Hai River Research Articles

A revised model for calculating virtual scarce water

Accounting for virtual scarce water facilitates and improves the analysis of virtual water, and facilitates comparisons between water use embodied in products from regions with different water resource conditions. However, previous virtual scarce water models were inadequate for the analysis of both water quantity and quality, and only provided a rough estimate. To address this problem, we proposed a modification of this model. In the revised model, we determined the scarcity induced by inadequate water quality by analyzing the availability of suitable quality water for specific sectors (residential, industrial, agricultural, and environmental) rather than focusing on a single sector (usually environmental). Additionally, the revised model evaluated scarcity from water quantity by considering clean water technologies and environmental flow demand. This comprehensive scarcity measure is then incorporated into an input–output model to determine virtual scarce water. We used two typical Chinese river basins (the Hai River and Pearl River basins) with different water endowments to evaluate the model. We found that in the water-scarcity region the virtual scarce water was more strongly affected by water quality and clean water technologies, whereas in the water-rich region it was more strongly affected by the environmental flow demand. These findings underscored the need for more targeted economic and environmental policies for different regions. Water-scarcity regions should prioritize the promotion of clean water technologies and contaminant mitigation techniques, while water-rich regions should pay more attention to ensuring the environmental flow demand.

Mechanically and accurately calculate river width in vegetation areas by coupling Sentinel-1 and -2 imageries within land-water-mixed pixels

Accurately measuring river width has been one of greatest challenges due to the presence of mixed land–water pixels intersecting river boundaries. Therefore, this study proposed a novel mechanical method (RW-vebasud), instead of traditionally empirical models, to estimate river width within a pixel in vegetation areas based on time series analysis of Sentinel-1 and Sentinel-2 spaceborne multispectral images. We initially explored the mechanism of variation in backscatter intensity (σ) with enhanced vegetation index (EVI) whereby we successfully removed noises in σ–EVI relationship resulted from vegetation growth. Then, for the first time a smooth functional relationship between water area proportion and backscatter intensity within a ROI (or region of interest) was constructed. Consequently, subpixel water–land separation based on the mechanism process was realized. The novel method could not only work at large-scaled rivers (satellite-visible) but perform well at small-scaled rivers within a water-land mixed pixel (satellite-invisible). A total of 197 measurements for river widths in China during 2016 ∼ 2021 were used for model verification, demonstrating a positive correlation between EVI and σ, with R2 ranging from 0.16 to 0.69 (P<0.05). The RW-vebasud exhibited superior accuracy in calculating river width compared to the widely used MNDWI (modified normalized difference water index). The Root Mean Square Error (RMSE) decreased by 4.32 ∼ 6.65 m when the river width was less than 90 m and by 66.12 % when it exceeded 90 m, compared to MNDWI. Remarkably, RW-vebasud maintains satisfactorily high accuracy (the Nash-Sutcliffe efficiency coefficient: NSE=0.70 and RMSE=3.19) even at the spatial scale less than 3 times the image resolution, breaking the internationally accepted limit that river width extraction can only be accurate when the river width is greater than 3 times the satellite resolution. Moreover, the accuracy of this method is better than that with the currently well-known global river width datasets GRWL and MERIT Hydro. For the RW-Vebasud/GRWL/MERIT Hydro datasets, the NSE=0.99 /0.93/0.87, the RMSE=5.99/42.33/54.27, and the R2 = 0.99/0.91/0.74, respectively. The application of RW-vebasud in China shows that river widths in wet and dry seasons exhibited an increasing trend over the previous six years (2016–2021), as global warming accelerated glacier melting and increased rainfall quantity, with an average growth rate of 2.26 m/year (wet, P<0.05) and 2.17 m/year (dry, P<0.05), respectively. In response to the summer/winter Asian monsoons, most rivers widen in summer. The largest river width occurs in the Yellow River Basin (YLRB, 155.28 m on average), while the smallest occurs in the Hai River Basin (HARB, 22.99 m on average). The method proposed in this study can provide efficient techniques for surface river-width reconstruction which can greatly facilitate global resource and environmental modelling.

