River eutrophication is difficult to diagnose and estimate quantitatively because of its complex degradation mechanism in large river systems. Conventional monitoring and modeling methods are limited to accurately revealing the evolution process and trends of river aquatic organisms. In the present study, based on HJ-1A/1B CCD sensor, combined with genetic algorithm (GA) and regression tree (GART), a remote sensing inversion prediction model was established; the model can estimate algal blooms in the Han River affected by China's Middle Route of the South-to-North Water Diversion Project (SNWTP). During the outbreak of algal blooms, the near-infrared band reflectance evidently increased between 2009 and 2015, with increasing algal density. The algal density in the downstream of the Han River has a nearly synchronous positive change with the reflectance in the B4 (near-infrared) band and a nearly synchronous reverse change with the B1 (blue) band. B1 and B4 screened by GA reduced redundancy by 14%, leading to a good prediction performance (R-2 = 0.88). According to GART and partial dependence analysis, the B4 band is a crucial characterization factor of algal blooms in the Han River. When the remote sensing band was in the range of B1 >= 0.085 and B4 <= 0.101, the algal density was lower than 0.15 x 10(7) cells l(-1), indicating no algal bloom in the downstream of the Han River. When B4 was >0.103 and B1 <= 0.076, algal density was higher than 1 x 107 cells l-1 and algal blooms were very likely to occur. These findings could provide a scientific reference for diagnosing and predicting large-scale water ecological degradation in similar watersheds.

The evolution of riverine aquatic ecosystems typically exhibits notable characteristic with cumulative, enduring, and hysteresis. Exploring the non-linear response of riverine ecology to long-term hydrological fluctuations become a major challenge in contemporary interdisciplinary research. In response to the critical issue of frequent river algal blooms in the lower Han River, which is impacted by Asian largest inter-basin water diversion project. We identified the non-linear response of eco-hydrology across various time scales through the integration of Continuous Wavelet Transform (CWT) and Inverse Wavelet Transform (IWT). Our study revealed that: 1) Over the past half century, the hydrological regime in the lower Han river showed a significant downward trend, and existed three significant hydrological oscillation periods (HOPs), including the short-scale Intra-AC (180 days), the medium-scale AC (365 days, the first major period), and the long-scale Inter-AC (2500 days), the variation of Inter-AC changed most dramatically. 2) We further found that the Inter-AC variation of hydrology is more closely related to the formation of river algal blooms in the Han River, and when the hydrological Inter-AC shows steady state or downward trend, the frequency of algal blooms in the lower Han River increases significantly. 3) The river algal blooms in the lower Han River is a cumulative consequence to the long-term hydrological influences. Weakened hydrological Inter-AC is more likely to increase the frequency of river algal blooms, and 10-years Inter-AC cumulation increased the frequency by 60%. Therefore, the weaken of long-scale HOP will significantly increase the frequency of river algal blooms in the future. This study received a critical scientific insight and aimed at provide guidance for the optimization of ecological management within the framework of national large-scale water conservation.

Increasingly intensive crop farming has been aggravating global water pollution. It is significant to retrospectively investigate world's crop production induced water pollution stress, facilitating sustainable agriculture in the future. In this study, nearly 60-yr grey water footprint (GWF) dynamics of global crop farming are examined at multi-scales covering 146 crops in 162 countries, based on a framework coupling GWF assessment with an advanced dynamic decomposition analysis (DDA). Results show that global crop GWF has increased by 1224.1 % in 1961-2018, presenting three stages of GWF growth: an accelerated period of 1961-1980 (P1), a linear period of 1980-1999 (P2), and a decelerated period of 1999-2018 (P3). The spatial and crop-wise patterns of GWF vary for the stages. Crop GWF generally shifts from developed (North America and Europe) to developing (Asia and South America) regions during P1-P2, with crop-wise GWF showing increased proportions in cash crops (Fruits, Vegetables, Oil crops, etc.) for P2-P3 and decreased in food crops (mainly Cereals). China, America, and India exhibit top three national crop GWF amounts and increases (in total 60.3 % of world's GWF growth). Overall, production efficiency and population scale are determinants of growing global crop GWF, with different drivers recognized for the stages. Fertilization level principally drives up world's crop GWF for P1 (50.7 %) while offsets GWF growth in P3 (-8.61 %). Proportion of rural population becomes the main contributor offsetting crop GWF growth especially in P3. Driving pattern of crop GWF evolution varies among continents, countries, and crop categories, as well. Based on these, we recommend optimizing regional/national crop activities expecting to enhance sustainability of global agriculture.

