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Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River-which is the longest river in China. As phytoplankton are sensitive indicators of trophic changes in water bodies, characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control. Here, we used direct microscopic count and environmental DNA (eDNA) metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin (Chengdu, Sichuan Province, China). The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis. Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling. At the phylum level, the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta, Chlorophyta, and Cyanophyta, in contrast with Chlorophyta, Dinophyceae, and Bacillariophyta identified by eDNA metabarcoding. In α-diversity analysis, eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method. Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios > 16:1 in all water samples. Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth. The results could be useful for implementing comprehensive management of the river basin environment. It is recommended to control the discharge of point- and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients (e.g., Jianyang-Ziyang). Algae monitoring techniques and removal strategies should be improved in 201 Hospital, Hongrihe Bridge and Colmar Town areas.

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This research investigates the future dynamics of water yield services in the Gorgan River Basin in the North of Iran by analyzing land cover changes from 1990 to 2020, using Landsat images and predicting up to 2040 with the Land Change Modeler and InVEST model under three scenarios: continuation, conservation, and mitigation. The results indicate significant shifts in agricultural land impacted water yields, which fluctuated from 324.7 million cubic meters (MCM) in 1990 to 279.7 MCM in 2010, before rising to 320.1 MCM by 2020. The study uniquely assesses the effects of land use changes on water yields, projecting a 13.6 % increase in water yield by 2040 under the continuation scenario, a 3.9 % increase under conservation, and a 1.6 % decrease under mitigation, which limits changes on steep slopes to prevent soil erosion and floods. This underscores the interplay between land use, vegetation cover, and water yield, emphasizing strategic land management for water resource preservation and effective watershed management in the GRB.

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Contaminants of emerging concern (CECs) in aquatic environments can adversely impact ecosystems and human health even at low concentrations. This study assessed the risk of 162 CECs, including neonicotinoid pesticides, triazine pesticides, carbamate pesticides, psychoactive substances, organophosphate esters, antidepressants, per- and polyfluoroalkyl substances, and antibiotics in 10 drinking water sources and two tributaries (Jialing and Wujiang Rivers) of the Upper Yangtze River in Chongqing, China. Target screening detected 156 CECs at 0.01-2218.2 ng/L, while suspect screening via LC-QTOF-MS identified 64 CECs, with 13 pesticides, 29 pharmaceuticals and personal care products, and 2 industrial chemicals reported for the first time in the Yangtze River Basin. Risk quotient-based ecological risk assessment revealed that 48 CECs posed medium to high risks (RQ > 0.1) to aquatic life, with antibiotics (n = 20) as the main contributors. Non-carcinogenic risks were below negligible levels, but carcinogenic risks from neonicotinoids, triazines, antidepressants, and antibiotics were concerning. A multi-criteria prioritization approach integrating occurrence, physico-chemical properties, and toxicological data ranked 26 CECs as high priority. This study underscores the importance of comprehensive CEC screening in rivers and provides insights for future monitoring and management strategies.

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Terrestrial ecosystems are critical to the global carbon cycle and climate change mitigation. Over the past two decades, the Yangtze River Basin (YRB) has implemented various ecological restoration projects and active management measures, significantly impacting carbon stock patterns. This study employed random forest models to analyze the spatial and temporal patterns of carbon stocks in the YRB from 2001 to 2021. In 2021, carbon density in the YRB ranged from 8.5 to 177.4 MgC/ha, with a total carbon stock of 18.05 PgC. Over 20 years, the YRB sequestered 1.26 billion tons of carbon, accounting for 11.28 % of the region's fossil fuel carbon emissions. Notably, forests exhibited the highest carbon density, averaging 98.01 ±â€¯25.01 MgC/ha (2021) with a carbon stock growth rate of 51.6 TgC/yr. Piecewise structural equation model was used to assess the effects of climate and human activities on carbon density, revealing regional variability, with unique patterns observed in the source region. Human activities primarily influence carbon density indirectly through vegetation alterations., while climate change directly impacts ecosystem biophysical processes. These findings offer critical insights for climate mitigation and adaptation strategies, enhancing the understanding of carbon dynamics for sustainable development and global carbon management.