The politics of heritage in a river-city: imperial, hyper-colonial, and globalising Tianjin

The intent of this article is to analyse the interconnectedness between urban transformation and eco-heritage value over time in Tianjin from a river-city perspective. The focus is on the Hai River’s (海河) contribution to the mechanisms of space and power in imperial, hyper-colonial, and globalising Tianjin. After an analytical excursus of the Haihe’s historical-political-economic roles, attention is given to the Haihe as the fulcrum of Tianjin's creation as a spectacle city in present times. The objectives are to elucidate the Tianjin Municipal Government-led urban ‘beautification’ strategy and analyse the aims and objectives of the 2002 ‘Comprehensive Reconstruction and Redevelopment Plan of the Haihe’s Riversides’ while also considering the actual experience of this transformation. The premise of this article is that the Haihe River has helped determine Tianjin’s politics of design via heritagisation: the historical processes through which cultural heritage is adapted to strategically promote favourable imagery of the river-city for political management purposes.

Long-Term Variation Patterns of Precipitations Driven by Climate Change in China from 1901 to 2022

Studying long-term precipitation trends is crucial for sustainable development, as the proper utilization of water resources is essential for maintaining a sustainable water supply. The objective and novelty of this paper was to reveal the gradual mutation process of precipitation in China over a century. This study utilized monthly precipitation data from 1901 to 2022 (at a century scale) to analyze and explore the spatiotemporal variability in precipitation across different time scales and regions with a trend analysis, an abrupt change analysis, and gravity center models. The findings indicate that (1) from 1901 to 2022, the precipitation in China generally decreased from the southeast coastal areas toward the northwest inland regions. (2) There were significant differences in the migration of precipitation gravity centers among the different study regions, with the least dispersion being observed in the Liao River basin, while the Hai River basin, various river basins in the northwest, and the Pearl River basin exhibited certain regularities in gravity center movement, and other regions showed periodic variations. (3) Over the period from 1901 to 2022, there was a trend of transitioning from lower to higher precipitation levels. (4) According to continuous long-term abrupt change tests, the timing of precipitation shifts varied across different basins. Precipitation, as a crucial component of natural resources, directly impacts various aspects of socio-economic life. Research findings provide decision support for regional flood control and disaster reduction and offer scientific decisions for ecological security.

Assessing a machine learning-based downscaling framework for obtaining 1km daily precipitation from GPM data

Hydro-meteorological monitoring through satellites in arid and semi-arid regions is constrained by the coarse spatial resolution of precipitation data, which impedes detailed analyses. The objective of this study is to evaluate various machine learning techniques for developing a downscaling framework that generates high spatio-temporal resolution precipitation products. Focusing on the Hai River Basin, we evaluated three machine learning approaches—Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagation (BP) neural networks. These methods integrate environmental variables including land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Precipitable Water Vapor (PWV), and albedo, to downscale the 0.1° spatial resolution Global Precipitation Measurement (GPM) product to a 1 km resolution. We further refined the results with residual correction and calibration using terrestrial rain gauge data. Subsequently, utilizing the 1 km annual precipitation, we employed the moving average window method to derive monthly and daily precipitation. The results demonstrated that the XGBoost method, calibrated with Geographical Difference Analysis (GDA) and Kriging spatial interpolation, proved to be the most accurate, achieving a Mean Absolute Error (MAE) of 58.40 mm for the annual product, representing a 14 % improvement over the original data. The monthly and daily products achieved MAE values of 11.61 mm and 1.79 mm, respectively, thus enhancing spatial resolution while maintaining accuracy comparable to the original product. In the Hai River Basin, key factors including longitude, latitude, DEM, LST_night, and PWV demonstrated greater importance and stability than other factors, thereby enhancing the model's precipitation prediction capabilities. This study provides a comprehensive assessment of the annual, monthly, and daily high-temporal and high-spatial resolution downscaling processes of precipitation, serving as an important reference for hydrology and related fields.

A Study on the Differences in Vegetation Phenological Characteristics and Their Effects on Water–Carbon Coupling in the Huang-Huai-Hai and Yangtze River Basins, China

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m2, respectively, while WUE decreased by 0.002–0.008 gC/kgH2O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency.

