Modelling the spatial and seasonal variability of water quality for entire river networks: Relationships with natural and anthropogenic factors

Elevation-associated pathways mediate aquatic biodiversity at multi-trophic levels along a plateau inland river

Aquatic biodiversity plays a significant role in maintaining the ecological balance and the overall health of riverine ecosystems. Elevation is an important factor influencing biodiversity patterns. However, it is still unclear through which pathway elevation influences riverine biodiversity at different trophic levels. In this study, the elevation-associated pathways affecting aquatic biodiversity at different trophic levels were explored using structural equation modeling (SEM) and taking the Bayin River, China as the case. The results showed that the elevational patterns were different among aquatic organisms at different trophic levels. For macroinvertebrates and bacteria, the pattern was hump-shaped; while for phytoplankton and zooplankton, it was U-shaped. Building upon these observed elevational patterns, our investigation delved into the direct and indirect pathways through which elevation influences aquatic biodiversity. We found that elevation exerts an impact on aquatic biodiversity via indirect pathways. For all aquatic organisms investigated, the major pathway through which elevation influences biodiversity is mediated by water temperature and water quality. For aquatic organisms at higher trophic levels, like macroinvertebrates and zooplankton, the crucial pathway is also mediated by the landscape. The results of this study contributed to understanding the effects of elevation on aquatic organisms at different trophic levels and provided an important basis for the assessment of riverine biodiversity at large scales.

A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement

Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods resulted in differences in prediction accuracy and performance. Hence, a unified deep learning framework for water quality prediction was established in the paper, including data processing module, feature enhancement module, and data prediction module. In the established model, the data processing module based on wavelet transform method was applied to decomposing complex nonlinear meteorology, hydrology, and water quality data into multiple frequency domain signals for extracting self characteristics of data cyclic and fluctuations. The feature enhancement module based on Informer Encoder was used to enhance feature encoding of time series data in different frequency domains to discover global time dependent features of variables. Finally, the data prediction module based on the stacked bidirectional long and short term memory network (SBiLSTM) method was employed to strengthen the local correlation of feature sequences and predict the water quality. The established model framework was applied in Lijiang River in Guilin, China. The maximum relative errors between the predicted and observed values for dissolved oxygen (DO), chemical oxygen demand (CODMn) were 12.4% and 20.7%, suggesting a satisfactory prediction performance of the established model. The validation results showed that the established model was superior to all other models in terms of prediction accuracy with RMSE values 0.329, 0.121, MAE values 0.217, 0.057, SMAPE values 0.022, 0.063 for DO and CODMn, respectively. Ablation tests confirmed the necessity and rationality of each module for the established model framework. The established method provided a unified deep learning framework for water quality prediction.

Multi-spatial-scale land/use land cover influences on seasonally dominant water quality along Middle Ganga Basin

Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales.

Nitrate leaching potential from arable land in Germany: Identifying most relevant factors

Diffuse nitrogen losses from agriculture in Germany continue to cause regionally increased nitrate concentrations in groundwater. Groundwater quality monitoring cannot be a timely indicator of the effects of mitigation measures being applied in agriculture, due to frequently long transport routes and high residence times of the leachate. Instead, nitrate leaching potential is often determined at field and farm scale by monitoring soil mineral nitrogen contents at 0-90 cm depth in autumn (SMNa), i.e. before the start of the annual leachate period. In this study, we developed an understanding of the controls on the soil mineral nitrogen content at the start of winter. In an on-farm approach, extensive data was collected from 48 farms in five nitrate-sensitive regions in Germany from 2017 to 2020. From this data set, 25 management and site factors were evaluated with regard to their significance for SMNa by means of a random forest model. With the random forest regression, we identified the role of the factors on SMNa with an acceptable model accuracy with R2 = 0.56. The results show that the cultivated crop is the most important factor influencing SMNa. Potatoes, oilseed rape and maize produced the highest SMNas, whereas SMNas were lowest after spring barley, sugar beet and winter barley. Among site factors, soil type and texture as well as precipitation in October were most decisive. The effects of N fertilisation parameters such as rate and timing were masked by these site factors. The results show that the reduction of nitrogen-intensive crops in crop sequences can be a promising measure for the reduction of nitrate loads. On the other hand, our analysis makes clear that soil-related factors controlling nitrogen release and risk of leaching, as well as weather, can significantly mask the effect of cultivation.

The impacts of spatio-temporal variation of natural and agricultural influences on the environmental water quality in a fluvial-lacustrine watershed in China

Despite the significant impacts of natural factors such as rainfall, topography, soil type, and river network as well as agricultural activities on the environmental water quality, little is known about the influence of their temporal and spatial variations in a fluvial-lacustrine watershed. In this study, a whole process accounting method based the export coefficient model (WP-ECM) was first developed to quantify how natural factors and agricultural activities distribution influenced water quality. A case study was performed in a typical fluvial-lacustrine area - Dongting basin, China. The simulated results indicated that the natural factors can promote and inhibit the migration and transformation of agricultural pollutants generated from the watershed and the spatial distribution of the natural factors displayed high variability. It should be priority to monitor the areas with greater natural impact in the basin. Moreover, the cultivated land area and the number of pig-breeding were positively correlated with the pollutant discharge. From the perspective of the spatial distribution of comprehensive influence, the comprehensive high-impact areas are mainly distributed in the Dongting Lake district in 2005-2010 and in Xiang River watershed in 2010-2020. A key strategy for controlling or reducing the cultivated land area and the intensity of livestock breeding in these high-impacts areas is recommended to reduce the impact of the environmental water quality for the entire basin.

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

Anthropogenic loading of nitrogen to river systems can pose serious health hazards and create critical environmental threats. Quantification of the magnitude and impact of freshwater nitrogen requires identifying key controls of nitrogen dynamics and analyzing both the past and present patterns of nitrogen flows. To tackle this challenge, we adopted a machine learning (ML) approach and built an ML-driven representation that captures spatiotemporal variability in nitrogen concentrations at global scale. Our model uses random forests to regress a large sample of monthly measured stream nitrogen concentrations onto a set of 17 predictors with a spatial resolution of 0.5-degree over the 1990-2013, including observations within the pixel and upstream drivers. The model was validated with data from rivers outside the training dataset and was used to predict nitrogen concentrations in 520 major river basins of the world, including many with scarce or no observations. We predicted that the regions with highest median nitrogen concentrations in their rivers (in 2013) were: United States (Mississippi), Pakistan, Bangladesh, India (Indus, Ganges), China (Yellow, Yangtze, Yongding, Huai), and most of Europe (Rhine, Danube, Vistula, Thames, Trent, Severn). Other major hotspots were the river basins of the Sebou (Morroco), Nakdong (South Korea), Kitakami (Japan), and Egypt's Nile Delta. Our analysis showed that the rate of increase in nitrogen concentration between 1990s and 2000s was greatest in rivers located in eastern China, eastern and central parts of Canada, Baltic states, Pakistan, mainland southeast Asia, and south-eastern Australia. Using a new grouped variable importance measure, we also found that temporality (month of the year and cumulative month count) is the most influential predictor, followed by factors representing hydroclimatic conditions, diffuse nutrient emissions from agriculture, and topographic features. Our model can be further applied to assess strategies designed to reduce nitrogen pollution in freshwater bodies at large spatial scales.