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Groundwater pollution has attracted widespread attention as a threat to human health and aquatic ecosystems. However, the mechanisms of pollutant enrichment and migration are unclear, and the spatiotemporal distributions of human health risks are poorly understood, indicating insufficient groundwater management and monitoring. This study assessed groundwater quality, human health risks, and pollutant sources in the Fen River Basin(FRB). Groundwater quality in the FRB is good, with approximately 87 % of groundwater samples rated as "excellent" or "good" in both the dry and rainy seasons. Significant precipitation elevates groundwater levels, making it more susceptible to human activities during the rainy season, slightly deteriorating water quality. Some sampling points in the southern of Taiyuan Basin are severely contaminated by mine drainage, with water quality index values up to 533.80, over twice the limit. Human health risks are mainly from As, F, NO3-, and Cr. Drinking water is the primary pathway of risk. From 2019 to 2020, the average non-carcinogenic risk of As, F, and NO3- increased by approximately 28 %, 170 % and 8.5 %, respectively. The average carcinogenic risk of As and Cr increased by 28 % and 786 %, the overall trend of human health risks is increasing. Source tracing indicates As and F mainly originate from geological factors, while NO3- and Cr are significantly influenced by human activities. Various natural factors, such as hydrogeochemical conditions and aquifer environments, and processes like evaporation, cation exchange, and nitrification/denitrification, affect pollutant concentrations. A multi-tracer approach, integrating hydrochemical and isotopic tracers, was employed to identify the groundwater pollution in the FRB, and the response of groundwater environment to pollutant enrichment. This study provides a scientific basis for the effective control of groundwater pollution at the watershed scale, which is very important in the Loess Plateau.

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Wildfires pose significant threats worldwide, requiring accurate prediction for mitigation. This study uses machine learning techniques to forecast wildfire severity in the Upper Colorado River basin. Datasets from 1984 to 2019 and key indicators like weather conditions and land use were employed. Random Forest outperformed Artificial Neural Network, achieving 72 % accuracy. Influential predictors include air temperature, vapor pressure deficit, NDVI, and fuel moisture. Solar radiation, SPEI, precipitation, and evapotranspiration also contribute significantly. Validation against actual severities from 2016 to 2019 showed mean prediction errors of 11.2 %, affirming the model's reliability. These results highlight the efficacy of machine learning in understanding wildfire severity, especially in vulnerable regions.

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Global industrial activities contribute significantly to carbon emissions, impacting climate change and necessitating innovative methods for precise emission monitoring and management at both regional and international levels. Based on nighttime light data, POI data, land use data and energy statistics, this study calculated the carbon emissions of different industrial categories in the Yellow River Basin from 2005 to 2020 and analyzed the temporal and spatial characteristics of their changes to reveal the carbon emission patterns of different industrial categories in the basin. This study analyzes the carbon emissions of various industrial categories from a spatial perspective, addressing the limitations of traditional industrial carbon emission assessments at the spatial scale. The results showed that although the growth rate of industrial carbon emissions in the Yellow River Basin has slowed down significantly, it has not yet reached the peak, with the carbon emissions increasing from 400,0647t in 2005 to 519,216,200t in 2020. The mechanical and electronic manufacturing industry had the largest carbon emissions, which accounting for 37.08% of the total carbon emissions. Medical pharmaceuticals had the fewest, only accounting for 1.16% of the total carbon emissions. The spatial distribution of carbon emissions showed a cluster distribution, and the emissions gradually decrease from the center to the periphery. In addition, the carbon emissions of the construction industry, medical pharmaceutical industry and mechanical and electronic manufacturing industry were concentrated in and around the cites, and were closely related to urban development, infrastructure and technological progress. Furthermore, the study reveals that the relationship between carbon emissions and population structure across different industrial categories is complex. A stable relationship exists between carbon emissions and the population within the mechanical and electronic manufacturing, metallurgy, and chemical industries. However, for the clothing, furniture, and pharmaceutical industries, population is not the sole influencing factor on their carbon emissions. This study provides a new perspective on low-carbon green and sustainable development strategies for industrial carbon emissions in the Yellow River Basin, and emphasizes the importance of constructing detailed, diversified and innovative management strategies in the face of climate change challenges.

