Linking water environmental factors and the local watershed landscape to the chlorophyll a concentration in reservoir bays

Integrating partial least square structural equation modelling and machine learning for causal exploration of environmental phenomena

Understanding the causes of environmental phenomena is crucial for promoting positive outcomes and mitigating negative ones. Partial least squares structural equation modelling (PLS-SEM) is becoming a valuable tool for evaluating causal relationships in ecological environment studies (EES). However, many studies using PLS-SEM often overlook nonlinear relationships and interactions between environmental factors, and have not fully utilized the powerful capabilities of machine learning. Using Gaoyang Lake in the Three Gorges Reservoir Region as a case study, this research presents a framework combining several techniques to better understand the causes of Spring Harmful Algal Blooms (Spring HABs) from 2019 to 2023. The framework uses PLS-SEM to compare and select the optimum causal structure among alternatives, Bayesian Networks (BN) to identify alternative causal pathways, Multivariate Adaptive Regression Splines (MARS) and Polynomial Regression (PR) to uncover interactions and non-linearities among predictors. Our findings indicate that, the BN-generated structure implemented in PLS-SEM had an improved Bayesian Information Criterion (BIC) score compared to the initial PLS-SEM. No interactions between latent variables were observed using MARS. However, significant non-linearities were identified using PR, and when integrated into the initial PLS-SEM, they produced the optimal model with Q2predict of 0.177, RMSE of 0.967, R2 of 0.421, and BIC of -23.497. Euphotic depth emerged as a critical factor influencing the occurrence of Spring HABs, due to its interaction with the epilimnion depth. Surface nutrient levels (indicated by total phosphorus loadings) and meteorological elements (mean air temperature and sun hours) were identified as the second and third most important latent variables, contributing 25.5 % and 13.5 % to Spring HABs, respectively. This framework is recommended for improving the causal understanding of other site-specific environmental phenomena, providing a scientific basis for more effective environmental management.

Long-term trends and rising levels of refractory dissolved organic matter in a suburban plateau lake: Impacts of hydrological changes

Dissolved organic matter (DOM) characteristics and concentrations in lakes are strongly associated with terrestrial input, phytoplankton dynamics, and physicochemical environment. Hydrological conditions can affect multiple aspects of the lake environment, thereby interfering with DOM cycling. This study investigates the long-term trends and drivers of DOM accumulation in Lake Erhai, a subtropical plateau lake in southwestern China, focusing on the role of hydrological processes in driving its accumulation and persistence. By analyzing data from 1992 to 2023-including bulk chemical analysis, 3D-EEM fluorescence spectroscopy, degradation experiments and bayesian structural equation modeling (BSEM), it is concluded that a 174 % increase in water residence time (WRT), from 2.8 years to 7.8 years, driven by reduced inflow and outflow volumes, has promoted the accumulation of refractory DOM (RDOM), raising chemical oxygen demand (CODMn) and presenting substantial challenges challenges to water quality management. Degradation experiments revealed limited biodegradability of DOM (15 % over 28 days) and minimal photodegradation (13.5 % over 72 h), with more than 80 % remaining in a refractory state. Spectroscopic analyses revealed compositional shifts in DOM with prolonged WRT, characterized by decreased humic-like substances and increased protein-like compounds, indicating a progressive transition from allochthonous to autochthonous DOM dominance. BSEM analysis identified a significant temporal shift in DOM drivers: during the initial phase (1992-2010), human activity pressure (HAP) and riverine input quality (RIQ) collectively explained 70 % of the variance, with natural drivers contributing less than 20 %; whereas in the subsequent phase (2010-2023), anthropogenic influences diminished as hydrological and climatic factors became predominant, with hydrological regime (HR) and climatic factors (CF) jointly accounting for 87 % of RDOM variance, reflecting a transition from anthropogenic to climate-hydrological driven accumulation patterns. This research underscores the critical role of hydrological residence time in determining DOM composition, sources, and persistence in plateau lakes following partial decoupling of external pollution sources. The findings highlight the dual influence of climate and hydrology on lakes experiencing significant pressures from reduced water resources and increasing water demand, challenging conventional management strategies focused exclusively on external nutrient control. The case of Lake Erhai demonstrates the necessity for integrated management approaches that address both external and internal DOM dynamics to support sustainable water quality and ecosystem integrity.

