model Linking Multivariate Statistical Analysis to Support Watershed-Scale Nitrogen Management in China

Excessive nitrogen loads and subsequent eutrophication risk have led to a series of critical water quality problems in Chinese watersheds. To address this issue, a modeling approach is useful for quantifying nitrogen sources, assessing source apportionment, and guiding management responses. In this study, we modeled the main hydrochemical processes of the Lian River watershed located in the south of China using the Regional Nutrient Management (ReNuMa) model, a model derived from the Generalized Watershed Loading Function (GWLF) model and incorporating Net Anthropogenic Nitrogen Inputs (NANI) to estimate runoff nitrogen concentrations. An informal Bayesian method, the Generalized Likelihood Uncertainty Estimation (GLUE) procedure, was applied for model calibration and uncertainty analysis. The resulting modeled monthly total nitrogen fluxes have high Nash-Sutcliff coefficients (>0.85) for the calibration (2005–2009) and verification (2003, 2004 and 2010) periods, representing an acceptable goodness-of-fit. The model outputs were further processed using multivariate statistical analysis to determine latent rules of nitrogen source apportionment under different circumstances, including different water regimes, seasonal patterns, and loading levels. The main nitrogen contributions in different natural and management-driven conditions have been identified, and appear to be significant for supporting decision-making priorities. We find that the ReNuMa model, with its Bayesian procedure and the linkage of subsequent multivariate statistical analysis, represents a useful approach with applicability within China and a great potential to be extended elsewhere.

Analyzing Hydrological Regime Variability and Optimizing Environmental Flow Allocation to Lake Ecosystems in a Sustainable Water Management Framework: Model Development and a Case Study for China’s Baiyangdian Watershed

AbstractThis study analyzed the variability of the hydrological regime in a Chinese watershed and in the available water resources and used this data to develop an optimal environmental flow allocation model that lets decision-makers work towards sustainable management of the watershed’s water resources. First, historical hydrological data was analyzed to determine its statistical parameters, flow duration characteristics, and trends based on data from two gauging stations (at the West Dayang and Wangkuai reservoirs) from 1956 to 2000 and data for Baiyangdian Lake from 1950 to 2005. The monthly and annual inflows and lake water levels were found to be highly variable. The optimization model that was developed for environmental flow allocation to the lake seeks a rational balance of providing appropriate environmental water to the lake, maximizing economic returns for humans, and minimizing agricultural yield losses. The appropriate quantity of environmental water was defined by minimizing the difference b...

Influence of Agricultural Activity on Nitrogen Budget in Chinese and Japanese Watersheds

AbstractTo analyze the effect of agricultural activity on nitrogen (N) budget at the watershed scale, a comparative study was conducted at two Japanese watersheds, the Shibetsu River watershed (SRW) and Upper-Naka River watershed (UNRW), and one Chinese watershed, the Jurong Reservoir watershed (JRW). The total area and the proportion of agricultural area (in parentheses) of the watersheds were 685 (51%), 1 299 (21%), and 46 km2 (55%) for SRW, UNRW, and JRW, respectively. The main agricultural land use in SRW was forage grassland, while paddy fields occupied the highest proportion of cropland in UNRW (11% of total area) and JRW (31% of total area). The farmland surplus N was 61, 48, and 205 kg N ha−1 year−1 for SRW, UNRW, and JRW, respectively. The total input and output for the whole watershed were 89 and 76, 83 and 61, and 353 and 176 kg N ha−1 year−1 for SRW, UNRW, and JRW, respectively. The proportion of discharged N to net anthropogenic N input was 31%, 37%, and 1.7% for SRW, UNRW, and JRW, respectively. The two watersheds in Japan showed similar proportions of discharged N to those of previous reports, while the watershed in China (JRW) showed a totally different characteristic compared to previous studies. The high N input in JRW did not increase the amount of discharged N at the outlet of the watershed due to high proportions of paddy fields and water bodies, which was an underestimated N sink at the landscape scale.

Programming a spatial water model for improving water efficiency in China

This paper deals with heavy pressures to save water in natural resource management due to increased water scarcity. We take the example of a Chinese watershed and investigate the water use efficiency in Chinese irrigated agriculture through a field study as dependent on investments in on-farm water saving technologies and canal water conveyance. The economic and environmental impacts of farmers adopting modern irrigation technologies and public sectors improving water transit systems are studied in particular. A spatial mathematical programming model is employed for optimization. The emphasis is on private and public investments in water saving. Water saving in a canal system enables an extension of the system and provides food and income for more farmers. The model results are of great value for policy makers and project managers, who want to optimize irrigation projects, because scenarios (for instance water and product pricing) are provided. We also provide references for farmers in applying suitable irrigation technologies.

Application of a Developed Grid-Xinanjiang Model to Chinese Watersheds for Flood Forecasting Purpose

A distributed rainfall-runoff model, Grid-Xinanjiang model based on the topographical information of each grid cell extracted from the digital elevation model (DEM), was developed to simulate the hydrologic processes within watersheds. The water exchange among grids within the watershed and the runoff routing along the river drainage networks are taken into consideration in the model. The Grid-Xinanjiang model was applied to the two watersheds in China for flood simulations. The performance of the developed model was compared with that of the original Xinanjiang model. Initial tests show that the Grid-Xinanjiang model can perform well as the original model in terms of estimates of streamflow and of the model efficiency coefficient. All of the qualified ratios relative to peak flow, runoff, and peak time for the two models applied to the Misai watershed and the Xixian watershed are more than 88%. The Grid-Xinanjiang model outperformed the original model in predicting flood runoff and outlet hydrographs, es...

Dry and Wet Periods in Eastern China Watersheds:Patterns and Predictability

降水观测记录是近50a来中国九大流域干旱和湿润期时空变化分析的基础.以两年为时间尺度引入标准降水指数(SPI)来分析评价气候变化情景,结果表明黄河流域、长江流域和淮河(或珠江)流域等三个主要的区域表现为低频率变化指数:另外分析显示北方地区自20世纪70年代以来干旱更频繁的出现,在SPI时间序列中呈现负趋势变化.这也许与表征SPI信号的长周期有关(24a和48a周期).与这些长周期一起,也有一些其它方面的因素有助于频谱变化,范围从3a到9a不等.在指数时间序列中这些周期成分的存在为长期预测干旱和湿润期提供了很好的条件.;Rain gauge observations are the basis for an analysis of the time-space variability of dry and wet periods during the last fifty years in nine Chinese watersheds. The Standardized Precipitation Index (SPI) is introduced to assess the climatic conditions on a biennial time-scale. To capture the spatial pattern of co-variability principal component analysis (PCA) is applied to the watersheds SPI time series. Results suggest that the index low-frequency variability for watersheds is well described by that of three main areas:regions near the Yellow river, regions nearby the Yangtze River and Huaihe or Zhujiang Basin. Furthermore, the analysis shows that the northern basins from the seventies is experiencing dry conditions more frequently, which is due to the presence of a negative trend in the SPI time series. It is perhaps related to long-term periodicities that characterise the SPI signal (say 24 and 48 year). Together with these long periods there are other ones contributing to the power spectrum variance ranging from 3 up to 9 year. These periodic components in the index time series provide good chances for the long-term predictability of dryness and wetness.