The Yangtze River Economic Belt (YREB), characterized by its dense population and dynamic industrial activities, is facing unprecedented challenges in balancing water resource utilization with environmental protection. Understanding the interplay between water environment and economic development is crucial for the YREB. In this study, the dynamics of sustainable state and driving mechanism for water utilization examined from a Water Footprint (WF) perspective at regional and sectoral scales from 2002 to 2017. The analysis is facilitated by a novel framework that integrates WF sustainability and equilibrium assessment for water pollution and scarcity stresses with structural decomposition analysis (SDA), based on multiregional input-output (MRIO) dataset. The results reveal that the YREB had an extremely high grey WF (GWF), approximately nine times higher than the blue WF (BWF), yet has consistently decreased by 210.00 billion m3 during 2002–2017, indicating a greater but mitigated stress from water pollution compared to water scarcity. Our analysis shows an increase in the Sustainability Index (SI) of YREB from 0.3706 to 0.4435, primarily due to the reduction in GWF. Water use equality among the three major sectors has also improved. The decomposition results highlight that water conservation and treatment technologies have the most significant impact on reducing WFs, followed by shifts in consumption patterns and adjustments in industry structure, all of which enhance the water sustainability of the YREB. This study underscores the critical role of water pollution control in ensuring sustainable water resource use, as reducing pollution loads supports responsible water exploitation, leading to improved water quality and optimized resource efficiency.

The complex hydrological conditions caused by the backwater effect at the confluence inevitably modify the geochemical processes of elements. However, there is still a lack of comprehensive understanding regarding the precise transformation mechanisms of nutrients in large river systems. This study aimed to investigate the hydrodynamic characteristics and their impact on phosphorus transfer in the lower Han River, which is influenced by backwater from the Yangtze River (the largest river in China). By establishing a hydrodynamic-water quality model, we have determined that the discharge ratio (the ratio of flow between the Han River discharge and the Yangtze River discharge) can be utilized as a representative indicator of the backwater effect from the Yangtze River on the Han River. Three distinct patterns were identified in this study: mixing, backwater, and intrusion. The corresponding discharge ratio values were categorized as >0.08, 0.01 similar to 0.08, and <0.01 respectively. Additionally, the extent of the backwater zone was determined, revealing that the length of the backwater zone increased from 50 km (XG) to 100 km (FS) as the discharge ratio decreased from 0.08 to 0.01. Furthermore, it was observed that the water level at the confluence rose from 2.52 m to 6.83 m in accordance with these changes in discharge ratio values. The migration pattern of phosphorus primarily involved the settling and retention of particulate phosphorus, particularly the labile particulate organic phosphorus (LOP) and dissolved organic phosphorus (DOP). When the confluent patterns became the intrusion pattern, the backwater zone expanded to 150 m (XT), causing a 10.40 m increase in water level at the confluence. An intrusion zone formed, and its phosphorus concentrations were same as Yangtze River's. Above the intrusion area, a backwater region formed and its concentrations of LOP and DOP decreased, while the concentration of PO43- increased due to the release from resuspended particles. This release was induced by higher velocity of bottom water brought about by the water exchange of two rivers. The discharge ratio of 0.01-0.08 resulted in the sedimentation of LOP and DOP, causing the lower Han River to act as a "sink" for phosphorus, potentially exacerbating phosphorus pollution. Higher discharge ratios in spring led to phosphorus release from sediment, increasing dissolved phosphorus concentrations and raising the risk of algal blooms in the lower Han River. These findings have significant implications for larger rivers worldwide and provide insights into strategies for ecological management and prevention of algal blooms.