A unified subregional framework for modeling stream water quality across watersheds of a hydrologic subregion

Modeling stream water quality is informed by knowledge about pertinent factors and processes. The models must be validated against water quality observations, which may exist sufficiently in some watersheds (data rich watersheds) but may be limited or lacking in other cases (i.e., ungauged and poorly gauged watersheds). Machine learning (ML) algorithms have been growingly applied for water quality modeling, but they are limited to the data used for their training and validation. The question arises whether an ML-based model developed in one watershed can be transferred to adjacent watersheds. Here, we developed a unified subregional framework (i.e., one single consistent model configuration and standardized input variables) for modeling daily in-stream concentrations of nutrients-total phosphorus (TP) and total nitrogen (TN)-fecal coliform (FC) and dissolved oxygen (DO) in watersheds of a hydrologic subregion. The watersheds differ in their characteristics in terms of dominant land use/land cover (LULC) and topography. The framework was presented in the Peace-Tampa Bay subregion located in Southwest Florida. We found that the unified framework can be successfully developed for the watershed-scale modeling of DO and TP (Nash Sutcliffe Efficiency [NSE] > 0.75), and to a lesser extent for TN and FC (NSE > 0.49). The influence of dominant LULC was most prominent in modeling FC and TP, while the effect of topography was more pronounced for FC and TN than TP and DO. We also observed that longer-term antecedent conditions were more influential in modeling FC and TP, while shorter term saturation was more influential for modeling TN and DO. Insights from this study can be used to develop similarity criteria based on watershed characteristics, which support development of transferable models for predicting stream water quality in ungauged and poorly gauged watersheds.

Changing climate intensifies downstream eutrophication by enhancing nitrogen availability from tropical forests

The contribution of diffuse nutrient exports from forests to downstream water bodies is significant owing to their extensive spatial distribution across watersheds. However, the intricacies of coupling mechanism between diffuse nutrient exports and meteorological factors driving downstream eutrophication remain poorly understood. Multiple methods involving field sampling, laboratory analysis, and model simulation were utilized to investigate the impact of diffuse nutrient exports from tropical forests on chlorophyll a concentration dynamic in the downstream reservoir. A strong positive correlation was observed between air temperature and chlorophyll a concentration, indicating the direct influence of climatic factors on microalgal biomass. The significant positive linear relationship was also observed between diffuse nitrate exports and chlorophyll a concentration, with a regression coefficient of 0.36 (P < 0.001), underscoring the role of nitrogen inputs in stimulating microalgal growth. The interplay between diffuse nitrate exports and meteorological factors was shown to regulate chlorophyll a concentration fluctuation. Additionally, the structural equation model revealed that increasing temperature and decreasing precipitation could elevate chlorophyll a concentration by enhancing nitrogen availability. Monte Carlo simulation results further revealed that temperature and precipitation were the most influential factors affecting chlorophyll a concentration during dry and rainy seasons, with sensitivity values of 0.94 and - 0.76, respectively. Notably, the eutrophication status was projected to deteriorate from light to moderate under diminishing precipitation conditions. These findings underscore the urgency of addressing eutrophication risks in reservoirs surrounded by tropical forests and the implementation of effective nitrate mitigation strategies is imperative, which offers theoretical guidance for the management of eutrophic water restoration within tropical rainforest regions under changing climate conditions.

qPCR-based phytoplankton abundance and chlorophyll a: A multi-year study in twelve large freshwater rivers across the United States

Phytoplankton overgrowth, which characterizes the eutrophication or trophic status of surface water bodies, threatens ecosystems and public health. Quantitative polymerase chain reaction (qPCR) is promising for assessing the abundance and community composition of phytoplankton. However, applications of qPCR to indicate eutrophication and trophic status, especially in lotic systems, have yet to be comprehensively evaluated. For the first time, this study correlates qPCR-based phytoplankton abundance with chlorophyll a (the most widely used indicator of eutrophication and trophic status) in multiple freshwater rivers. From early summer to late fall in 2017, 2018, and 2019, we evaluated phytoplankton, chlorophyll a, pheophytin a, and the Trophic Level Index (TLI) in twelve large freshwater rivers in three regions (western, midcontinent, and eastern) in the United States. qPCR-based phytoplankton abundance had positive allometric correlations with chlorophyll a concentration (adjusted R2 = 0.5437, p-value < 0.001), pheophytin a concentration (adjusted R2 = 0.3378, p-value <0.001), and TLI (adjusted R2 = 0.4789, p-value < 0.001). Thus, a greater phytoplankton abundance suggests a higher trophic status. This work also presents the numerical values of qPCR-based phytoplankton abundance defining the boundaries among trophic statuses (e.g., oligotrophic, mesotrophic, and eutrophic) of freshwater rivers. The sampling sites in the midcontinent rivers were more eutrophic because they had significantly higher chlorophyll a concentrations, pheophytin a concentrations, and TLI values than the sites in the western and eastern rivers. The higher phytoplankton abundance at the midcontinent sites confirmed their higher trophic status. By linking qPCR-based phytoplankton abundance to chlorophyll a, this study demonstrates that qPCR is a promising avenue to investigate the population dynamics of phytoplankton and the trophic status (or eutrophication) of freshwater rivers.