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Nowadays, one of the most critical challenges is reduced access to water. Climate change, industrialization, and population growth have caused many countries to suffer from water crises, especially in arid and semi-arid areas. The Culiacan River basin in Sinaloa is a region of great importance in Mexico due to its intensive agricultural activity. Hence, water quality assessment has become a necessity to ensure sustainable water use. This study describes the spatiotemporal water quality features of the Humaya, Tamazula, and Culiacan Rivers within the Culiacan River basin and their sources of contamination. Twenty-two water quality parameters were analyzed from samples taken every 6 months from 2012 to 2020 at 19 sampling sites in the basin. A multivariate statistical analysis revealed significant correlations (r > 0.85) between the water quality parameters. The modified Integrated Water Quality Index (IWQI) identified severe pollution in samples from the urban river section of the basin, while good water quality conditions were found upstream. Severe contamination was observed in 26.32% of the samples, whereas only 13.45% evidenced good water quality. The Water Quality Index (WQI) indicated that 94.74% of the samples presented fair water quality, suggesting that the surface waters of the Culiacan River Basin are suitable for agricultural irrigation. This study provides insights into the current water quality status of the surface waters in the Culiacan River Basin, identifying significant pollution sources and areas of concern. The spatiotemporal dynamics of water quality in the Culiacan River basin revealed the importance of continuous monitoring and effective water management practices to improve water quality and achieve sustainable agricultural practices.

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Elevated ammonium (NH4-N) contents in groundwater are a global concern, yet the mobilization and enrichment mechanisms controlling NH4-N within riverside aquifers (RAS) remain poorly understood. RAS are important zones for nitrogen cycling and play a vital role in regulating groundwater NH4-N contents. This study conducted an integrated assessment of a hydrochemistry dataset using a combination of hydrochemical analyses and multivariate geostatistical methods to identify hydrochemical compositions and NH4-N distribution in the riverside aquifer within Central Yangtze River Basin, ultimately elucidating potential NH4-N sources and factors controlling NH4-N enrichment in groundwater ammonium hotspots. Compared to rivers, these hotspots exhibited extremely high levels of NH4-N (5.26 mg/L on average), which were mainly geogenic in origin. The results indicated that N-containing organic matter (OM) mineralization, strong reducing condition in groundwater and release of exchangeable NH4-N in sediment are main factors controlling these high concentrations of NH4-N. The Eh representing redox state was the dominant variable affecting NH4-N contents (50.17 % feature importance), with Fe2+ and dissolved organic carbon (DOC) representing OM mineralization as secondary but important variables (26 % and 5.11 % feature importance, respectively). This study proposes a possible causative mechanism for the formation of these groundwater ammonium hotspots in RAS. Larger NH4-N sources through OM mineralization and greater NH4-N storage under strong reducing condition collectively drive NH4-N enrichment in the riverside aquifer. The evolution of depositional environment driven by palaeoclimate and the unique local environment within the RAS likely play vital roles in this process.

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The ecological environment of wetlands in semi-arid regions has deteriorated, and vegetation succession has accelerated due to climate warming-induced aridification and human interference. The nutrient acquisition strategies and biomass allocation patterns reflect plant growth strategies in response to environmental changes. However, the impact of nutrient acquisition strategies on biomass allocation in successional vegetation remains unclear. We investigated 87 plant communities from 13 wetland sites in the semi-arid upper Yellow River basin. These communities were divided into three successional sequences: the herbaceous community (HC), the herbaceous-shrub mixed community (HSC), and the shrub community (SC). The nutrient composition of stems and leaves, as well as the biomass distribution above and belowground, were investigated. Results revealed that aboveground biomass increased with succession while belowground biomass decreased. Specifically, SC exhibited the highest stem biomass of 1,194.53 g m-2, while HC had the highest belowground biomass of 2,054.37 g m-2. Additionally, significant positive correlations were observed between leaf and stem biomasses in both HC and SC. The nitrogen (N) and phosphorus (P) contents within aboveground parts displayed an evident upward trend along the succession sequence. The highest N and P contents were found in SC, followed by HSC, and the lowest in HC. Stem N was negatively correlated with stem, leaf, and belowground biomass but positively correlated with root-shoot ratio. Leaf P displayed positive correlations with aboveground biomass while showing negative correlations with belowground biomass and root-shoot ratio. The ratios of C:N, C:P, and N:P in stem and leaf exhibited positive correlations with belowground biomass. The random forest model further demonstrated that stem N and leaf P exerted significant effects on aboveground biomass, while leaf P, stem N and P, and leaf C:P ratio had significant effects on belowground components. Additionally, the root-shoot ratio was significantly influenced by leaf P, leaf C:P ratio, and stem N, P, and C:P ratio. Therefore, the aboveground and belowground biomasses exhibited asynchronism across successional sequences, while plant nutrient acquisition strategies, involving nutrient levels and stoichiometric ratios, determined the biomass allocation pattern. This study offers valuable insights for assessing vegetation adaptability and formulating restoration plans in the semi-arid upper Yellow River basin.