Analysis of changes in water quality and treatment effectiveness of seven major river basins in China from 2001 to 2020

The seven major river basins (the Yangtze River, the Yellow River, the Pearl River, the Songhua River, the Huai River, the Hai River and the Liao River) are the most important surface water resources in China, but there is a lack of quantitative analyses of water quality change trends, horizontal comparisons of governance effects, and systematic review of effective policies since the 21st century. Based on the water resources bulletin and environmental status bulletin issued by government departments, the changes in water quality, pollutant indicators and treatment effectiveness of seven major basins from 2001 to 2020 have been scientifically analyzed using mathematical and statistical methods. (1) Over the period 2001 to 2020, the overall water quality in the seven major river basins exhibited a gradual improvement. Different basins demonstrated varied growth values for Grade I-III water, reduction values for Grade IV-V, and inferior Grade V water. (2) Between 2001 and 2020, changes in sewage discharge volume and types led to adjustments in the main pollutant indicators of the seven basins. (3) The ranking of the pollution degree in the seven major basins exhibited dynamic changes but also remained relatively stable during specific periods or years. (4) Assessing the average annual growth rate of Grade I-III water and the average annual reduction rate of Grade IV-V and inferior Grade V water, the Huai River basin demonstrated the most outstanding governance effectiveness, while the Liao River basin, the Yellow River basin, and the Songhua River basin also achieved notable treatment results. (5) The improvement in water quality across the seven major river basins can be attributed to scientific planning, enhanced policies and regulations, surge in investment in water conservancy infrastructure, heightened environmental protection awareness, application of green production technology. To sum up, the research findings not only provide a scientific foundation for the governance and protection of the seven major basins but also offer a valuable reference for other developing countries to strike a balance between economic development and environmental protection.

Effect of water vapor transport and budget on precipitation in the Yangtze–Huang–Huai–Hai River Basin

Study regionThe Yangtze−Huang−Huai−Hai River basin (3.19 million km2) in China, which is vital for China's economic development. Study focusWe employed the Eulerian method to identify the primary water vapor channels from 2005 to 2020, and the water vapor net budget over the basin was calculated. Additionally, the Lagrangian method was utilized to simulate water vapor transport trajectories across four seasons, with the aim of investigating the influence of changes in water vapor on precipitation. New hydrological insights for the regionUtilizing both Eulerian and Lagrangian methods, the findings indicate that: (1) Precipitation in the basin experienced a downward trend of −3.5 mm·a−1 during 2005−2020, mainly in spring (−1.4 mm·a−1) and summer (−1.7 mm·a−1); (2) The water vapor net budget has a substantial downward trend (−0.9 kg·m−1·s−1·a−1), mainly in spring (−0.4 kg·m−1·s−1·a−1) and summer (−0.3 kg·m−1·s−1·a−1), which aligns with the declining trend of precipitation. (3) The Hybrid Single Particle Lagrangian Integrated Trajectory Model demonstrates that the decrease in long−distance water vapor transport trajectories impacts the basin’s water vapor channels. The decline in spring is predominantly influenced by the water vapor transport trajectories from Asia, Europe, and Africa−Arctic Ocean (−0.7 %·a−1), while the summer is mainly driven by the water vapor transport trajectory from the Indian Ocean−South China Sea (−0.3 %·a−1). This study improves the existing understanding of the hydrological cycle in the context of the basin, and offers a crucial scientific basis for water resource management and regulation.

Surface water resource attenuation attribution and patterns in Hai River Basin

Surface water resource attenuation attribution and patterns in Hai River Basin

A dataset of water vapor-heat-carbon fluxes and meteorological observations in croplands of Hai River Basin from 2013 to 2020