Identifying factors influencing reservoir eutrophication using interpretable machine learning combined with shoreline morphology and landscape hydrological features: A case study of Danjiangkou Reservoir, China

Reservoir nearshore areas are influenced by both terrestrial and aquatic ecosystems, making them sensitive regions to water quality changes. The analysis of basin landscape hydrological features provides limited insight into the spatial heterogeneity of eutrophication in these areas. The complex characteristics of shoreline morphology and their impact on eutrophication are often overlooked. To comprehensively analyze the complex relationships between shoreline morphology and landscape hydrological features, with eutrophication, this study uses Danjiangkou Reservoir as a case study. Utilizing Landsat 8 OLI remote sensing data from 2013 to 2022, combined with a semi-analytical approach, the spatial distribution of the Trophic State Index (TSI) during flood discharge periods (FDPs) and water storage periods (WSPs) was obtained. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), explained the relationships between landscape composition, landscape configuration, hydrological topography, shoreline morphology, and TSI, identified key factors at different spatial scales and validated their reliability. The results showed that: (1) There is significant spatial heterogeneity in the TSI distribution of Danjiangkou Reservoir. The eutrophication levels are significant in the shoreline and bay areas, with a tendency to extend inward only during the WSPs. (2) The importance of landscape composition, landscape configuration, hydrological topography, and shoreline morphology to TSI variations during the FDPs are 25.12 %, 29.6 %, 23.09 %, and 22.19 % respectively. Besides shoreline distance, the Landscape Shape Index (LSI) and Hypsometric Integral (HI) are the two most significant environmental variables overall during the FDPs. Forest and grassland areas become the most influential factors during the WSPs. The influence of landscape patterns and hydrological topography on TSI varies at different spatial scales. At the 200 m riparian buffer zone, the increase in cropland and impervious areas significantly elevates eutrophication levels. (3) Morphology complexity, shows a noticeable threshold effect on TSI, with complex shoreline morphology increasing the risk of eutrophication.

Disentangling the main factors influencing spring algal blooms in the Three Gorges Reservoir using partial least square structural equation modelling

Eutrophication and the associated algal blooms is a pervasive problem affecting global health, aquaculture, agriculture, water-related industries, and freshwater ecosystems. Spring algal blooms (SABs), which are less common than summer blooms, occur during a time that is thought to be less advantageous due to mild winds, little precipitation, and a relatively small amount of NPS pollutants being transported. Thus, It is pertinent to understand further the factors influencing SABs directly and/or indirectly for improved management. Consequently, Partial least square structural equation modelling (PLS-SEM) was employed to measure the direct and indirect effects of nutrients, lake hydrodynamics (Lake HD), meteorological elements (ME), stratification, Lake Bio-Optics, and Bottom Sediment-Water interaction (BSWI) in Gaoyang Lake of the Three Gorges Reservoir which is characterized by SABs. Based on our findings, total phosphorus (TP) and total nitrogen (TN) together slightly outperformed TP alone in explaining variations in chlorophyll a (chl-a), but the difference was not statistically significant. Thus the parsimonous PLS-SEM model with TP was chosen, and it explained 66.8%, 54.0%, 21.4%, and 59.7% variation in stratification, Lake Bio-Optics, nutrients, and chl-a, respectively. Surprisingly, ME and Lake Bio-Optics had a negative total effect on chl-a during the study. The magnitude of factors influencing SAB occurrence was of the order Lake Hydrodynamics > nutrients > Bottom Sediments-Water Interphase > Lake Bio-Optics > Meteorological Elements > Stratification. This study successfully decoupled and quantified several latent variables' complex simultaneous causal effects on chl-a.