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Scarcity of stream salinity data poses a challenge to understanding salinity dynamics and its implications for water supply management in water-scarce salt-prone regions around the world. This paper introduces a framework for generating continuous daily stream salinity estimates using instance-based transfer learning (TL) and assessing the reliability of the synthetic salinity data through uncertainty quantification via prediction intervals (PIs). The framework was developed using two temporally distinct specific conductance (SC) datasets from the Upper Red River Basin (URRB) located in southwestern Oklahoma and Texas Panhandle, United States. The instance-based TL approach was implemented by calibrating Feedforward Neural Networks (FFNNs) on a source SC dataset of around 1200 instantaneous grab samples collected by United States Geological Survey (USGS) from 1959 to 1993. The trained FFNNs were subsequently tested on a target dataset (1998-present) of 220 instantaneous grab samples collected by the Oklahoma Water Resources Board (OWRB). The framework's generalizability was assessed in the data-rich Bird Creek watershed in Oklahoma by manipulating continuous SC data to simulate data-scarce conditions for training the models and using the complete Bird Creek dataset for model evaluation. The Lower Upper Bound Estimation (LUBE) method was used with FFNNs to estimate PIs for uncertainty quantification. Autoregressive SC prediction methods via FFNN were found to be reliable with Nash Sutcliffe Efficiency (NSE) values of 0.65 and 0.45 on in-sample and out-of-sample test data, respectively. The same modeling scenario resulted in an NSE of 0.54 for the Bird Creek data using a similar missing data ratio, whereas a higher ratio of observed data increased the accuracy (NSE = 0.84). The relatively narrow estimated PIs for the North Fork Red River in the URRB indicated satisfactory stream salinity predictions, showing an average width equivalent to 25 % of the observed range and a confidence level of 70 %.

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Two previously unknown species of Rhinogobius have been discovered in the streams of the Upper Youshui River, within the Yuan River Basin, Xiushan County, Chongqing, China. These new species are named as Rhinogobiussudoccidentalis and Rhinogobiuslithopolychroma. Phylogenetic analysis based on mitochondrial genomes revealed that R.sudoccidentalis is genetically closest to R.reticulatus, while R.lithopolychroma shares the greatest genetic similarity with R.leavelli. Morphological distinctions allow for the clear differentiation of these species. Rhinogobiussudoccidentalis sp. nov. is characterized by having VI-VII rays in the first dorsal fin and I, 8-9 rays in the second dorsal fin. The longitudinal scale series typically consists of 22-24 scales, while the transverse scale series comprises 7-8 scales. Notably, the predorsal scale series is absent and the total vertebrae count is 12+17=29. Rhinogobiuslithopolychroma sp. nov. can be distinguished from other species by the presence of 13-15 rays on the pectoral fin. Its longitudinal scale series ranges from 30 to 33 scales, with no scales in the predorsal area. The total vertebral count is 30, with 12 precaudal and 18 caudal vertebrae. The head and body of this species are light gray with irregular orange markings on the cheeks and opercle. Through morphological and molecular analyses, it has been confirmed that R.lithopolychroma and R.sudoccidentalis represent novel species within the Rhinogobius genus.