Nitrogen and phosphorus spatio-temporal distribution and fluxes intensifying eutrophication in three tropical rivers of Côte d'Ivoire (West Africa)

Nutrient contamination assessments in the three West African tropical Comoé, Bandama, and Bia Rivers (Côte d'Ivoire) were performed from March 2016 to March 2018. Five stations per river were sampled. Nutrients spatio-temporal distributions were mapped and showed nitrogen concentrations (nitrite 0.001 to 0.025 mg/L NO₂^--N, and nitrate 0.26 to 3.60 mg/L NO₃^--N) increased significantly with rainfall contrary to phosphorus (0.01 to 0.12 mg/L P). The Chl-a and TSI_tsr data revealed the hypereutrophic status of rivers. Moreover, N:P mass ratio suggests nitrogen as the main limiting factor of primary production during the low (March) and high flow periods (October-November), while phosphorus is the limiting factor in June, at the high flow beginning. The land uses around watersheds were the main sources of phosphorus and nitrogen enhancing the rivers' eutrophication. Phosphorus and nitrogen fluxes were related to leaching river catchments and were significant sources of nutrients to the Atlantic Ocean.

Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach

Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

Decoding river pollution trends and their landscape determinants in an ecologically fragile karst basin using a machine learning model

Karst watersheds accommodate high landscape complexity and are influenced by both human-induced and natural activity, which affects the formation and process of runoff, sediment connectivity and contaminant transport and alters natural hydrological and nutrient cycling. However, physical monitoring stations are costly and labor-intensive, which has confined the assessment of water quality impairments on spatial scale. The geographical characteristics of catchments are potential influencing factors of water quality, often overlooked in previous studies of highly heterogeneous karst landscape. To solve this problem, we developed a machining learning method and applied Extreme Gradient Boosting (XGBoost) to predict the spatial distribution of water quality in the world's most ecologically fragile karst watershed. We used the Shapley Addition interpretation (SHAP) to explain the potential determinants. Before this process, we first used the water quality damage index (WQI-DET) to evaluate the water quality impairment status and determined that COD_Mn, TN and TP were causing river water quality impairments in the WRB. Second, we selected 46 watershed features based on the three key processes (sources-mobilization-transport) which affect the temporal and spatial variation of river pollutants to predict water quality in unmonitored reaches and decipher the potential determinants of river impairments. The predicting range of COD_Mn spanned from 1.39 mg/L to 17.40 mg/L. The predictions of TP and TN ranged from 0.02 to 1.31 mg/L and 0.25-5.72 mg/L, respectively. In general, the XGBoost model performs well in predicting the concentration of water quality in the WRB. SHAP explained that pollutant levels may be driven by three factors: anthropogenic sources (agricultural pollution inputs), fragile soils (low organic carbon content and high soil permeability to water flow), and pollutant transport mechanisms (TWI, carbonate rocks). Our study provides key data to support decision-making for water quality restoration projects in the WRB and information to help bridge the science:policy gap.

Random forest-based modeling of stream nutrients at national level in a data-scarce region

Nutrient runoff from agricultural production is one of the main causes of water quality deterioration in river systems and coastal waters. Water quality modeling can be used for gaining insight into water quality issues in order to implement effective mitigation efforts. Process-based nutrient models are very complex, requiring a lot of input parameters and computationally expensive calibration. Recently, ML approaches have shown to achieve an accuracy comparable to the process-based models and even outperform them when describing nonlinear relationships. We used observations from 242 Estonian catchments, amounting to 469 yearly TN and 470 TP measurements covering the period 2016-2020 to train random forest (RF) models for predicting annual N and P concentrations. We used a total of 82 predictor variables, including land cover, soil, climate and topography parameters and applied a feature selection strategy to reduce the number of dependent features in the models. The SHAP method was used for deriving the most relevant predictors. The performance of our models is comparable to previous process-based models used in the Baltic region with the TN and TP model having an R² score of 0.83 and 0.52, respectively. However, as input data used in our models is easier to obtain, the models offer superior applicability in areas, where data availability is insufficient for process-based approaches. Therefore, the models enable to give a robust estimation for nutrient losses at national level and allows to capture the spatial variability of the nutrient runoff which in turn enables to provide decision-making support for regional water management plans.

Land use, hydrology, and climate influence water quality of China's largest river

Influences of multiple environmental factors on water quality patterns is less studied in large rivers. Landscape analysis, multiple statistical methods, and the water quality index (WQI) were used to detect water quality patterns and influencing factors in China's largest river, the Yangtze River. Compared with the dry season, the wet season had significantly higher total phosphorus (TP), chemical oxygen demand (COD), total suspended solids (TSS), and turbidity (TUR). The WQI indicated "Moderate" and "Good" water quality in the wet and dry seasons, respectively. Compared with other sites, the upper reach sites that immediately downstream of the Three Gorges Dam had lower TP, TN, TSS and TUR in both seasons, and had lower and higher water temperature in the wet and dry seasons, respectively. Water quality patterns were mainly driven by heterogeneity in land use (i.e., wetland, cropland, and urban land), hydrology (i.e., water flow, water level), and climate (i.e., rainfall, air temperature). Water quality in the wet season was primarily driven by land use while the joint effect of land use and hydrology primarily drove in the dry season. Decision-makers and regulators of large river basin management may need to develop programs that consider influences from both human and natural drivers for water quality conservation.