Impact of Landscape Patterns on Water Quality in Urbanized Rivers at Characteristic Scale: A Case of Pearl River Delta, China

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Seasonal Effects of Constructed Wetlands on Water Quality Characteristics in Jinshan Lake: A Gate Dam Lake (Zhenjiang City, China)

Urban lakes commonly suffer from nutrient over-enrichment, resulting in water quality deterioration and eutrophication. Constructed wetlands are widely employed for ecological restoration in such lakes but their efficacy in water purification noticeably fluctuates with the seasons. This study takes the constructed wetland of Jinshan Lake as an example. By analyzing the water quality parameters at three depths during both summer and winter, this study explores the influence of the constructed wetland on the water quality of each layer during different seasons and elucidates the potential mechanisms underlying these seasonal effects. The results indicate that the constructed wetland significantly enhances total nitrogen (TN) concentration during summer and exhibits the capacity for nitrate-nitrogen removal in winter. However, its efficacy in removing total phosphorus (TP) is limited, and may even serve as a potential phosphorus (P) source for the lake during winter. Water quality test results of different samples indicated they belong to Class III or IV. Restrictive factors varied across seasons: nitrate-nitrogen and BOD5 jointly affected water quality in winter, whereas TP predominantly constrained water quality in summer. These results could provide a reference for water quality monitoring and management strategies of constructed wetlands in different seasons in Jiangsu Province.

Insights into priming effects of dissolved organic matter degradation in urban lakes with different trophic states

Priming effect (PE) is recognized as an important potential mechanism for dissolved organic matter (DOM) degradation in aquatic ecosystems. However, the priming effects (PEs) of various priming substances on the degradation of DOM pools in urban lakes along diverse trophic states remain unknown. To address this knowledge gap, the PEs and drivers of glucose and plant leachate of lake water with three trophic states were investigated. We reveal differences in the bioavailability of DOM in lake water, glucose, and plant leachate. The PE of the same priming substance was significantly higher in highly-eutrophic lake water than in mesotrophic lake. The priming intensity induced by glucose was significantly higher when compared to plant leachate. Regarding the addition of glucose, humic-like components (C1 and C3) showed slight PE, while the tyrosine-like component C2 showed negative PE. However, the positive PEs were observed on three components after adding plant leachate. The driver of PE by glucose shifted from nutrients to DOM components with increasing trophic levels. The PEs induced by plant leachate were affected by nutrients, chlorophyll-a (Chl-a), water chemistry, and DOM components in lightly/moderately-eutrophic lake water. This study revealed the intensities, directions, and drivers of PEs, providing essential insights into uncovering the DOM biogeochemical process in urban lakes.

Greenspace pattern, meteorology and air pollutant in Taiwan: A multifaceted connection

Air pollution is a global environmental concern that poses a significant threat to human health. Given the impact of urbanization and climate change, green planning is being encouraged to improve air quality. The study aims to examine the intricate relationships between greenspace pattern and outdoor air around 73 in-situ stations over Taiwan during the dry (November to April) and wet (May to December) seasons from 2015 to 2020. To achieve this, Partial Least Squares - Structural Equation Modeling was utilized to analyze the interactions among seven dimensions: greenspace - GS, gaseous pollutant - GP, particle pollutant - PP, O3 - OZONE, air temperature - TEMP, relative humidity - RH, and wind speed - WS. The GS involves seven landscape metrics: edge density, total edge, effective mesh size, largest patch area, percentage of landscape, total core area, and patch cohesion index. The results indicate that the GS has a stronger effect on the GP, whereas its effect on the PP is weaker during the dry season compared to the wet season. While its effect on the TEMP is weaker, it shows a slightly stronger effect on the RH during the dry season. Moreover, the GS mediates the air pollutant dimensions during the two seasons, with the RH acting as a primary mediator. The meteorological dimensions primarily have a greater influence on the air pollutant dimensions during the dry season than the wet season. Consequently, the GS explains 11.3 % more and 18.4 % less of the variances in the RH and TEMP during the dry season, respectively. Moreover, the GS and meteorological dimensions yield a seasonal difference in explained variance, with the highest value observed for the OZONE (R2 = 24.2 %), followed by the PP (R2 = 9.7 %) and GP (R2 = 7.7 %). Notably, seven landscape metrics serve as potential indicators for green strategies in urban planning to enhance outdoor air quality.