The present study area was located in the cropland ecosystem near Guanting Reservoir in Haihe River Basin. We conducted long-term observations of the multi-scale fluxes and meteorological elements, using various observation systems, including lysimeter, eddy covariance, large aperture scintillometer and automatic weather station/gradient of meteorological elements. This dataset consists of multi-scale fluxes (meter, hundred-meter, and kilometer-meter) and meteorological elements data. Observation items include ecosystem net carbon exchange, latent heat flux/evapotranspiration, sensible heat flux, air temperature, relative humidity, wind speed, wind direction, downward/upward shortwave radiation, downward/upward longwave radiation, net radiation, atmospheric pressure, precipitation, infrared radiation temperature, photosynthetic effective radiation, soil temperature, soil moisture, soil heat flux, and average soil temperature, etc. This dataset is processed carefully and can be used to study the effects of reservoir expansion and shrinkage on the circulation of carbon and water or other substances in the surrounding ecosystem. Additionally, it can serve as a powerful data basis for the research on relevant remote sensing estimation and model simulation.

Dominant Drivers for Terrestrial Water Storage Changes Are Different in Northern and Southern China

AbstractSubsurface water storage is a key component in the water cycle, and China is facing severe issues with water resource shortage. This study estimates the terrestrial water storage (TWS) changes from 2003 to 2017 using a water balance approach over seven large basins in China. The estimated inter‐annual variations and trends in TWS correspond well with those obtained from the Gravity Recovery and Climate Experiment observation. Variance analysis shows that annual precipitation in northern China plays a more important role in controlling annual TWS variability than that in southern China. This increased sensitivity is primarily attributed to the limited availability of water resources and the arid climate conditions prevalent in northern China. Interestingly, actual evapotranspiration is the dominant driver, contributing more than 55% to the declining trend in TWS in two northern basins: Hai River and Yellow River basins, where the leaf area index has strongly increased. Precipitation and evapotranspiration account for over 89% of the TWS trend over basins in northeastern China. Meanwhile, the dominant drivers for TWS in southern China are precipitation (50%–54%) and runoff (33%–41%). Our results suggest that it requires strong ecological conservation actions in order to keep water resources sustainable in northern China.

Pleistocene Landscape Dynamics Drives Lineage Divergence of a Temperate Freshwater Fish Gobio rivuloides in Coastal Drainages of Northern China.

Understanding historical processes underlying lineage distribution patterns is a primary goal of phylogeography. We selected Gobio rivuloides (Cypriniformes: Gobionidae) as a model to improve our knowledge about how intraspecific genetic divergence of freshwater fishes arises in coastal drainages of northern China via statistical analysis using cytochrome b gene. The time-calibrated phylogeny of G. rivuloides showed the divergence of two major lineages (I and II) at ~0.98 Ma (million years ago). Lineage I can be divided into two sub-lineages (I-A and I-B) with a divergence time of ~0.83 Ma. Sub-lineage I-A inhabits the Amur River, and sub-lineage I-B lives in the Luan River and Liao River. Lineage II is distributed in the Yellow River and Hai River, with close genetic relationships between the two drainages, and can be split into two sub-lineages (II-C and II-D) with a divergence time of ~0.60 Ma. Our findings indicate that the splitting of lineages and sub-lineages could be attributed to geographic isolation caused by the formation of the Bohai Sea, river capture, and the episodic hydrologic closing of a paleolake during the late Lower-Middle Pleistocene. It is also the first report we know of displaying a clear phylogeographic break for freshwater fishes across coastal drainages in northern China.

An often-overestimated ecological risk of copper in Chinese surface water: bioavailable fraction determined by multiple linear regression of water quality parameters

BackgroundRisks of adverse ecological effects of copper (Cu) consider of water quality parameters were not fully understood in China. Here, a national-scale exposure of Cu in Chinese surface water was investigated, and the first report using multiple linear regression approach to predict and correct toxicity data based on water chemistries in China. Risk of Cu was overestimated without considering water quality parameters in the previous studies.ResultsUnder prevalent water quality conditions of hardness = 150.0 mg/L, pH = 7.8, and dissolved organic carbon (DOC) = 3.0 mg/L, across China, the predicted no effect concentration for total, dissolved Cu was 9.71 μg/L. Based on results of the preliminary risk quotients method, 1.19% (a total of 43 in 3610 sites) were classified as “high risk”, only one sixth of the percentage of sites with “high risk” than the proportion predicted when not considering water quality parameters, which was 7.51%. Similar results were obtained by application of both the margin of safety method (0.71% compared to 2.81%) and joint probability curve method (3.34% compared to 16.29%), both of which overestimated risks posed by Cu to aquatic organisms in China.ConclusionAfter correcting for bioavailability based on water quality parameters, consider both concentrations and frequencies during ecological risk assessment, regions of China at greatest risk from adverse effects of Cu were the Hai River (Haihe), Huai Rivers (Huaihe) and Chao Lake. These findings provide a comprehensive method for a more accurate assessment of risks of adverse effects of Cu to aquatic life in surface waters.

