Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river.

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.

Impact of Land use dynamics on the water yields in the Gorgan river basin.

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.

Two new species of freshwater goby (Teleostei, Gobiidae) from the Upper Youshui River, Chongqing, China.

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.

Integrating machine learning for enhanced wildfire severity prediction: A study in the Upper Colorado River basin.

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.

Evidencing anthropogenic pollution of surface waters in a tropical region: a case study of the Culiacan River basin.

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.

Stream salinity prediction in data-scarce regions: Application of transfer learning and uncertainty quantification.

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 %.

Mechanisms of evolution and pollution source identification in groundwater quality of the Fen River Basin driven by precipitation.

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.

Climate change and C4 and C3 grasses in a midlatitude dryland steppe.

Climate change is projected to alter the structure of plant communities due to increasing temperatures and changes to precipitation patterns, particularly in midlatitude dryland ecosystems. Modifications to climatic suitability may lead to major community changes such as altered dominant plant functional types. Previous studies have indicated that climatic suitability is likely to increase for C4 grasses and decrease for C3 grasses in the Western United States. However, if no C4 grass species currently exist to serve as a propagule source, expansion into areas of increased suitability will be limited. We conducted a field and modeling study in the Upper Green River Basin (UGRB) of Western Wyoming to determine if (1) C4 grasses are present to provide a propagule source and (2) C4 grasses are likely to increase in importance relative to C3 grasses due to climatic changes. We searched 44 sites for C4 grasses to establish presence, and modeled suitability at 35 sites using 17 Global Climate Models, two greenhouse gas Representative Concentration Pathways (RCPs; 4.5 and 8.5), and two time periods (mid- and late century; 2030-2060 and 2070-2099, respectively). We found C4 grasses at 10 of the 44 sites, indicating that there is a present propagule source. Our model projected increases in suitability for both C3 and C4 grasses across sites for all RCPs and time periods. In the mid-century RCP 4.5 scenario, the C3 functional type increased in projected biomass in 29 of 35 sites, and the C4 type increased in 31 sites. In this scenario, C3 grasses increased in projected biomass by a median 4 g m-2 (5% change), and C4 grass biomass increased by a median 8 g m-2 (21% change). Our study suggests that climate change will increase climatic suitability for grasses across the UGRB, and that all requirements are in place for C4 grasses to increase in abundance.

An adaptive cycle resilience perspective to understand the regime shifts of social-ecological system interactions over the past two millennia in the Tarim River Basin.

Socio-ecological systems (SESs) in arid regions have experienced multiple transformations throughout history due to human activities and natural forces. However, few studies have used the resilience cycle model to explain the resilience status and determinants of SESs over the past two millennia. This study proposes the adaptive cycle resilience (ACR) perspective to investigate regime shifts of socio-ecological system interactions in the Tarim River Basin (TRB) over the past two millennia. An ACR framework combining a piecewise linear regression model (PLR), ACR theory, and physical resilience models has been built to assess and quantify socio-ecological system resilience. Key indicators such as climate variability, settlement numbers, war frequency, glacier accumulation, and oasis area changes are identified and quantified to evaluate SESs adaptability and transformability. Glacier accumulation serves as a proxy for long-term climate change, while oasis area changes reflect the direct impact of human activities and environmental feedback on ecosystem productivity. Population and war indicators provide insights into social system stability and the impact of conflicts on SESs dynamics. The findings reveal that the 7th century and 1850s are critical points of regime shifts in the ACR. 200s BC-350s AD and 700s AD-900s AD are in the forward loop (r-K) period of the ACR. 350s AD-700s AD and 900s AD-1850s AD are the adaptive resilience backward loop (Ω-α) phase. Assessing the historical socio-ecological system resilience and identifying key transition points can inform proactive measures to mitigate potential regime shifts. Combining historical data with resilience theory provides a deep understanding of the ACR of SESs and their driving factors. This enriches the theoretical understanding of SESs and offers a robust case study for future resilience assessments and scenario analyses in arid regions.

Revealing the Ground Deformation and Its Mechanism in the Heihe River Basin by Interferometric Synthetic Aperture Radar and Optical Images.

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China's second largest inland river basin. It is a typical oasis-type agricultural area in northwest China's arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites' line-of-sight (LOS) direction can be up to -70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone.

Studies on the conservation and sustainability challenges of the wetland resources of the Baro-Akobo River Basin in Gambella and Benishangul-Gumuz regions, Ethiopia.