Decoupling the response of vegetation dynamics to asymmetric warming over the Qinghai-Tibet plateau from 2001 to 2020

Global land surface air temperature data show that in the past 50 years, the rate of nighttime warming has been much faster than that of daytime, with the minimum daily temperature (Tmin) increasing about 40% faster than the maximum daily temperature (Tmax), resulting in a decreased diurnal temperature difference. The Qinghai-Tibet Plateau (QTP) is known as the "roof of the world", where temperatures have risen twice as fast as the global average warming rate in the last few decades. The factors affecting vegetation growth on the QTP are complex and still not fully understood to some extent. Previous studies paid less attention to the explanations of the complicated interactions and pathways between elements that influence vegetation growth, such as climate (especially asymmetric warming) and topography. In this study, we characterized the spatial and temporal trends of vegetation coverage and investigated the response of vegetation dynamics to asymmetric warming and topography in the QTP during 2001-2020 using trend analysis, partial correlation analysis, and partial least squares structural equation model (PLS-SEM) analysis. We found that from 2001 to 2020, the entire QTP demonstrated a greening trend in the growing season (April to October) at a rate of 0.0006/a (p < 0.05). The spatial distribution pattern of partial correlation between NDVI and Tmax differed from that of NDVI and Tmin. PLS-SEM results indicated that asymmetric warming (both Tmax and Tmin) had a consistent effect on vegetation development by directly promoting greening in the QTP, with NDVI values being more sensitive to Tmin, while topographic factors, especially elevation, mainly played an indirect role in influencing vegetation growth by affecting climate change. This study offers new insights into how vegetation responds to asymmetric warming and references for local ecological preservation.

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality

The study goal was to determine spatiotemporal variations in chlorophyll-a (Chl-a) concentration using models that combine hydroclimatic and nutrient variables in 150 tropical reservoirs in Brazil. The investigation of seasonal variability indicated that Chl-a varied in response to changes in total nitrogen (TN), total phosphorus (TP), volume (V), and daily precipitation (P). Therefore, an empirical model for Chl-a prediction based on the product of TN, TP, and normalized functions of V and P was proposed, but their individual exponents as well as a general multiplicative factor were adjusted by linear regression for each reservoir. The fitted relationships were capable of representing algal temporal dynamics and blooms, with an average coefficient of determination of R2 = 0.70. The results revealed that nutrients yielded better predictability of Chl-a than hydroclimatic variables. Chl-a blooms presented seasonal and interannual variability, being more frequent in periods of high precipitation and low volume. The equations demonstrate different Chl-a responses to the parameters. In general, Chl-a was positively related to TN and/or TP. However, in some cases (22%), high nutrient concentrations reduced Chl-a, which was attributed to limited phytoplankton growth driven by light deficiency due to increased turbidity. In 49% of the models, precipitation intensified Chl-a levels, which was related to increases in the nutrient concentration from external sources in rural watersheds. Contrastingly, 51% of the reservoirs faced a decrease in Chl-a with precipitation, which can be explained by the opposite effect of dilution of nutrient concentration at the reservoir inlet in urban watersheds. In terms of volume, in 67% of the reservoirs, water level reduction promoted an increase in Chl-a as a response to higher nutrient concentration. In the other cases, Chl-a decreased with lower water levels due to wind-induced destratification of the water column, which potentially decreased the internal nutrient release from bottom sediment. Finally, applying the model to the two largest studied reservoirs showed greater sensitivity of Chl-a to changes in water use classes regarding variations in TN, followed by TP, V, and P.

Controlled and driving mechanism of the SPM variation of shallow Brackish Lakes in arid regions

Suspended particulate matter (SPM) in the brackish Ebinur Lake of arid northwest China profoundly affect its water quality and watershed habitat quality. However, the actual driving mechanisms of the Lake's SPM changes remain unclear. Therefore, the purpose of this study is to explore the controlling factors driving the variability of SPM in the Ebinur Lake. This study constructed month-by-month SPM maps of Ebinur Lake based on time-series remote-sensing imageries and SPM inversion model. Thirty-four factors that might influence SPM changes were extracted, and the Partial Least Squares Structural Equation Modeling (PLS-SEM), suitable for complex relationships and factor interactions, was applied to identify the relative influence of each factor quantitatively. The results showed: (1) a clear increasing trend of SPM concentration in Ebinur Lake from 2011 to 2020; (2) that SPM changes were influenced by external and internal factors, explaining 48.2 % and 46.9 % of the changes, respectively; (3) that, to the external factors, meteorological factors exerted the greatest influence on SPM (relative contribution of 38.9 %); that, to the internal factors, water salinity imposed the greatest influence on SPM (relative contribution of 43.3 %); (4) that, among the meteorological factors, the measured variable Alashankou wind speed expressed the most significant positive effect on SPM (weighting coefficient of 0.894), and sulfate generated the strongest positive effect on SPM (weighting coefficient of 0.791) among the water salinity factors. Hence, the quantitative identification of drivers of SPM changes in Ebinur Lake could provide a new perspective to investigate the driving mechanisms of lake water quality in arid areas and inform their sustainable restoration and management.