Significant Reduction in Precipitation Seasonality and the Association with Extreme Precipitation in the Hai River Basin of China from 1960 to 2018

The Hai River Basin (HRB) serves as a vital center for the population, economy and politics in northern China. Natural hazards, particularly floods, pose significant risks to the region, often attributed to extreme precipitation events. Changes in precipitation seasonalitycc play a pivotal role in influencing precipitation extreme events. Therefore, this study presents a comprehensive analysis of precipitation seasonality and its impact on precipitation extremes in HRB. By implementing a novel relative entropy method, we calculated the precipitation seasonality indicators using daily precipitation observations from 1960 to 2018 in HRB. We found a significant decreasing trend in precipitation seasonality (−0.03 decade−1, p = 0.04), accompanied by an earlier onset date (4.0 days decade−1, p = 0.01) and longer duration (4.3 days decade−1, p = 0.03) of the wet season. Notably, these trends are notably concentrated in the Beijing-Tianjin administrative regions. Additionally, a lower precipitation seasonality value indicated a more evenly distributed precipitation throughout the year, resulting in reduced occurrences of precipitation extremes. Consistently, we observed two precipitation extremes, extreme wet day precipitation R99T and maximum 1-day precipitation RX1Day, which exhibited significant decreasing trends at the rate of −0.5 mm decade−1 (p = 0.02) and −1.4 mm decade−1 (p = 0.05), respectively. Furthermore, we detected significant positive correlations of 0.31 (p = 0.02) and 0.35 (p = 0.01) between precipitation seasonality and precipitation extremes (R95T and R99T), suggesting that a more evenly distributed precipitation across seasons corresponds to fewer precipitation extremes over the past sixty years. Metropolitan areas, in particular, experienced a noteworthy reduction in precipitation seasonality and a decreased frequency of precipitation extreme events. The findings of this study shed new light on the intricate relationship between precipitation seasonality and extreme events, further helping policy making develop effective risk regulations for agriculture, floods, and urban waterlogging, ensuring sustainable development within the HRB.

A framework for estimating actual evapotranspiration through spatial heterogeneity-based machine learning approaches

Actual evapotranspiration (ET) is a key variable controlling the exchange of energy and water in terrestrial ecosystems. The spatial heterogeneity of ET patterns poses a great challenge for regional ET estimation over heterogeneous landscapes. In this study, we proposed a framework for estimating ET through interactive detector for spatial associations (IDSA)-based machine learning (ML) approaches by combining data from remote sensing and four flux towers distributed in the Hai River Basin. The IDSA model was applied to explore individual and interactive determinants of ET over the Hai River Basin. In addition, the geographical regions of ET were determined according to the spatial heterogeneity. Then we simulated ET in each geographical region separately using three ML models, including random forest, gradient boosting decision tree, and Cubist. General ML models without considering spatial heterogeneity were used for comparison, and evaluations were conducted with the eddy covariance flux tower observations at the site scale, also with water balance ET at the basin scale. The results demonstrated that the spatial patterns of ET were difficult to be explained by individual environmental variables (64.9 %). The maximum interpretability was improved by about 11.2 % through the interaction of air temperature (T) and normalized different vegetation index (NDVI). The framework developed in this study had excellent performance with coefficient of determination (R2) ranging from 0.807 to 0.811, root mean square error (RMSE) ranging from 0.654 mm/day to 0.661 mm/day, and mean absolute error (MAE) ranging from 0.470 mm/day to 0.485 mm/day. It was superior to general ML models at the site and basin scales. Additionally, the ET simulated by IDSA-based ML models were in good agreement with the reference product, further illustrating its reliability. The proposed framework provides a theoretical basis for in-depth understanding of spatial heterogeneity of ET and a new perspective for ET prediction over large scales.