Natural and anthropogenic factors and their interactions drive stream community integrity in a North American river basin at a large spatial scale

Urbanization, agriculture, and other human activities can exert considerable influence on the health and integrity of stream ecosystems. These influences vary greatly over space, time, and scale. We investigated trends in stream biotic integrity over 19 years (1997-2016) in relation to natural and anthropogenic factors in their spatial context using data from a stream biomonitoring program in a region dominated by agricultural land use. Macroinvertebrate and fish diversity and abundance data were used to calculate four multimetric indices (MMIs) that described biotic integrity of streams from 1997 to 2016. Boosted regression trees (BRT), a machine learning technique, were used to model how stream integrity responded to catchment-level natural and anthropogenic drivers including land use, human population density, road density, runoff potential, and natural factors such as latitude and elevation. Neither natural nor anthropogenic factors were consistently more influential on the MMIs. Macroinvertebrate indices were most responsive to time, latitude, elevation, and road density. Fish indices were driven mostly by latitude and longitude, with agricultural land cover among the most influential anthropogenic factors. We concluded that 1) stream biotic integrity was mostly stable in the study region from 1997 to 2016, although macroinvertebrate MMIs had decreased approximately 10% since 2010; 2) stream biotic integrity was driven by a mix of factors including geography, human activity, and variability over yearly time intervals; 3) MMI responses to environmental drivers were nonlinear and often nonmonotonic; 4) MMI composition could influence causal inferences; and 5) although our findings were mostly consistent with the literature on drivers of stream integrity, some commonly seen patterns were not evident. Our findings highlight the utility of large-scale, publicly available spatial data for understanding drivers of stream biodiversity and illustrate some potential pitfalls of large scale, integrative analyses.

A simpler statistical approach to estimate the allowable effluent discharge into a low monitored river network

The goal of the study was to illustrate how the variation in streamflow patterns affects the magnitude of pollutants that can be loaded into the hydrography in a more straightforward manner. For that, two basins in the Doce River basin (Brazil) were evaluated in order to estimate the variation in the allowed wastewater in streamflow units (m³s^-1) into the hydrography without impairing the rivers' water quality. The main water quality parameters of the basin were considered for the analysis: E. coli, total phosphorus (P), nitrate‑nitrogen (N-NO₃^-), and biochemical oxygen demand (BOD). Because the already existing E. coli in the system is too high, the allowable streamflow, considering this pollutant, was null. For N-NO₃^-_, the model was not valid because the concentration of it the wastewater is smaller than the allowed in the legislation. For last, considering P and BOD, it was observed that, for most gauges, it was possible to load more wastewater in the hydrography during wet months, especially when the effluent goes through some treatment. For example, considering P, a significant difference between the allowable pollutants in the wet season in comparison to the dry season, indicating that for one gauge, more wastewater is allowed in the dryer season. For BOD, contrarily, the allowable wastewater in the hydrography increased by about 50% in both seasons, without much variation. With that, we conclude that the dilution capacity in the waters is variable, so should be the allowed amount of pollutants into the river network throughout the year.

Diffuse phthalate acid esters losses induced from large amount of agricultural plastic film residues caused low risks for water quality in China during 1991-2017

Extensive application of agricultural plastic films has resulted in abundant film residues in farmlands. Phthalate acid esters (PAEs) are vital additives of the agricultural plastic film and are easily emitted into soils. However, spatio-temporal variations of diffuse PAEs loss to water bodies have not been explored in China. This study used an integrated estimation framework and high-resolution activity data to conduct a comprehensive inventory of diffuse PAEs loss associated with plastic films of six main crop types in China for 1991-2017. We found that the diffuse PAEs loss induced from agricultural plastic films increased 10.57-46.30 kg over the same time. Di-butyl phthalate (DBP) and bis(2-ethylhexyl) phthalate (DEHP) accounted for ~75% of the national total loss. High PAEs loss regions are mainly located in Eastern China, the Middle-Lower Yangtze Plain, and eastern Yunnan and Sichuan provinces. We proved that PAEs emission, agricultural film residues, surface runoff, precipitation, and soil organic carbon explained 19.64%, 17.50%, 15.45%, 12.88%, and 9.83% of the total variation, respectively. The potential ecological risks to the various aquatic species were assessed to be low. Overall, our results are valuable for addressing severe agricultural plastic film residues and associated pollutant emissions and losses in China.

Trends and influential factors of high ecological status mobility in Irish Rivers

The decline in high ecological water status in rivers is a significant concern in European countries. It is thus important to investigate the factors that cause sites to lose high status in order to undertake measures to protect and restore high status water quality. Analysis of 20 years of water quality data reveals strong mobility between high status and non-high status (especially good status) rivers. Associations between this mobility and socio-economic and physical environmental variables were estimated by multinomial logistic regression at national scale and regional scale. Based on reported changes in water quality status cross across 1990, 2000 and 2010, four classes of the mobility of high status were defined in this study: those sites that maintain high status (maintain), enter high status (enter), fluctuate between high and non-high status (fluctuate) and exit from high status (exit). The national results indicate that agricultural activity as indicated by variables representing intensity of livestock farming (organic nitrogen) and tillage farming (cereal share) and elevation had significant negative impacts on high status rivers. Meanwhile, significant differences in population density and septic tank density between 'exit', 'maintain', 'fluctuate' and 'enter' classes indicate that these factors played important roles in the stability of high status rivers. The regional outcomes reveal differential significant pressures across regions. For example, rainfall and elevation had positive impacts on high status rivers in the north-west region, while organic nitrogen had a negative effect in the south-west. This paper demonstrates the challenge in achieving the Water Framework Directive goal of maintaining high status rivers, given the sensitive and highly differentiated nature of areas that have lost high status or fluctuated in and out of high status. This paper also suggests the necessity for localised policies and mitigation measures.

Tracking nitrate sources in agricultural-urban watershed using dual stable isotope and Bayesian mixing model approach: Considering N transformation by Lagrangian sampling

A dual isotopes approach and the Bayesian isotope mixing model were applied to trace nitrogen pollution sources and to quantify their relative contribution to river water quality. We focused on two points to enhance the applicability of the method: 1) Direct measurement on the end-members to distinguish "sewage" and "manure" which used to be grouped in one pollution source as their isotope ranges overlap; 2) The Lagrangian sampling method was applied to consider the transport of nitrogen pollutants in a long river so that any fractionation process can be dealt with in the given Bayesian modeling framework. The results of the analysis confirmed the NO₃^- isotope composition in the river of interest to be within the range of NO₃^- with origins in "NH₄⁺ in fertilizer", "Soil N", and "Manure and sewage" pollution. This suggests that nitrogen pollution is mostly attributed to anthropogenic sources. The δ¹⁸O _NO3 value follows the range +2.5∼+15.0‰, implying that NO₃^- in the river is mainly derived from nitrification, and possible nitrification in groundwater or waterfront other than surface water. The ratio of the concentration of δ¹⁵N _NO3 to that of δ¹⁸O _NO3, and the corresponding regression equation indicates that the denitrification effect in surface water was insignificant during the study period. From the results of the contribution ratio of each source, improving the water quality of the discharge from the sewage treatment plants was proved to be the key factor to reduce nitrogen pollution in the river.