Determining the main contributing factors to nutrient concentration in rivers in arid northwest China using partial least squares structural equation modeling

Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.

Intensive human land uses cause the biotic homogenization of algae and change their assembly process in a major watershed of China

Human land uses are a crucial driver of biodiversity loss in freshwater ecosystems, and most studies have focused on how cities or croplands influence alpha diversity while neglecting the changes in community composition (beta diversity), especially in algae. Here, we examined the taxonomic and functional composition of algae communities and their underlying drivers along the human land-use intensity gradient in the Huai River basin, the third largest basin in China. Our results indicated that the increased intensity of human land use caused biotic homogenization (decreasing compositional dissimilarity between sites) of algae communities in terms of both taxonomic and functional traits. Functional beta diversity was more sensitive to human land uses than taxonomic beta diversity. Furthermore, we found that the increased intensity of human land use altered algae assemblage processes. As opposed to the low- or moderate-intensity human land uses, in high-intensity groups, species sorting rather than dispersal limitations dominated algae community assembly. NO2-N, HCO3, and Fe were the major factors explaining the variance in the taxonomic and functional beta diversities of algae. Human land use reshaped the taxonomic and functional structures of algae, raising concerns about the ecological processes altered by human activity.

Changes in the water environment and its major driving factors in Poyang Lake from 2016 to 2019, China

The hydrological exchange process between Poyang Lake (PYL), the largest freshwater lake in China, and the Yangtze River leads to drastic changes in water area (WA) and water level (WL), as well as apparent fluctuations in lake nutrients, algal organisms, and trophic level index. This study investigated the current status of the PYL water environment and the influence of hydrological changes on the nutrient status of the floodplain of the lake. Based on monthly measured data from six hydrological stations from 2016 to 2019, it was hypothesized that WA and WL were the key regulators of the spatial and temporal distribution patterns of lake water quality and algal growth, including water temperature, water clarity (Secchi depth [SD]), and nutrient levels. The results revealed that (1) the spatial and temporal distribution characteristics of major nutrients in PYL were influenced by dynamic changes in hydrological characteristics (SD, total nitrogen [TN], and total phosphorus [TP]); (2) the eutrophication level in PYL has been in a steady state in recent years, while the central area has been more prone to the risk of eutrophication (e.g., the peak eutrophication index during Period 1 [January to April] in the water near the Duchang station reached 70); and (3) there were significant correlations among environmental variables, nutrients, and algal organisms, with different spatial and temporal distribution characteristics (p < 0.05), while the changes in WA and WL considerably influenced the water environment in the PYL.

Estimating the pollutant loss rate based on the concentration process and landscape unit interactions: a case study of the Dianchi Lake Basin, Yunnan Province, China

The landscape analysis model establishes a quantitative relationship between landscape patterns and pollution processes. The spatial heterogeneity within and between landscapes affects the pollutant transmission process and originates from the superposition effect of terrestrial geographical and morphological characteristics. This study aimed to develop a new method to estimate the pollutant loss rate. From the perspective of the flow process of pollutants entering a water body, the interaction between each landscape unit and adjacent unit during pollutant migration was simulated along the pollutant migration flow path. The role of pollutants affected by external forces in the process of migration could be divided into "promoting" and "hindering." Four indices were proposed to simulate the pollutant loads entering the lake. The linear coefficients between the load of the pollutants chemical oxygen demand (COD_Cr), ammoniacal nitrogen (NH₃-N), total nitrogen (TN), and total phosphorus (TP) entering the lake and the pollutant load emission weighted by the upstream and downstream confluence ratio index were 0.930, 0.835, 0.925, and 0.795, respectively, and the non-linear variance explanation coefficients were 87.70%, 87.50%, 87.60%, and 84.70%, respectively. When the surface resistance was integrated into the index as a parameter, the linear and nonlinear correlation coefficients were significantly improved. The linear coefficients were 0.952, 0.897, 0.919, and 0.939, respectively, and the non-linear variance explanations were 99.00%, 97.30%, 95.10%, and 97.30%, respectively. The spatial distribution of landscape surface resistance reflects the spatial movement trend of pollutants from different sources. The indices characterizing the promoting and hindering effects could be integrated to calculate the loss rate of pollutant load entering the lake from landscape units at different locations in the basin space.