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It is essential to determine the dominant/indicator species and their ecological preferences to develop a comprehensive bioassessment strategy for rivers. The objective of this work was to provide dependable ecological evaluation techniques for ecosystems that experience significant human-induced disruptions, with the Hari Rud River (a transboundary water resource) serving as a case study during the May (wet) and July (dry) periods of 2023. Canonical correspondence analysis revealed that electrical conductivity (EC), dissolved oxygen, ortho-phosphate (P O 4 3 - ), and nitrate (N O 3 - ) had substantial impacts on the spatial distribution of diatom species in the basin. Relatively pollution-tolerant species, including Nitzschia brevissima, N. capitellata, N. umbonata, N. palea, N. dissipata, and Navicula cryptocephala, had close relationships with EC and P O 4 3 - , integrated with Joi Injil and Karbar streams. Of the sampling stations, especially Hari Rud River1 and Hari Rud River2, exhibited pollution-sensitive diatom species, Cymbella excisa, Achnanthidium minutissimum, Diatoma moniliformis, Cymbella affinis, and Meridion circulare. Various eco-regional diatom metrics exhibited distinct scores, indicating a range of ecological status from high to bad in the Lower Hari Rud River basin. European diatom indices revealed good ecological status for Hari Rud River 1 and 4, but poor or bad ecological statuses for Joi Injil and Karbar streams. The findings of the current study emphasize the requirements of autecological studies to understand the regional diatom compositions and their ideal survival ranges in different locations before considering using non-regional diatom indices to evaluate the ecological status of lotic systems.

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Accurate and rapid prediction of water quality is crucial for the protection of aquatic ecosystems. This study aims to enhance the prediction of total phosphorus (TP) concentrations in the middle reaches of the Yangtze River by integrating advanced modeling techniques. Using operational and discharge data from the Three Gorges Reservoir (TGR), along with water quality parameters from downstream sections, we used Grey Relational Analysis (GRA) to rank the factors contributing to TP concentrations. The analysis identified turbidity, permanganate index (CODMn), total nitrogen (TN), water temperature, chlorophyll a, upstream water level variation, and discharge from the Three Gorges Dam (TGD) as the top contributors. Subsequently, a coupled neural network model was established, incorporating these key contributors, to predict TP concentrations under the dynamic water level control during flood periods in the TGR. The proposed GRA-CEEMDAN-CN1D-LSTM-DBO model was compared with conventional models, including BP, LSTM, and GRU. The results indicated that the GRA-CEEMDAN-CN1D-LSTM-DBO model significantly outperformed the others, achieving a correlation coefficient (R) of 0.784 and a root mean square error (RMSE) of 0.004, compared to 0.58 (R) and 0.007 (RMSE) for the LSTM model, 0.576 (R) and 0.007 (RMSE) for the BP model, and 0.623 (R) and 0.006 (RMSE) for the GRU model. The model's accuracy and applicability further validated in two sections: YC (Yunchi) in Yichang City and LK (Liukou) in Jingzhou City, where it performed satisfactorily in predicting TP in YC (R = 0.776, RMSE = 0.007) and LK (R = 0.718, RMSE = 0.007). Additionally, deep learning analysis revealed that as the distance away from dam increased, prediction accuracy gradually decreased, indicating a reduced impact of TGR operations on downstream TP concentrations. In conclusion, the GRA-CEEMDAN-CN1D-LSTM-DBO model demonstrates superior performance in predicting TP concentration in the middle reaches of the Yangtze River, offering valuable insights for dynamic water level control during flood seasons and contributing of smart to the advancement of water management in the Yangtze River.

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Land use change stands as the primary factor influencing habitat quality (HQ). Clarifying the spatiotemporal change and the obstacle factors of the coupling relationship between HQ and urbanization level (UL) can provide imperative references for achieving sustainability in the Yellow River Basin (YRB). This study is based on the InVEST model, spatial autocorrelation, and obstacle factor analysis to measure the spatiotemporal dynamics and impediments of the coupling relationship between HQ and UL from 2000 to 2020 in the YRB. The findings were as follows: (1) From 2000 to 2020, the HQ showed a tendency of rise first and then fall, with the pattern of "High in the middle and west, low in the east"; (2) from 2000 to 2020, the UL had an upward trend, with the pattern of "Low in the west, high in the middle and east"; (3) the coupling and coordination level of HQ and UL in the YRB changed from extreme incoordination to verge of coordination, and it had a distribution pattern of "High in the east, low in the west", with the high-value area expanding to the east and the low-value area shrinking to the west. (4) Location condition, climate, proportion of construction land, vegetation index, and proportion of non-agricultural employment are the main obstacle factors that determined the coupling and coordination of the HQ and UL.