Regulation-based probabilistic substance quality index and automated geo-spatial modeling for water quality assessment

Natural environments are recognized as complex heterogeneous structures thus requiring numerous multi-scale observations to yield a comprehensive description. To monitor the current state and identify negative impacts of human activity, fast and precise instruments are in urgent need. This work provides an automated approach to the assessment of spatial variability of water quality using guideline values on the example of 1526 water samples comprising 21 parameters at 448 unique locations across the New Moscow region (Russia). We apply multi-task Gaussian process regression (GPR) to model the measured water properties across the territory, considering not only the spatial but inter-parameter correlations. GPR is enhanced with a Spectral Mixture Kernel to facilitate a hyper-parameter selection and optimization. We use a 5-fold cross-validation scheme along with [Formula: see text]-score to validate the results and select the best model for simultaneous prediction of water properties across the area. Finally, we develop a novel Probabilistic Substance Quality Index (PSQI) that combines probabilistic model predictions with the regulatory standards on the example of the epidemiological rules and hygienic regulations established in Russia. Moreover, we provide an interactive map of experimental results at 100 m² resolution. The proposed approach contributes significantly to the development of flexible tools in environment quality monitoring, being scalable to different standard systems, number of observation points, and region of interest. It has a strong potential for adaption to environmental and policy changes and non-unified assessment conditions, and may be integrated into support-decision systems for the rapid estimation of water quality spatial distribution.

Denitrification in Intrinsic and Specific Groundwater Vulnerability Assessment: A Review

Several groundwater vulnerability methodologies have been implemented throughout the years to face the increasing worldwide groundwater pollution, ranging from simple rating methodologies to complex numerical, statistical, and hybrid methods. Most of these methods have been used to evaluate groundwater vulnerability to nitrate, which is considered the major groundwater contaminant worldwide. Together with dilution, the degradation of nitrate via denitrification has been acknowledged as a process that can reduce reactive nitrogen mass loading rates in both deep and shallow aquifers. Thus, denitrification should be included in groundwater vulnerability studies and integrated into the various methodologies. This work reviewed the way in which denitrification has been considered within the vulnerability assessment methods and how it could increase the reliability of the overall results. Rating and statistical methods often disregard or indirectly incorporate denitrification, while numerical models make use of kinetic reactions that are able to quantify the spatial and temporal variations of denitrification rates. Nevertheless, the rating methods are still the most utilized, due to their linear structures, especially in watershed studies. More efforts should be paid in future studies to implement, calibrate, and validate user-friendly vulnerability assessment methods that are able to deal with denitrification capacity and rates at large spatial and temporal scales.

Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation

A systematic understanding of the spatial distribution of water quality is critical for successful watershed management; however, the limited number of physical monitoring stations has restricted the evaluation of spatial water quality distribution and the identification of features impacting the water quality. To fill this gap, we developed a modeling process that employed the random forest regression (RFR) to model the water quality distribution for the Taihu Lake basin in Zhejiang Province, China, and adopted the Shapley Additive exPlanations (SHAP) method to interpret the underlying driving forces. We first used RFR to model three water quality parameters: permanganate index (COD_Mn), total phosphorus (TP), and total nitrogen (TN), based on 16 watershed features. We then applied the built models to generate water quality distribution maps for the basin, with the COD_Mn ranging from 1.39 to 6.40 mg/L, TP from 0.02 to 0.23 mg/L, and TN from 1.43 to 4.27 mg/L. These maps showed generally consistent patterns among the COD_Mn, TN, and TP with minor differences in the spatial distribution. The SHAP analysis showed that the TN was mainly affected by agricultural non-point sources, while the COD_Mn and TP were affected by agricultural and domestic sources. Due to differences in sewage collection and treatment between urban and rural areas, the water quality in highly populated urban areas was better than that in rural areas, which led to an unexpected positive relationship between water quality and population density. Overall, with the RFR models and SHAP interpretation, we obtained a continuous distribution pattern of the water quality and identified its driving forces in the basin. These findings provided important information to assist water quality restoration projects.

Modeling water quantity and quality for a typical agricultural plain basin of northern China by a coupled model

Water crisis across the globe has placed high pressure on social development due to the need to balance the water consumption between sustainable economy and functioning ecosystem. Integrated process-based modeling has been reported as an effective tool to better understand the complex mechanisms of water issues on a basin scale. Considering that it is still relatively difficult to simulate the water quantity-quality processes simultaneously, this study proposed an integrated modeling framework by coupling a hydrological model with a water quality model. Taking the Xiaoqing River Basin in the Shandong Province of northern China as an example, this study coupled a distributed hydrological model, SWAT, with a one-dimensional hydrodynamic-water quality model, HEC-RAS, to investigate its ability to simulate water quality and quality at the basin scale. The coupling of the two models adopted the "output-input" scheme, where the runoff modeling results from SWAT are input into HEC-RAS for hydrodynamic and water quality simulations of the river channel. The results show that the SWAT model can adequately reproduce runoff with accepted accuracy for the calibration and validation periods with acceptable R² and Nash-Sutcliffe coefficients for the two hydrological stations. Further analysis also shows that the coupled model can simulate the concentration of ammonia nitrogen (NH₄-N) and the chemical oxygen demand (COD) in the middle and upper stream of the river for both low and high flow periods. The coupling of the hydrological and hydraulic models in this study provides a good tool for identifying the spatial patterns of the water pollutants over the basin and, thus, helps simplify precision water management.