The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010-2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions.

Lake Taihu, a subtropical shallow lake in the Yangtze River Basin, is the third-largest freshwater lake in China. It serves not only as a crucial source of drinking water and an ecological resource but also holds significant economic, tourism, and fisheries value. Phytoplankton, a vital component of aquatic ecosystems, plays a critical role in nutrient cycling and maintaining water structure. Its community composition and concentration reflect changes in the aquatic environment, making it an important biological indicator for monitoring ecological conditions. Understanding the impact of water quality on phytoplankton is essential for maintaining ecological balance and ensuring the sustainable use of water resources. This paper focuses on Lake Taihu, with water samples collected in February, May, August, and November from 2011 to 2019. Using quantile regression, a robust statistical analysis tool, the study investigates the heterogeneous effects of water quality on phytoplankton and seasonal variations. The results indicate significant seasonal differences in water quality in Lake Taihu, which substantially influence phytoplankton, showing weakly alkaline characteristics. When phytoplankton concentrations are low, pondus hydrogenii (pH), chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), water temperature (WT), and conductivity significantly affect them. At medium concentrations, COD, TP, TN, and WT have significant effects. At high concentrations, transparency and dissolved oxygen (DO) significantly impact phytoplankton, while TP no longer has a significant effect. These findings provide valuable insights for policymakers and environmental managers, supporting the prevention and control of harmful algal blooms in Lake Taihu and similar aquatic systems.

Semi -arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi -arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C -means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 degrees C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO 3 -N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north -south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

River receive substantial nutrient inputs, and serve as the main channel for nitrogen and phosphorus to enter the lake, their nutrient control is of great significance to the alleviation of lake eutrophication. While nutrient limitation affects the primary productivity of water ecosystems and the biodiversity of aquatic communities, identifying the limiting factors in riverine ecosystems across China remains elusive. Here, we explore which nutrients have a stronger effect on nutritional balance and aquatic ecosystems in China's rivers based on the total nitrogen (TN) and total phosphorus (TP) observations from 1412 sampling sites in 2018. This study supports the following three main conclusions. Though the percentages of the sites with TN or TP exceeding the limits varied as per different mesotrophic targets, and TP (53.7 %) contributed more to nutrient enrichment than TN (46.3 %). In addition, the spatial distribution characteristics of river nutrients were high in the north (arid zone) and low in the south (humid zone) in China. According to four classification criteria of N:P ratio, 70.8 % of the sampling sites were attributed to phosphorus limiting, much higher than the sites with nitrogen limiting (4.1 %). TN and TP have a synergistic effect on river nutrients, while TP has a stronger regulation framework. Our results reveal that the nutrients in China's rivers are mainly phosphorus limiting, which implies that phosphorus-oriented best management practices are more likely to maintain the nutrient balance of rivers towards healthy aquatic ecosystems. Synopsis: Phosphorus is the key factor that affecting the stability and nutrient balance of riverine ecosystem.

Mechanistic understanding and prediction of river algal blooms remain challenging. It is generally believed that these blooms are formed by the slowdown of water dynamics in tributaries due to the support of the main stream. However, few studies have investigated the impact of flow backward caused by the difference in water dynamics between the main stream and tributaries. Here, we focus on the eutrophication issue in the middle-lower reaches of the Han River, which is affected by the Middle Route of the South-to-North Water Diversion Project (SNWDP), the largest inter-basin water transfer project in Asia. We discover that the reversal of the Yangtze River water level could effectively alleviate the occurrence of Han River water blooms. The Yangtze River frequently back flows into the lower reaches of the Han River, with the probability of such events increasing as it nears the confluence (20 km from the Yangtze: 9.5 %, 10 km: 19.0 %, 8 km: 28.6 %). This flow backward carries nutrients that reduce the nitrogen to phosphorus ration (N:P), leading to a shift in the nutrient structure of the Han River. This change is concomitant with a significant decline in algae biomass (Chlorophyll -a = 11.19 mu g & sdot;L-1 and algae density = 0.41x10(7) cells & sdot;L(-1 )under natural flow, Chlorophyll -a = 5.19 mu g & sdot;L-1 and algae density = 0.18x10(7) cells & sdot;L-1 under flow backward), as well as a weakening of the correlation (R) between diatom density and chlorophyll-a concentration, i.e., R = 0.38 (p>0.05) under flow backward conditions versus R = 0.72 (p<0.01) under natural flow conditions. As phosphorus limitation typically suppresses algae growth, the correlation be-tween diatom density and chlorophyll-a concentration can help to reveal the dominance of diatoms, with stronger correlations indicating greater diatom dominance. Consequently, our study provides evidence that the flow backward can alleviate river algal blooms by weakening the growth advantage of diatoms. This study could prove valuable in investigating the eutrophication mechanism within the complex hydrodynamic conditions of rivers.Synopsis: Flow backward caused by the water level difference between the main streams and tributary alleviated the occurrence of river algal blooms in the confluence area.