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The Pearl River Basin (PRB), the second-largest river basin in China, is the most economically developed at its lower reaches and rich in arsenic-bearing mineral resources at its upper reaches. Arsenic (As) is emerging as a serious environmental and health-related concern, becoming a focal point of public attention. The objective of this study was to explore the spatiotemporal variations of As concentration and distribution in the topsoils of the PRB using monitoring data 3 times from the 1990s, the 2000s, and the 2010s, based on geochemical baselines project. Results indicate that the As content in soils displayed an increasing pattern from the 1990s (median 11.40 mg/kg) to the 2000s (14.46 mg/kg), followed by a decrease from the 2000s to the 2010s (12.25 mg/kg). The largest changes occurred in mining areas. The proportion of samples with As concentrations exceeding the risk screening value decreased from 19.51 % (1990s), 10.78 % (2000s), to 4.69 % (2010s). The hazard quotient (HQ) of pollutant into non-carcinogenic risk for adults increased from 0.12 in the 1990s to 0.19 in the 2000s, and then decreased to 0.08 in the 2010s. Meanwhile, the HQ for children increased from 0.96 in the 1990s to 1.54 in the 2000s, and decreased to 0.67 in the 2010s. These characteristics suggest that certain areas still exhibited localized As pollution and associated health risks. The high values and changes of As in soils are attributed to geologic background and anthropogenic activities. Comprehensive management, particularly the implementation of soil pollution prevention and control policies by the Chinese government since 2008, has constituted a pivotal tool in reducing the As content in the alluvial surface soils newly formed by river water picking up pollutants that decreased from the 2000s to the 2010s into watercourses and deposited in the overbank or plain region.

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The Jinsha River Basin (JRB) contributes a significant amount of sediment to the Yangtze River; however, an imbalance exists between runoff and sediment. The underlying mechanisms and primary factors driving this imbalance remain unclear. In this study, the Shapley Additive Explanation (SHAP) and Geographical Detector Model (GDM) were employed to quantify the importance of the driving factors for water yield (WYLD) and sediment yield (SYLD) using the Soil and Water Assessment Tool (SWAT) model in the JRB. The results indicated that the SWAT model performed well in simulating runoff and sediment, with R2 > 0.61 and NSE > 0.5. Based on the simulated data, SYLD exhibited strong spatiotemporal linkages with WYLD. Temporally, both sediment and runoff showed decreasing trends, with the sediment decrease being more pronounced. Spatially, WYLD and SYLD displayed similar distribution patterns, with low values in the southwest and high values in the northeast. By quantifying the driving factors, we found that climatic factors, including precipitation and potential evapotranspiration, were the main influencing factors for WYLD and SYLD across the entire region, though their contributions to the two variables differed. For WYLD, climatic factors accounted for 70 % of the total influencing factors, whereas their contribution to SYLD was 50 %. Furthermore, soil type and land-use type played significant roles in the SYLD, with importance values of 16 % and 12 %, respectively. Under the influence of surface conditions, the proportion of SYLD in the JRB to the total SYLD in the Yangtze River Basin was greater than that of WYLD. The findings of this study provide scientific evidence and technical support for local environmental impact assessments and the formulation of soil and water conservation plans.

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Sulfate (SO42-) is an essential anion in drinking water and a vital macronutrient for plant growth. However, elevated sulfate levels can impact ecosystem or human health and could be an important indicator of acid rock drainage or pollution. Therefore, monitoring SO42- sources and transport is important for water quality assessments. This study focused on exploring the sources and transformations of SO42- as well as estimating the proportional contribution of the potential SO42- pollutant sources to groundwater and surface water in a tropical river basin, the Densu River Basin. The study used major ions combined with stable sulfur and oxygen isotope compositions and a Bayesian isotope mixing model, MixSIAR. The major ion characteristics indicate that SO42- concentrations remain stable throughout the rainy and dry seasons but originate from diverse sources. The multi-isotope model (δ34SSO4, δ18OSO4) identified four potential SO42- sources: detergent, precipitation, sewage, and sulfate fertilizer. However, the δ34SSO4 and δ18OSO4 values of the fertilizer source signatures overlapped with those of precipitation and sewage. Nevertheless, the contributions from each source were disentangled using the MixSIAR model, which revealed sewage as the most dominant SO42- pollutant in the Densu Basin, accounting for ~47 % of sulfate in groundwater and ~ 56 % of sulfate in surface water. Sulfate fertilizer (~33 %) was the second most important source after sewage for groundwater, while detergent (~23 %) was the second most important source for surface water. The redox processes of bacterial sulfate reduction and sulfide oxidation were determined to have a minimal impact on the sulfur isotope fractionation within the basin. This study highlights the benefits of combining major ions, sulfur isotopes and the MixSIAR model for identifying sources of sulfate. This approach accounts for uncertainties in source contributions which allows for more robust and reliable apportionment of sulfate sources. The study emphasizes the need for effective waste management and pollution control measures to protect water quality and provides vital guidelines on how to partition sulfate sources on a large catchment scale and evidence for making pollution management decisions on water resources.