A comprehensive modelling approach to understanding the fate, transport and potential risks of emerging contaminants in a tropical reservoir

We developed a comprehensive integrated water quality modeling approach towards a better understanding of the fate and transport of emerging contaminants and comprehensive assessment of their potential risks in a tropical reservoir. Two representative emerging contaminants, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET), were selected for this study. Unlike the traditional water quality modeling approach, the target emerging contaminants were modelled in four multi-compartments and coupled to a 3D-dimensional eutrophication model to investigate their interactions with other water quality state variables. First, the integrated model was calibrated and validated in four multi-compartments against an observed dataset in 2014. Subsequently, the correlation analysis between emerging contaminants and general water quality parameters were conducted. The potential ecological risks in this reservoir were also assessed via the trophic state index (TSI) and coupled to a species sensitivity distribution (SSD)-Risk Quotient (RQ) method. Finally, the model was applied to describe the dynamics of the two emerging contaminants and examine the direct and indirect influences of other environmental factors on their multi-compartment distributions in the aquatic environment. The comprehensive approach provides new insights into dynamic modeling of the fate and transport of emerging contaminants, their interactions with other state variables as well as an assessment of their potential risks in aquatic ecosystems.

Identifying Key Watershed Characteristics That Affect the Biological Integrity of Streams in the Han River Watershed, Korea

Understanding the complex human and natural processes that occur in watersheds and stream ecosystems is critical for decision makers and planners to ensure healthy stream ecosystems. This study aims to characterize the Han River watershed in Korea and extract key relationships among watershed attributes and biological indicators of streams using principal component analysis (PCA) and self-organizing maps (SOM). This study integrated watershed attributes and biological indicators of streams to delineate the watershed and stream biological status. Results from PCA strongly suggested that the proportions of watershed and riparian land use are key factors that explain the total variance in the datasets. Forest land in the watershed appeared to be the most significant factor. Furthermore, SOM planes showed that the biological indicators of streams have strong positive relationships with forest land, well-drained soil, and slope, whereas they have inverse relationships with urban areas, agricultural areas, and poorly drained soil. Hierarchical clustering classified the watersheds into three clusters, exclusively located in the study areas depending on the degree of forest, urban, and agricultural areas. The findings of this study suggest that different management strategies should be established depending on the characteristics of a cluster to improve the biological condition of streams.

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques

In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005-2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (-0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO₃, Mg, NO₂ + NO₃, SiO₂ and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO₄ are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO₂ in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin.

Relative performance of different data mining techniques for nitrate concentration and load estimation in different type of watersheds

The increasing availability of water quality datasets has led to a greater focus on hydrologic and water quality analysis, thus requiring more efficient and accurate modelling methods. Data mining techniques have been increasingly used for water quality analysis and prediction of the concentration and load of nitrogen pollutants instead of more traditional simulation methods. In this study, we tested the multilayer perceptron (MLP), k-nearest neighbor (k-NN), random forest, and reduced error pruning tree (REPTree) methods, along with the traditional linear regression, to predict nitrate levels based on long-term data from six watersheds with different land-use practices in the midwestern United States. Both the concentration and load results indicated that REPTree had the best performance, with an R² of 0.61-0.85 and a relative absolute error of <75.8%. The different watershed types, however, influenced the performance of the data mining methods, where all four methods showed a higher accuracy for urban dominant watershed and lower accuracy for agricultural and forest watersheds. Out of these four methods, classification tree methods (REPTree and RF) performed better than cluster methods (MLP and k-NN) for agricultural and forested watersheds. Our results indicated that both the data structure based on the dominant land use and type of algorithmic method should be carefully considered for selecting a data mining method to predict nitrate concentration and load for a watershed.

Patterns and predictions of drinking water nitrate violations across the conterminous United States

Excess nitrate in drinking water is a human health concern, especially for young children. Public drinking water systems in violation of the 10 mg nitrate-N/L maximum contaminant level (MCL) must be reported in EPA's Safe Drinking Water Information System (SDWIS). We used SDWIS data with random forest modeling to examine the drivers of nitrate violations across the conterminous U.S. and to predict where public water systems are at risk of exceeding the nitrate MCL. As explanatory variables, we used land cover, nitrogen inputs, soil/hydrogeology, and climate variables. While we looked at the role of nitrate treatment in separate analyses, we did not include treatment as a factor in the final models, due to incomplete information in SDWIS. For groundwater (GW) systems, a classification model correctly classified 79% of catchments in violation and a regression model explained 43% of the variation in nitrate concentrations above the MCL. The most important variables in the GW classification model were % cropland, agricultural drainage, irrigation-to-precipitation ratio, nitrogen surplus, and surplus precipitation. Regions predicted to have risk for nitrate violations in GW were the Central California Valley, parts of Washington, Idaho, the Great Plains, Piedmont of Pennsylvania and Coastal Plains of Delaware, and regions of Wisconsin, Iowa, and Minnesota. For surface water (SW) systems, a classification model correctly classified 90% of catchments and a regression model explained 52% of the variation in nitrate concentration. The variables most important for the SW classification model were largely hydroclimatic variables including surplus precipitation, irrigation-to-precipitation ratio, and % shrubland. Areas at greatest risk for SW nitrate violations were generally in the non-mountainous west and southwest. Identifying the areas with possible risk for future violations and potential drivers of nitrate violations across U.S. can inform decisions on how source water protection and other management options could best protect drinking water.

Isotopic biomonitoring of N pollution in rivers embedded in complex human landscapes

The dynamic and hierarchical structure of rivers, together with disruption of the natural river continuum by human activities, makes it difficult to identify and locate sources of nutrient pollution affecting receiving waters and observe its dispersion, thus impairing monitoring efforts. The identification of reliable indicators of anthropogenic nitrogen inputs in catchments is therefore key to achieving effective management of polluted rivers. We tested the capacity of N isotopic signatures (δ¹⁵N) of epilithon and snails to provide useful indications of organic and inorganic anthropogenic N inputs in three Mediterranean rivers differing in terms of surrounding land use and physicochemical conditions. We used a combined approach based on (i) analysis of nutrient concentrations in water, (ii) CORINE land cover classification and drainage patterns in catchments and (iii) isotopic analysis of river biota to verify whether isotopic variations were indicative of anthropic activities in the watershed, the associated alteration of water quality, and the consequent impact on snail abundance and diversity. Variation in the δ¹⁵N of epilithon within and between rivers reflected localised and diffuse N inputs from inorganic and organic sources. Negative epilithon δ¹⁵N values (<0‰) indicated inorganic pollution from agriculture. Values between 4‰ and 8‰ and those above 8‰ respectively indicated moderate organic pollution from urban areas, and high organic pollution, mostly from waste waters. The diversity and abundance of snails decreased with increasing water pollution. While their isotopic variations reflected between-river differences, they failed to indicate within-river variations in anthropogenic N inputs, since the proportion of epilithon in their diet varied along the rivers. Concluding, epilithon was a reliable indicator of anthropogenic N sources across a wide range of nutrient concentrations and anthropogenic inputs, and the proposed approach allowed us to determine the nature of nitrogen pollutants, their sources, location and dispersion along rivers embedded in complex human landscapes.