Autochthonous sources and drought conditions drive anomalous oxygen-consuming pollution increase in a sluice-controlled reservoir in eastern China

Freshwater reservoirs are an important type of inland waterbody. However, they can suffer from oxygen-consuming pollution, which can seriously threaten drinking water safety and negatively impact the health of aquatic ecosystems. Oxygen-consuming pollutants originate from both allochthonous and autochthonous sources, and have temporally and spatially heterogeneous drivers. Datanggang Reservoir, China, is located in a small agricultural watershed; it is controlled by multiple sluice gates. Anomalously high oxygen consumption indicators were observed in this reservoir in March 2021. Here, it was hypothesized that autochthonous sources were the primary drivers of oxygen-consuming pollution in the reservoir under drought conditions. Datasets of water quality, precipitation, primary productivity, and sediment were used to analyze water quality trends in the reservoir and inflow rivers, demonstrating the effects of allochthonous inputs and autochthonous pollution. No correlation was found between reservoir oxygen consumption indicators and allochthonous inputs; reservoir oxygen consumption indicators and chlorophyll-a concentration were significantly positively correlated (p < 0.05). Substantially lower precipitation and higher water temperature and pH (compared to historical levels) were also observed before the pollution event. Therefore, during this period the hydrological conditions, water temperature, pH, and other variables caused by short-term drought conditions may have facilitated phytoplankton growth in the reservoir. This contributed to a large increase in autochthonous oxygen-consuming pollutants, as reflected by the abnormally high indicators. Sediments contaminated with organic matter may also have been an important contributor. As the effects of environmental management and pollution control continue to emerge, exogenous pollutants imported from the land to reservoirs are currently effectively controlled. However, endogenous pollutants driven by a variety of factors, such as meteorology and hydrology, will likely become the main drivers of short-term changes in oxygen-consuming pollution in freshwater reservoirs in the foreseeable future.

Nutrient potentiate the responses of plankton community structure and metabolites to cadmium: A microcosm study

Metal pollution is a worldwide concern and may pose risks to aquatic organisms, communities, and ecosystems. The toxic effects of metals at the organism level are relatively clear. However, their impacts at the community level are still poorly understood, especially with concurred eutrophication in surface water. In the present study, the effects of Cd on the plankton community structure and function under varying nutrient conditions were evaluated using a microcosm study. The employed concentrations of Cd and nutrient were based on the values currently measured in the freshwater ecosystem. For the plankton structure, our results showed that the Chl a concentration, the abundances of total phytoplankton, Cyanophyta, and Chlorophyta, and the abundance of Copepoda decreased by Cd consistently. The Cyanophyta Oscillatoria tenuis and Copepoda nauplius were the most sensitive species to Cd in the phytoplankton and zooplankton community, respectively. For the community effects, we found the inhibitory effects of Cd on the photosystem II (PSII) activity of phytoplankton community because of the consistent decrease in the chlorophyll fluorescence parameters (F_v/F_m, Y(Ⅱ), and ETR). Furthermore, the reductions of DOC and pH by Cd were only found in the high nutrient condition, which indicated that the toxic effects of Cd on the community structure and community metabolites were aggravated by the increased nutrient. This study emphasizes the importance of considering nutrient conditions when assessing the metal ecotoxicological effects at environmentally relevant concentrations.

Zooplankton Size Structure in Relation to Environmental Factors in the Xiangxi Bay of Three Gorges Reservoir, China