Occurrence, spatiotemporal distribution patterns,partitioning and risk assessments of multiple pesticide residues in typical estuarine water environments in eastern China

The low terrain and the prosperous agriculture in the east of China, have caused the accumulation of pesticide residues in the estuaries. Therefore, this study analyzed the spatiotemporal distribution and partition tendency of 106 pesticides based on their abundance, frequencies, and concentrations in the aquatic environment of 16 river estuaries in 7 major basins in the eastern China by using solid-phase extraction (SPE) with high-performance liquid chromatography tandem mass spectrometry (HPLC‒MS/MS) and gas chromatography tandem mass spectrometry (GC‒MS/MS). In addition, potential risk of multiple pesticides was also evaluated. The results showed that herbicides were the dominant pesticide type, while triazines were the predominate substance group of pesticide. In addition, triadimenol, vinclozolin, diethylatrazine, prometryn, thiamethoxam, atrazine, and metalachlor were the major pesticides in the water, while prometryn, metalachlor, and atrazine were the main pesticides in the sediment. The average total concentration of pesticide was 751.15 ng/L in the dry season, 651.17 ng/L in the wet season, and 617.37 ng/L in the normal season, respectively. The estuaries of the Huai River Basin, the Yangtze River Basin, the Hai River Basin, and the Yellow River Basin have been affected by the low pollution treatment efficiency, weak infrastructure, and agricultural/non-agricultural activities in eastern China, resulting in relatively serious pesticide pollution. The estuaries of Huaihe River, Yangtze River, Xiaoqing River, and Luanhe River had large pesticide abundance and comparatively severe pesticide pollution, while the estuaries of Tuhai River and Haihe River had heavy pesticide contamination in the sediment, which might be induced by historical sedimentary factors. The log KOC values showed that except for thioketone, other pesticides were relatively stable due to the adsorption by sediment. The ecological risk assessment results indicated that insecticides had a high risk. Teenagers were the most severely affected by the noncarcinogenic risk of pesticides, while adults were mostly affected by the carcinogenic risk of pesticides. Therefore, pesticide hazards in the water environment of estuaries in eastern China needs to be further close supervision.

Assessing environmental, economic, and social impacts of inter-basin water transfer in China

Inter-basin water transfer (IBWT) has been widely implemented to address water deficit problems in many parts of the world. IBWT projects can pose various environmental, economic, and social impacts on both water recipient and source basins. This study presents an analysis of multi-dimensional impacts of China’s IBWT at the sub-basin scale, using three newly developed indicators. The analysis is based on China’s operating IBWT projects in 2016, which had a collective capacity of transferring ∼48 billion m3/yr water across sub-basins. Results indicate that IBWT helped improve environmental flow conditions in 20 out of 26 recipient sub-basins, with 6 out of 17 source sub-basins experiencing critical environmental flow conditions. IBWT generated net economic benefits of approximately 4.2 billion US dollars and net social benefits of providing domestic water use for 170 million people. IBWT posed overall positive environmental, economic, and social impacts, with the most benefits delivered to the North China Plain (i.e., the Huai and Hai River Basins). However, a few water source basins with relatively low local water availability were faced with a high risk of low environmental flows and high socio-economic costs, especially under low water resources conditions. Large spatial variations of IBWT impacts were found across sub-basins, which cannot be known by assessment at the major basin level. This highlights the need to assess IBWT impacts at finer spatial scales, considering regional differences in local water resources and social-economic development conditions. This study for the first time reveals multi-dimensional impacts of IBWT in China at the sub-basin scale. The results can be used by policymakers to inform water transfer and water management policies.