Coupled effects of climate variability and land use pattern on surface water quality: An elasticity perspective and watershed health indicators

Understanding the coupled effects of climate variability and land use on riverine nitrogen is essential for watershed management. The climate-water relationships for ammonium (NH₄-N) and nitrate (NO₃-N) were determined by an elasticity approach and then the watershed health index was estimated using the reliability, resilience, and vulnerability framework. These methods were applied to an in-situ monitoring dataset of N concentrations measured during 2010-2017 from nine sub-watersheds in the Jiulong River Watershed, China. The results showed that temperature and precipitation elasticity of NH₄-N and NO₃-N changed substantially among various land use patterns. The N concentrations were highly sensitive to extreme climate conditions, particularly at urban and agricultural sub-watersheds. The measure of risk indicators revealed that the watershed health index varied from good health to unhealthy status. Linear regression analysis was used to analyze the interactions among watershed characteristics, climate elasticity, and watershed health. Cropland and population had strong positive correlations with climate elasticity of NO₃-N. Forest and elevation had strong negative associations with climate elasticity of NO₃-N. Watershed health significantly declined with increasing proportion of cropland and population density. This study demonstrated that human-impacted watersheds were less healthy to unhealthy and tend to be more sensitive to climate variability than natural watersheds, which is useful for efforts aimed at improving watershed management.

Impact of Human Activities and Natural Processes on the Seasonal Variability of River Water Quality in Two Watersheds in Lampung, Indonesia

This study identified seasonal water quality characteristics in two adjacent mountainous rivers (Sangharus and Sekampung Hulu Rivers) in Lampung, Indonesia and determined the impacts of fertilizer application on river chemistry as a result of social forestry management. In 2016, we measured water chemistry and conducted a farmers’ questionnaire survey to obtain information on fertilizer application. The water quality results indicated that several parameters, including nitrate (NO3) and phosphate (PO4), were significantly higher in the Sangharus River than in the Sekampung Hulu River. In addition, several parameters were influenced by dilution from high river flow in the rainy season. Some parameters were likely influenced by the weathering of parent materials. By contrast, electrical conductivity (EC) and NO3 were higher in the rainy season, which was likely linked to the dominant timing of urea fertilizer application during this season. Despite the application of fertilizers in the watersheds, NO3 levels remained below the recommended standard. However, aluminum and iron concentrations were higher than the recommended level for drinking water, which was likely due to elevated soil erosion from improper land management. Therefore, we recommend that effective land management policies be implemented through the adoption of soil conservation practices for nutrient loss prevention.

Sources and transformations of anthropogenic nitrogen in the highly disturbed Huai River Basin, Eastern China

Due to serious nitrogen pollution in the Huai River, Eastern China, nitrogenous concentrations and dual stable isotopes (δ¹⁵N and δ¹⁸O) were measured to ascertain the sources and transformation of nitrogen in the Shaying River, the largest and most polluted tributary of the Huai River during the summer and winter seasons. Total nitrogen (TN), NO₃^-, and NH₄⁺ were significantly higher in winter, with values of 7.84 ± 3.44 mg L^-1, 2.31 ± 0.81 mg L^-1, and 3.00 ± 2.24 mg L^-1, respectively, while the highest nitrogen compounds occurred in the Jialu River, one of the tributaries of the Shaying River, in both summer and winter. Isotope characteristics of nitrate reveal that manure and sewage were the principal nitrate sources in both summer (62.44 ± 19.66%) and winter (67.33 ± 15.45%), followed by soil organic nitrogen, with 24.94 ± 15.52% in summer and 26.33 ± 9.45% in winter. Values of δ¹⁵N-suspended particulate nitrogen (SPN) ranged from 0.78 to 13.51%, revealing that point source from industrial and domestic sewage accounted for the largest input to SPN at most sites, whereas soil organic nitrogen and agricultural fertilizers were found in the Jialu River in both sampling periods. Point sources from septic/manure and household waste were the main contributors to ammonium in most river water samples in both summer and winter; most wastewater discharged into the river was untreated, which was one of the main reasons for the high level of ammonium in winter. Nitrogen pollution and the dams had an effect on N transformation in the river. Significant assimilation of NH₄⁺ and aerobic denitrification competed for NH₄⁺, resulting in the weakness of nitrification in the summer. Denitrification was also an important process of nitrate removal during the summer, whereas nitrification was a key N transformation process in the river in the winter time. To reduce nitrogen pollution and improve water quality, greater effort should be focused on the management of sources from urban input as well as on the improvement in sewage treatment.

Source and fate of nitrate in contaminated groundwater systems: Assessing spatial and temporal variations by hydrogeochemistry and multiple stable isotope tools