Body size is sensitive to environmental changes and one of the fundamental traits linking ecological functions. Size structure has been suggested as a useful indicator for environmental monitoring and assessment in aquatic ecosystems. However, the organisms’ size structure and the relationship with environmental factors remain seldom addressed in reservoir ecosystems. In this study, we investigated the size spectrum, size diversity of the zooplankton and their relationships with environmental conditions across nitrogen and phosphorus gradients in the Xiangxi Bay of Three Gorges Reservoir, China. We further tested the hypotheses that how nutrient and water temperature affect zooplankton size structure: nutrients indirectly affect zooplankton size spectrum and size diversity via phytoplankton (H1); increasing water temperature will reduce size diversity and result in a steeper size spectrum (H2); size diversity is a more robust metric indicating environment changes than the size spectrum in high dynamic ecosystems (H3). We found that both the size spectrum and size diversity showed high spatiotemporal dynamics. The size spectrum ranged from −3.373 to −0.984. The size diversity ranged from 0.631 to 3.291. Spatially, the lowest values of the size spectrum and size diversity were observed in the upstream areas of Xiangxi Bay, where nitrogen and phosphorus concentrations are high and low, respectively. And in temporal dynamics, lower values of the size spectrum and size diversity were generally observed in March and April. Further analyses based on the structural equation model (SEM) found a clear pathway revealing that nutrient variables affect the zooplankton abundance and size structure, supporting hypothesis H1. That is, dissolved inorganic nitrogen had an indirect effect on the zooplankton abundance, size spectrum, and size diversity by influencing the concentration of phytoplankton chlorophyll a. In addition, results of SEM suggested that increased water temperature had a significant negative effect on the size diversity but had non-significant effects on zooplankton abundance and size spectrum. This finding suggests that size diversity is a reliable and useful index in measuring the zooplankton size structure in reservoir ecosystems with high dynamics, which may have a wide application in environmental monitoring and assessment, especially for complex and dynamic aquatic ecosystems.

Hyper-Nutrient Enrichment Status in the Sabalan Lake, Iran

Lakes/reservoirs are rapidly deteriorating from cultural eutrophication due to anthropogenic factors. In this study, we aimed to (1) explore nutrient levels in the Sabalan dam reservoir (SDR) of northwest Iran, (2) determine the reservoir water fertility using the total phosphorus (TP) based and total nitrogen (TN) based Carlson trophic state indices, and (3) specify primary limiting factors for the reservoir eutrophication. Our field observations showed a state of hyper-nutrient enrichment in the SDR. The highest variation of TN in the reservoir water column happened when the reservoir was severely stratified (in August) while the highest variation of TP took place when the thermocline was attenuated with the deepening of the epilimnion (in October). Both TP and TN based trophic indicators classified the SDR as a hypereutrophic lake. TN:TP molar ratio averaged at the epilimnion indicated a P–deficiency in the reservoir during warm months whilst it suggested a co–deficiency of P and N in cold months. Given the hyper-nutrient enrichment state in the reservoir, other drivers such as water residence time (WRT) can also act as the main contributor of eutrophication in the SDR. We found that WRT in the SDR varied from hundreds to thousands of days, which was much longer than that of other reservoirs/lakes with the same and even much greater storage capacity. Therefore, both hyper-nutrient enrichment and WRT mainly controlled eutrophication in the reservoir. Given time consuming and expensive management practices for reducing nutrients in the watershed, changes in the SDR operation are suggested to somewhat recover its hypereutrophic state in the short-term. However, strategic long-term recovery plans are required to reduce the transition of nutrients from the watershed to the SDR.

Alterations to sediment nutrient deposition and transport along a six reservoir sequence

Reservoir presence and construction has become commonplace along rivers due to the multitude of ecosystem services they provide. Many services are well recognized, including the effectiveness of sequestering both sediments and sediment-bound nutrients such as silts and phosphorus (P). Reservoirs are also capable of transforming or sequestering significant quantities of nutrients with more complex biogeochemical pathways, like nitrogen (N). Reservoir assessments, independent of inflow-outflow models, have primarily focused on a small number of systems creating a growing need to understand how reservoirs function both individually and as reservoir sequences within large rivers and their watersheds. Models have simulated the overall efficiency and drivers of reservoir nutrient deposition, but few have considered how a sequence of reservoirs alters deposition as an interdependent watershed-sediment-transport-system. In this study, we collected sediment cores from a six-reservoir sequence along a 5th - 6th order stream receiving treated waters from a large metropolitan area in the subtropical southeastern United States. Paleolimnological studies of subtropical reservoirs are underrepresented and are needed to understand the history of reservoir development. Using paleolimnological techniques and a known 30 year flux of riverine nutrient loading from waste water treatment facilities, we compared nutrient deposition to reservoir morphological qualities and primary producer community structure during the past ~50 years. Our findings suggest phosphorus deposition is associated with reservoir order downstream of the primary nutrient source, nitrogen deposition is linked to reservoir water retention time, and N:P is most strongly linked to reservoir surface area and watershed population density. Our results were strongly influenced by a large upstream and metropolitan nutrient source, common in large rivers, but under different conditions of nutrient loading (i.e. nonpoint source), reservoirs may express other nutrient depositional patterns.