The hydrological regimes and environmental changes in large riverine lakes are known for their complexity and high level of uncertainty. Scientifically uncovering the response mechanisms of water environments under complex hydrological conditions has become a challenging research objective, in the interdisciplinary of environmental science and hydrology. This study delved into the unstable response process between water level and quality of Poyang Lake, the largest freshwater lake as well as one of the most intense hydrological variability water bodies in China. We developed a non-steady state identification approach incorporates Seasonal and Trend decomposition using Loess (STL) and Wavelet Correlation (WTC) methods. The results showed that there were remarkable alterations in the hydrological regime and water quality at both seasonal and long-term scale of Poyang Lake over the past nine years. These alterations were accompanied by significant non-steady state characteristics, reflecting the changes in the response between water level and quality. The employment of the STL-WTC method revealed a significant nonlinear response between the long-term trends of water level and quality, in both the 4-month and 12-month frequency bands. In particular, our findings showed an intriguing shift towards in-phase behavior between water level and quality in the 12-month frequency band, rather than the anti-phase pattern observed previously. This correlation changed more significantly in seasons where the fluctuation pattern of water level varied sharply, such as summer and winter in Poyang Lake. Our study underscored the hydrological conditions and water quality of large lakes connected to rivers do not exhibit a long-term stable unidirectional response state, alterations in hydrological rhythms may induce a transition in the relationship from negative correlation towards nonlinear positive correlation between water level and water quality. Finally, this non-steady state fluctuation of water conditions can further exacerbate long-term and seasonal degradation of water quality.

One critical question for water security and sustainable development is how water quality responses to the changes in natural factors and human activities, especially in light of the expected exacerbation in water scarcity. Although machine learning models have shown noticeable advances in water quality attribution analysis, they have limited interpretability in explaining the feature importance with theoretical guarantees of consistency. To fill this gap, this study built a modelling framework that employed the inverse distance weighting method and the extreme gradient boosting model to simulate the water quality at grid scale, and adapted the Shapley additive explanation to interpret the contributions of the drivers to water quality over the Yangtze River basin. Different from previous studies, we calculated the contribution of features to water quality at each grid within river basin and aggregated the contribution from all the grids as the feature importance. Our analysis revealed dramatic changes in response magnitudes of water quality to drivers within river basin. Air temperature had high importance in the variability of key water quality indicators (i.e. ammonia-nitrogen, total phosphorus, and chemical oxygen demand), and dominated the changes of water quality in Yangtze River basin, especially in the upstream region. In the mid-and downstream regions, water quality was mainly affected by human activities. This study provided a modelling framework applicable to robustly identify the feature importance by explaining the contribution of features to water quality at each grid.