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Agricultural production and sustainable human livelihoods in large river basins are threatened by climate change, human activities, and resource constraints. However, due to the complexity of socio-ecological interactions and agricultural sustainability, current studies are still limited by a priori knowledge and systematic analyses, as well as by the lack of quantification and identification of key factors and valuable pathway structures for agricultural production activities. Here, we combined observation-based causal inference and network analysis to quantify and assess the complex interactions in agricultural production in the Yellow River Basin (YRB) based on data from 12 factors relevant to agriculture over 40 years. We quantitatively assessed the leveraging and hindering roles of the factors in the interacting network system and provided managers with optimization priorities and possible causal pathways to achieve sustainable agriculture in the basin. For example, the fruit yield and income of rural households were identified as leveraging factors that positively affect the agricultural economy. Groundwater was seen as a hindering factor in dampening the negative impacts of the system, highlighting the importance of preventing groundwater depletion. Moreover, the findings suggest that spatially diverse causal interaction structures exist in the YRB and have shaped a variety of distinctive agricultural development modes. Our research ideas and results highlight both systemic considerations and the amplifying or dampening role of factors in interaction pathways, providing valuable quantitative insights into the management and intervention of sustainable agriculture in large river basins. Owing to replaceable and extensible network models, the methodology has the potential to be utilized in a variety of study areas and topics with complex socio-ecological interactions.

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Quantitatively predicting the impacts of climate change on water demands of various crops is essential for developing measures to ensure food security, sustainable agriculture, and water resources management, especially in arid regions. This study explored the water footprints (WFs) of nine major crops in the middle and downstream areas of Shule River Basin, Northwest China, from 1989 to 2020 using the WF theory and CROPWAT model and predicted the future WFs of these crops under four emission and socio-economic pathway (SSPs-RCPs) scenarios, which provides scientific support for actively responding to the negative impacts of climate change in arid regions. Results indicated: (1) an increasing trend of the overall crop WF, with blue WF accounting for 80.31-99.33% of the total WF in the last 30 years. Owing to differences of planting structure, water-conservation technologies, and other factors, the multi-year average WF per unit area of crops was 0.75 × 104 m3 hm-2 in downstream area, which was higher than that in midstream area (0.57 × 104 m3 hm-2) in the last 30 years; therefore agricultural water use efficiency in the downstream area was lower than that in the midstream area, implying that the midstream area has more efficient agricultural water utilization. (2) an initial increase and then decrease of crop WFs in the study area under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios by the end of the century, reaching their peak in 2030s which was higher than that from 1989 to 2020; with the maximum growth rates in the midstream area ranging from -0.85% in SSP5-8.5 to 5.33% in SSP2-4.5 and 29.74% in SSP5-8.5 to 34.71% in SSP2-4.5 in the downstream area. The local agricultural water demand would continue to increase and water scarcity issues would be more severe in the next 10-20 years, affecting downstream areas more. Under the SSP3-7.0 scenario, crop WF values of the midstream and downstream regions will be 2.63 × 108 m3 and 4.22 × 108 m3 in 2030, respectively, which is significantly higher than those of other scenarios and show a long-term growth trend. The growth rate of the midstream and downstream regions will reach 44.71% and 81.12%, respectively, by the end of this century, so the local agricultural water use would be facing more strain if this scenario materializes in the future. Therefore, the Shule River Basin should encourage development of water-saving irrigation technologies, adjust the planting ratio of high water consuming crops, and identify other measures to improve water resource utilization efficiency to cope with future water resource pressures.