Intensive farming usually imply a degradation of groundwater resources worldwide. In particular, nitrate concentrations exceeding the 50 mg L^-1 limit established for drinking water pose the human health at risk. Therefore, assessing the impact of farming on groundwater, in terms of space and time, is of fundamental importance for policy decision makers and land managers. This study was aimed at assessing the nitrate source and fate in groundwater by combining hydrogeochemical and isotopic tools. The study area is located in the coastal plain of Arborea (Italy), a nitrate vulnerable zone (NVZ) due to intensive farming and animal husbandry (28,000 bovine livestock units). This area represents Mediterranean environments where groundwater resources are of relevant importance. In order to assess the present level of groundwater contamination and evaluate temporal variations, 6 hydrogeochemical surveys were carried out bimonthly at 13 sampling sites located in an area of 6 km². Additional samples were collected in specific surveys (82 water samples in total). The physical-chemical parameters, nitrogen species concentrations, major and minor components were determined, together with the boron, hydrogen, oxygen, nitrogen, and sulfur isotopic delta values. Results showed that groundwater samples were of meteoric origin, as indicated by the δ²H and δ¹⁸O_H2O values. The groundwater showed near-neutral pH (6.8-7.9) and different values of redox potential (0.2 ÷ 0.5 V), dissolved oxygen (2 ÷ 6 mg L^-1), electrical conductivity (0.8 ÷ 2.1 mS cm^-1) and chemical composition (sodium-chloride ÷ calcium-bicarbonate). Nitrate was not homogeneously distributed in groundwater, being observed a large range of concentrations, from <1 up to 162 mg L^-1. The above differences reflected the variability of groundwater circulation at small scale, which in turn controlled the interaction of water with different sediments (sands and/or clays). The shallow wells (about 5 m depth), screened in groundwater interacting mainly with sands, showed marked variations under the monitoring period, with nitrate peaks reflecting high leaching of nitrate in correspondence of fertilization and irrigation periods. The deeper wells (15-37 m depth) showed high to moderate nitrate when screened in sandy aquifer, whereas they had very low nitrate and relatively high ammonium (up to 1.8 mg L^-1) when clay layers were intercepted. Trends of δ¹⁵N and δ¹⁸O_NO3 values in the nitrate of shallow groundwater were related to the nitrate concentration observed over the monitored period. This dual isotope systematic showed a likely source of nitrate in groundwater from either manure or sewage. The δ¹¹B signature coupled to δ¹⁵N values clearly identified the manure as the predominant source of nitrate in the shallow and deep groundwater at Arborea. Relative enrichments in heavy nitrogen coupled to high concentrations of nitrate in groundwater were mainly attributed to volatilization processes occurring during the storage of animal wastes prior to application on the soil. Mixing of groundwater with seawater was not recognized, whereas mixing between shallow and deep groundwater may have occurred locally. Natural attenuation of nitrate contamination was observed in the deep groundwater interacting with lagoon clays rich in organic matter. Heterotrophic denitrification processes were highlighted by relatively high δ¹⁵N, δ¹⁸O_NO3, δ³⁴S and δ¹⁸O_SO4 values in association with low SO₄^2-/Cl^- and high HCO₃^-/SO₄^2- molar ratios observed in the groundwater with low concentration of nitrate. Results of this study showed that site-specific investigations are required for designing the best practices aimed at preserving groundwater resources under Mediterranean conditions. The spreading of animal waste on soils affects groundwater systems and likely extends over long time, strongly depending on the time lag of nutrient transport from source areas to receptor wells. Therefore, adequate monitoring of groundwater quality is required in areas of intensive farming.

Drivers of spatio-temporal patterns of salinity in Spanish rivers: a nationwide assessment

The salinization of freshwaters is a global water quality problem that leads to the biological degradation of aquatic ecosystems. However, little is known about the spatial extent of freshwater salinization and the relative contribution of each human activity (e.g. agriculture, urbanization, mining or shale-gas extraction). Here, we investigated environmental factors that explain spatio-temporal patterns of water salinity and examined the causes, the extent and the degree of salinization of Spanish rivers. Results showed a strong variation in water salinity among river typologies and between river reaches in good and poor ecological status according to the Water Framework Directive. The variation in water salinity was largely explained by a combination of natural (i.e. climate and geology) and anthropogenic (i.e. land use) factors. By contrast, land use factors as urbanization and agriculture were the main drivers of salinization, which affected more than one quarter of the rivers and streams in Spain, especially those in the most arid regions (central and southern regions) and in the main courses of the largest rivers such as the Ebro, Douro and Tajo rivers. The information provided here can be relevant to set priority regions and actions to ameliorate freshwater salinization.This article is part of the theme issue 'Salt in freshwaters: causes, ecological consequences and future prospects'.

Effect of residential development on stream phosphorus dynamics in headwater suburbanizing watersheds of southern Ontario, Canada

Suburban landscapes are known to have degraded water quality relative to natural settings, including increased total phosphorus (TP) levels; however, the effect of subdivision construction activities on stream TP dynamics are less understood. This study measured TP and its constituents particulate, dissolved organic, and dissolved inorganic phosphorus (PP, DOP, and DIP, respectively) in two headwater streams of contrasting urbanization activity to examine whether the land-use conversion process itself contributed to TP concentrations and export. The nested watershed undergoing significant active residential community construction contained large areas of cleared former agricultural field and associated sediment mounds with elevated soil TP (~1000 mg kg^-1), and twice as many stormwater management (SWM) ponds than the watershed with completed suburban communities. Daily stream sampling for six months revealed limited differences in TP between urbanized and urbanizing watersheds regardless of season or stream flow condition; however, the forms of TP varied significantly. The proportion of TP as DOP was consistently higher in the urbanizing stream relative to the urban stream, which was in line with significant decreases in DOP concentration as proportion of cleared former agricultural land decreased and density of SWM ponds increased. The DOP, and to a lesser extent DIP and PP, dynamics resulted in a 2.5× greater areal export of TP from a small watershed actively being suburbanized during the study period compared to the larger watershed with greater land urbanized 3-5 years ago. The results of this study suggest stream TP concentrations are relatively unresponsive to active versus established suburban cover, but the forms of TP can be quite different, and the period of home construction can increase phosphorus (P) delivery to and export through nearby streams. This information can aid land managers and urban planners update best management practices to mitigate the transfer of terrestrial P to the aquatic environment.

The cycle of nitrogen in river systems: sources, transformation, and flux

Nitrogen is a requisite and highly demanded element for living organisms on Earth. However, increasing human activities have greatly altered the global nitrogen cycle, especially in rivers and streams, resulting in eutrophication, formation of hypoxic zones, and increased production of N2O, a powerful greenhouse gas. This review focuses on three aspects of the nitrogen cycle in streams and rivers. We firstly introduce the distributions and concentrations of nitrogen compounds in streams and rivers as well as the techniques for tracing the sources of nitrogen pollution. Secondly, the overall picture of nitrogen transformations in rivers and streams conducted by organisms is described, especially focusing on the roles of suspended particle-water surfaces in overlying water, sediment-water interfaces, and riparian zones in the nitrogen cycle of streams and rivers. The coupling of nitrogen and other element (C, S, and Fe) cycles in streams and rivers is also briefly covered. Finally, we analyze the nitrogen budget of river systems as well as nitrogen loss as N2O and N2 through the fluvial network and give a summary of the effects and consequences of human activities and climate change on the riverine nitrogen cycle. In addition, future directions for the research on the nitrogen cycle in river systems are outlined.