In this study, the water quality of the Baro-Akobo River Basin in Ethiopia was comprehensively assessed. Key parameters including temperature (°C), pH, dissolved oxygen (DO), electrical conductivity, total dissolved solids (TDS), and ion concentrations such as K+, Ca2+, Na+, NO3 -, NO2 -, PO4 3-, CO3 2-, HCO3 -, and NH4 +were measured using standard methods, alongside sampling of phytoplankton, zooplankton, macroinvertebrates, macrophytes, and fish. Phytoplankton and zooplankton were sampled using Hydrobios nets with mesh sizes of 30 µm and 55 µm, respectively, while macroinvertebrates were collected via the kicking method. Results indicated generally alkaline pH levels and elevated turbidity, but acceptable levels of dissolved oxygen and ion concentrations according to WHO and FAO guidelines. Moreover, the study suggests that the wetlands in Gambella and Benishangul-Gumz regions are currently in good condition, providing valuable insights for the conservation and sustainable management of Ethiopia's water resources, ensuring their conservation for both present and future generations. Local authorities can use the study's findings to implement remedial measures to protect water quality and biodiversity in the regions.

[Effects of Landscape Pattern on Water Quality at Different Spatial and Temporal Scales in Wuding River Basin and Yanhe River Basin].

To explore the relationship between land use and water quality in basins with different land use patterns at different spatial and temporal scales, the Wuding River Basin and Yanhe River Basin were taken as research objects. Based on land use data in 2020 and water quality monitoring data during two periods, the redundancy analysis method was adopted to quantitatively explore the impact of land use on water quality at multiple scales. The results showed that： ① The main land use types in the two basins were cultivated land and grassland, and the difference was mainly in the proportion of bare land and forest land. ② The water quality in spring was better than that in autumn, and the water quality in the middle and lower reaches was worse. ③ The interpretation rate of land use on the riparian scale was the highest in the two basins. ④ The effect of land use on water quality was more significant in the Wuding River Basin in autumn than in spring, whereas the Yanhe River Basin showed the opposite trend. ⑤ Different land uses had different impacts on water quality. Bare land, cultivated land, and Shannon diversity index （SHDI） in the Wuding River Basin had significant impacts on water quality, whereas grassland, cultivated land, artificial surface, patch density （PD）, and SHDI were significant in the Yanhe River Basin. Cultivated land and artificial surfaces in the Wuding River Basin had a negative impact on water quality. Grassland and bare land had a negative correlation with most chemical indicators. Artificial surfaces and grasslands in the Yanhe River Basin had a negative impact on water quality, whereas forest land had a significant purification effect. The research results provide important information for sustainable land use and multi-scale landscape planning, which can be used to improve water quality.

[Spatio-temporal Pattern of Ecosystem Service Value Evolution in the Yihe River Basin in the Context of Land Use Change].

In this study, the modified equivalent factor method was applied to account for the long time series ecosystem service value （ESV） of the Yihe River Basin from 1975 to 2020 in the context of land use change, and the cold hot spot analysis and topographic position analysis methods were introduced to explore the characteristics of its spatial pattern. The results showed that： ① From 1975 to 2020, the land use type of the Yihe River Basin was dominated by arable land, and the land use changes were characterized by the rapid decrease of arable land and the continuous expansion of construction land, a slight increase in the area of forest land and grassland, a contraction of the water body area, and little change in the area of unused land. ② The modified equivalent factor method was more suitable for accounting for the ESV in the basin. From 1975 to 2020, the overall ESV of the basin showed an upward spiral trend （33.369-33.816 billion CNY）, dominated by the regulating services. The ESV of arable land was the highest with a decreasing trend, whereas the ESV of unused land was the lowest. ③ In the horizontal spatial pattern, the hot spot of ESV was near mountains and reservoirs, and the cold spot of ESV was near urban areas. In terms of vertical spatial patterns, with growing topographic gradient, vertical changes in ESV for all land use types showed an increasing trend followed by a decreasing trend. The results of the study revealed the spatial and temporal patterns of ecosystem service values in the Yihe River Basin in the context of land use change and provide a scientific basis for optimizing the land use structure and spatial pattern and enhancing ecosystem services.

Multi-objective double layer water optimal allocation and scheduling framework combing the integrated surface water - groundwater model.

Balancing the water consumption of agricultural and ecological is the key point of sustainable social and economic development in an inland river basin. The growth of desert riparian forests in inland river basins mainly depends on a certain phreatic water table depth (PWTD). The main object of this study was to allocate and schedule water resources to regulate the PWTD and satisfy agricultural water demand. Therefore, a multi-objective double layer optimal allocation and scheduling framework based on the computationally efficient integrated surface water-groundwater model (ISGWM), which can simulate the surface water processes, groundwater recharge and discharge processes, and PWTD changes, was constructed and applied to the mainstream of Tarim River Basin (TRB). The top layer model of the framework is an optimal ecological water allocation model, and its optimal allocation results are used as the initial solution of the bottom layer model. The results show that under 5 different inflow frequencies, the agricultural water shortage rate is 0, 17.38 %, 17.41 %, 14.06 %, and 19.94 %, respectively. The PWTD regulation has a great performance. After the optimal scheduling, the proportions of good growth of the control area behind the gate under different inflow frequencies were 98.18 %, 98.18 %, 98.18 %, 90.91 %, and 94.55 %. Agricultural water shortage is mainly due to the non-uniformity distribution of intra-annual inflow and the lack of controlling hydraulic engineering. The regulation of PWTD can guarantee the growth of desert riparian forests on both sides of the mainstream of TRB. Besides, we explored the feasibility of exploiting groundwater to supplement agricultural water consumption. The groundwater exploitation should be controlled within the scope of not causing excessive increase of PWTD (difference between PWTD and target depth <1 m), due to the groundwater exploitation to supplement agricultural water will lead to the increase of PWTD. Overall, this framework, which regulates the PWTD with the change of ecological water supply based on the ISGWM, provides a new idea for the allocation and scheduling of agricultural and ecological water resources in arid inland river basins. It also provides a new method for the coupled cooperative operation of surface water and groundwater.