China produces a large amount of industrial effluent with multiple pollutants contained, along with a flourishing economy. This study aims to examine the dynamics between China's industrialization and accompanying environmental pressure based on the gray water footprint (GWF) concept. A newly proposed net GWF (NetGWF) and the decoupling index (DI) are applied to evaluate China's industrial activities during 2002-2015 in different modes considering typical, all, and individual pollutants. The NetGWF dynamics are further quantitatively decomposed into 17 effects of not only commonly assessed drivers but also industrial fixed capital formation, inventory variation, and import, using an advanced dynamic decomposition analysis approach. Results show NetGWF is an effective indicator measuring domestic water pollution stress from industrialization, with NetGWF-AllPlt (estimated using all pollutants) validated to be more reliable and sensitive than NetGWF-COD&NH3N (estimated using Chemical oxygen demand and Ammonia nitrogen). An overall decoupling between China's industrialization and wastewater pollution is identified with most of DIs less than 1.0 caused mainly by decreased (by around 40%) industrial NetGWFs for 2002-2015. Industrial fixed capital formation and export have caused main components of China's industrial GWF, with proportions of 37.3% and 30.8%, respectively, followed by urban household consumption (16.8%). Volatile phenol, Petroleum, and Ammonia nitrogen are recognized as three decisive contaminants to the industrial NetGWFs. Technological development is the dominant contributor (-50%) to decreasing China's industrial NetGWFs, while fixed capital formation (18%) and export (16%) are principal drivers increasing the NetGWFs. Based on these, we expect to provide informative findings for building a pollution-decoupled industrialization.

Scientific understanding of the scale effects of landscape structure on water quality is essential for national land planning and water environment management. However, there is no consensus on the optimal spatial landscape scale affecting water quality. To this end, we developed a novel mix scale division method, which is consist of the intersection of sub-basin, stream buffer zone and circular buffer zone. Based on the water quality data from 113 monitoring stations of the Yellow River Basin, we quantitatively identified the spatial scale effects of various landscape indexes on water quality, as well as the nonlinear response of the water quality along the gradient of landscape indicators in the Yellow River, by using the Redundancy analysis (RDA) and Generalized Additive Model (GAM). We show that landscape indexes related to buildup and agricultural lands had significant impact on water quality changes, and this impact was more influential during the rainy season compared with the dry season. We also identified that the optimal scale and landscape pattern corresponding to the main non-point source factors, i.e., CODCr, total nitrogen (TN), and total phosphorus (TP). The variations of these three water quality indicators were predominated by buildup land (B-CA) at sub-basin (with a contribution percentage of 43.7%), agricultural land (A-CA) at the scale of stream buffer (bs) = 1000 m and circular buffer (bc) = 20 km (50.2%), and the largest patch index of agriculture (A-LPI) at the scale of bs = 1000m and bc = 25 km (41.7%), respectively. The B-CA>50000 ha at the sub-basin scale and A-LP = 50% at the mixed scale (bs = 1000m, bc = 25 km) would accelerate the deterioration of water quality. Results implied that water quality could be improved by implementing landscape regulation within an appropriate scale and considering the non-linear effects of landscape structure on water quality.

鄱阳湖近年氮磷营养物浓度逐步升高,入湖河流是鄱阳湖氮磷输入的重要途径.采用BATHTUB模型建立了鄱阳湖入湖河流与湖区ρ(TP)、ρ(TN)的响应关系,模拟了入湖河流执行GB 3838—2002《地表水环境质量标准》中不同氮磷标准限值对湖区水质的影响,发现当入湖河流ρ(TP)执行河流Ⅲ类标准限值或超过Ⅲ类标准限值时,对应湖区ρ(TP)超标;入湖河流执行Ⅲ类及以上湖泊水质标准限值时,湖区水质可以达到Ⅲ类保护目标,但对入湖河流存在一定的过保护现象.因此,以满足现行湖泊水质达标为情景,以湖泊ρ(TP)、ρ(TN)各类别标准限值为目标,试算了入湖河流氮磷控制限值,提出了鄱阳湖入湖河流的氮磷控制限值建议方案,其中鄱阳湖湖体水质目标为Ⅲ类时,入湖河流ρ(TP)、ρ(TN)控制限值分别为0.075和1.20 mg∕L,此时入湖河流氮磷控制限值方案既能保证湖泊水质达标,又不会造成对河流的水质控制过于严格.研究显示,基于湖泊水环境质量达标情况试算的入湖河流氮磷所需控制限值,建议可作为解决入湖氮磷污染控制问题的参考.