Tracing surface water pollution in China’s supply chain

Decades of economic growth in China were enabled by rapid industrialization with insufficient water quality controls. Previous studies have traced water pollution discharges or grey water footprint using the multi-regional input–output (MRIO) model. However, there is a research gap in understanding the relation between surface water pollutant concentrations and final consumption of local and external basins. Here, we present the first national analysis to map surface water quality degradation in watersheds embedded in China’s supply chains. To do this, we developed a basin-specific relationship between surface water pollution concentration and discharge, and combined it with the MRIO model to trace the water pollution of different basins through the trade of products and services. We find that ∼50% chemical oxygen demand (COD) and ∼46% ammonium nitrogen (NH4+-N) discharges from production processes can be traced to consumer demands beyond the basin where the pollution was initially released. 0.3–2.2 mg/L COD and 0.03–0.31 mg/L NH4+-N water quality degradation (the range indicates pollution concentration in different basins) can be attributed to final consumption of commodities from other basins in China. International consumers contributed to increased degradation of water quality (0.43 mg/L COD in Huai River Basin and 0.07 mg/L NH4+-N in Hai River Basin). High pollution concentrations were often concentrated in dry North China, because water scarce basins in this region are more susceptible to human pollution loadings. Basins outsourcing water pollution were mainly developed economies that outsourced production and subsequent water quality impairments to other basins. This study highlights the interactions between water quality and supply chains.

Impacts of climate change and anthropogenic stressors on runoff variations in major river basins in China since 1950

Runoff is one of the main components of hydrological cycle and an important index for water resources evaluation, understanding the runoff change and their causes is vital to water resource management. In the study, we analyzed the runoff change and the impacts of climate change and land use alteration on runoff variation based on natural runoff and previous studies in China. The results showed that there was a significant increasing trend in the annual runoff during 1961–2018 (p < 0.05), with change rate of 0.4 mm/a and abrupt point at 1999 across China, climate change dominated the runoff variation with a contribution of 54 %. In previous studies, the runoff of the major basins in China had a downward trend on the whole (−0.99 mm/a) except Continental River Basin (CRB) showed an increasing trend (0.25 mm/a), the abrupt points were mainly concentrated in 1991–2000, and human activity was the leading factor of runoff change with the contribution of 54 % across China. Human activity was the dominant factor of runoff change in Songhua and Liao River Basin (SLRB), Yellow River Basin (YRB), Hai River Basin (HRB) and Pearl River Basin (PRB), the contribution was >56 %, while climate change was the dominant factor of runoff change in Huai River Basin (HuRB), CRB, and Yangtze River Basin (YZRB). Overall, there was a significant correlation between runoff and precipitation, unused land, urban and grassland in China. We concluded that runoff change and the contribution of climate change and human activities varies greatly among different basins. The findings in this work can shed light on the quantitative understanding of runoff changes in national scale and offer a scientific basis for sustainable water management.

Assessing Transboundary Impacts of Energy-Driven Water Footprint on Scarce Water Resources in China: Catchments under Stress and Mitigation Options.

The energy supply chains operating beyond a region's jurisdiction can exert pressure on the availability of water resources in the local area. In China, however, there is a lack of transboundary assessments that investigate the effects of energy consumption on water stress within and across river basins. In this study, we therefore investigate transboundary impacts on scarce water resources that are induced by energy demands (i.e., electricity, petroleum, coal mining, oil and gas extraction, and gas production). We develop a bottom-up high spatial resolution water inventory and link it to a 2017 multiregional input-output (MRIO) table of China to analyze supply chain scarce water use at provincial and river basin levels. We find that the energy-driven water footprint accounts for 21.6% of national water usage, of which 35.7% is scarce water. Nonelectric power energy sectors contribute to around half of the nation's scarce water transfer. We identify three sets of catchments whose water resources are stressed by energy demand, i.e., (a) from the northern Hai River Basin to the eastern part of the Yellow River Basin and the Huai River Basin, (b) the northern area of the Northwest Rivers, and (c) the developed coastal city clusters in the Yangtze River Basin and the Pearl River Basin. We then evaluate the impacts of eight mitigation options, which may potentially shift around half of the moderate- or high-stress areas in the Hai River Basin and the Northwest Rivers to low to moderate (or even low) stress. We highlight the need for transboundary collaboration to sustain water-constrained energy demand and to develop targeted measures to mitigate stress on water resources within a river basin.