Predicting the impact of climate change on crop water footprint using CMIP6 in the Shule River Basin, China.

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.

Exploring the evolution of ecosystem health and sustainable zoning: A perspective based on the contributions of climate change and human activities.

Maintaining ecosystem health (EH) in watersheds is crucial for building a national pattern of ecological security. However, a comprehensive diagnosis of watershed EH and an exploration of its driving mechanisms are still lacking. This study proposed an EH assessment model from a vitality-organization-resilience-service-environment (VORSE) perspective. Taking the Yellow River Basin of Shaanxi Province (YRBS), China, as a research object, the spatiotemporal evolution trend of EH from 2000 to 2020 was quantified. At the same time, we also quantified the respective contributions of climate change (CC) and human activities (HA) to the EH dynamics based on residual analysis. The results showed that EH in the YRBS increased by 11.80 % from 2000 to 2020, and the spatial distribution of the EH was higher in the southern region than in the northern part. At the pixel scale, areas with improving trends accounted for 90.57 % of the YRBS, while 9.43 % deteriorated, with the improving areas mainly in northern Shaanxi and the deteriorating areas in the Guanzhong region. The correlation between the EH and precipitation was primarily positive, while the correlation between the EH and temperature was mainly negative. The residual analysis showed that the contribution rate of CC to EH changes was 78.54 %, while that of HA was 21.46 %, indicating that CC was the dominant driver of EH changes in the YRBS. Specifically, 82.64 % of the improvement in EH was attributed to CC and 17.36 % to HA. Conversely, 65.30 % of the deterioration in EH was attributed to CC and 34.70 % to HA. Furthermore, CC, HA, and CC&HA dominated EH changes in 26.85 %, 3.77 %, and 69.38 % of the YRBS area, respectively. In addition, the Hurst exponent analysis identified six types of future EH development scenarios, each requiring different restoration strategies. This study provides valuable insights for future EH diagnosis, EH restoration efforts, and the formulation of sustainable development goals in other watersheds.

Spatiotemporal evolution of runoff and sediment and their dominant driving factors in the Lower Jinsha River basin.

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.

Tracing sulfate sources in a tropical agricultural catchment with a stable isotope Bayesian mixing model.

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.

Phosphorus prediction in the middle reaches of the Yangtze river based on GRA-CEEMDAN-CNLSTM-DBO.

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.

Cross-scale coupling of ecosystem service flows and socio-ecological interactions in the Yellow River Basin.

As research on the full spectrum of ecosystem service (ES) generation and utilization within coupled human and natural systems (CHANS) has expanded, many studies have shown that the spatiotemporal dynamics of ESs are managed and influenced by human activities. However, there is insufficient research on how ESs are affected by bidirectional coupling between societal and ecological factors during spatial flow, particularly in terms of cross-scale impacts. These bidirectional influences between humans and nature are closely related to the utilization and transfer of ESs and affect the perception of spatiotemporal patterns of ESs and the formulation of management strategies. To fill this research gap, this study focuses on the Yellow River Basin (YRB), using network models to track the spatial dynamics of ES flows (ESFs) and the interactions between ecosystems and socio-economic systems within the basin on an annual scale from 2000 to 2020. The results highlight cross-scale impacts and feedback processes between local subbasins and the larger regional basin: As the supply-demand ratios of freshwater ESs, soil conservation ESs, and food ESs increase within individual subbasins of the YRB, more surplus ESs flow among subbasins. This not only alleviates spatial mismatches in ES supply and demand across the entire basin but also enhances the connectivity of the basin's ESF network. Subsequently, the cascading transfer and accumulation of ESs feedback into local socio-ecological interactions, with both socio-economic factors and the capacity for ES output within subbasins becoming increasingly reliant on external ES inflows. These results underscore the crucial role of ESFs within the CHANS of the YRB and imply the importance of cross-regional cooperation and cross-scale management strategies in optimizing ES supply-demand relationships. Furthermore, this study identifies the potential risks and challenges inherent in highly coupled systems. In conclusion, this work deepens the understanding of the spatial flow characteristics of ESs and their socio-ecological interactions; the analytical methods used in this study can also be applied to research on large river basins like the YRB, and even larger regional ecosystems.