Trends and seasonality of river nutrients in agricultural catchments: 18years of weekly citizen science in France

Agriculture and urbanization have disturbed three-quarters of global ice-free land surface, delivering huge amounts of nitrogen and phosphorus to freshwater ecosystems. These excess nutrients degrade habitat and threaten human food and water security at a global scale. Because most catchments are either currently subjected to, or recovering from anthropogenic nutrient loading, understanding the short- and long-term responses of river nutrients to changes in land use is essential for effective management. We analyzed a never-published, 18-year time series of anthropogenic (NO₃^- and PO₄^3-) and naturally derived (dissolved silica) riverine nutrients in 13 catchments recovering from agricultural pollution in western France. In a citizen science initiative, high-school students sampled catchments weekly, which ranged from 26 to 1489km². Nutrient concentrations decreased substantially over the period of record (19 to 50% for NO₃^- and 14 to 80% for PO₄^3-), attributable to regional, national, and international investment and regulation, which started immediately prior to monitoring. For the majority of catchments, water quality during the summer low-flow period improved faster than during winter high-flow conditions, and annual minimum concentrations improved relatively faster than annual maximum concentrations. These patterns suggest that water-quality improvements were primarily due to elimination of discrete nutrient sources with seasonally-constant discharge (e.g. human and livestock wastewater), agreeing with available land-use and municipal records. Surprisingly, long-term nutrient decreases were not accompanied by changes in nutrient seasonality in most catchments, attributable to persistent, diffuse nutrient stocks. Despite decreases, nutrient concentrations in almost all catchments remained well above eutrophication thresholds, and because additional improvements will depend on decreasing diffuse nutrient sources, future gains may be much slower than initial rate of recovery. These findings demonstrate the value of citizen science initiatives in quantifying long-term and seasonal consequences of changes in land management, which are necessary to identify sustainable limits and predict recovery timeframes.

Spatial prediction of water quality variables along a main river channel, in presence of pollution hotspots

In order to treat and evaluate the available data of water quality and fully exploit monitoring results (e.g. characterize regional patterns, optimize monitoring networks, infer conditions at unmonitored locations, etc.), it is crucial to develop improved and efficient methodologies. Accordingly, estimation of water quality along fluvial ecosystems is a frequent task in environment studies. In this work, a particular case of this problem is examined, namely, the estimation of water quality along a main stem of a large basin (where most anthropic activity takes place), from observational data measured along this river channel. We adapted topological kriging to this case, where each watershed contains all the watersheds of the upstream observed data ("nested support effect"). Data analysis was additionally extended by taking into account the upstream distance to the closest contamination hotspot as an external drift. We propose choosing the best estimation method by cross-validation. The methodological approach in spatial variability modeling may be used for optimizing the water quality monitoring of a given watercourse. The methodology presented is applied to 28 water quality variables measured along the Santiago River in Western Mexico.

Effects of sewage effluents and seasonal changes on the metabolism of three Atlantic rivers

Sewage inputs on fluvial ecosystems affect benthic communities and alter trophic networks resulting in changes on river functioning. Functional indicators (e.g. river metabolism) have been proposed as a valuable tool to evaluate ecosystem impairment. In the present study we monitored river metabolism in spring (few days after a major flood) and in summer (after 35days of low flow conditions) using both single-station and two-stations methods over a 24h period up and downstream of wastewater treatment plant (WWTP) effluents on three Atlantic river reaches located in northern Spain (Europe). Concurrently with river metabolism, we characterized environmental characteristics (flow, velocity, depth, pH, water temperature, nutrients, etc.), benthic macroinvertebrate communities and biofilm (algae and epilithic biomass). Ecosystem Respiration (ER₂₄) was similar at the different periods and locations, but Gross Primary Productivity (GPP) tended to decrease in impacted reaches (downstream WWTPs) and in summer (except in the Saja River). However, the balance of the metabolic processes showed a trend towards autotrophy in the largest river, while WWTP effluents increased its autotrophy. Chlorophyll a concentration was >4 times larger in spring than in summer in all river reaches, while epilithic biomass followed a similar but less obvious pattern. Increase of invertebrate scraper densities (mainly, Potamopyrgus antipodarum) seems to be a plausible explanation for biofilm biomass temporal patterns in all sites (higher in spring than in summer), altering GPP and ER₂₄ patterns. Thus, metabolism rates show different responses to WWTP effluents depending on season and on the relationships among functional and structural components, with special focus on the composition and structure of macroinvertebrate communities. Increasing our understanding of cause-effect relationships on the impairment of aquatic ecosystems needs to account for both structural and functional components and their interactions.

Developing an integrated 3D-hydrodynamic and emerging contaminant model for assessing water quality in a Yangtze Estuary Reservoir

An integrated 3D-hydrodynamic and emerging contaminant model was developed for better understanding of the fate and transport of emerging contaminants in Qingcaosha Reservoir. The reservoir, which supplies drinking water for nearly half of Shanghai's population, is located in Yangtze Delta. The integrated model was built by Delft3D suite, a fully integrated multidimensional modeling software. Atrazine and Bisphenol A (BPA) were selected as two representative emerging contaminants for the study in this reservoir. The hydrodynamic model was calibrated and validated against observations from 2011 to 2015 while the integrated model was calibrated against observations from 2014 to 2015 and then applied to explore the potential risk of high atrazine concentrations in the reservoir driven by agriculture activities. Our results show that the model is capable of describing the spatial and temporal patterns of water temperature, salinity and the dynamic distributions of two representative emerging contaminants (i.e. atrazine and BPA) in the reservoir. The physical and biodegradation processes in this study were found to play a crucial role in determining the fate and transport of atrazine and BPA in the reservoir. The model also provides an insight into the potential risk of emerging contaminants and possible mitigation thresholds. The integrated approach can be a very useful tool to support policy-makers in the future management of Qingcaosha Reservoir.

Relative linkages of stream water quality and environmental health with the land use and hydrologic drivers in the coastal-urban watersheds of southeast Florida

A systematic data analytics was employed to determine the relative linkages of stream water quality and environmental health with the land use and hydrologic drivers in the coastal-urban watersheds of southeast Florida. Power law-based partial least squares regression models were developed to reliably estimate the linkages by appropriately resolving multicollinearity (Nash-Sutcliffe efficiency = 0.72-0.95). The analytics indicated Everglades as the external and the largest source of total nitrogen (TN) in the coastal-urban streams for both wet (June-October) and dry (November-May) seasons. The "external driver" exhibited 1.5-2 times stronger control on stream TN than that of the watershed "land use," "hydrology," and the "upstream reach" contributions. In contrast, Everglades appeared to be a minor source of in-stream total phosphorus (TP), which was predominantly controlled by the internal watershed processes. TP was most strongly linked with the upstream reach concentrations and watershed land uses in the wet and dry seasons, respectively. Despite the predominantly built-up fraction (74%) of the study area, agricultural land was the most substantial watershed source of in-stream nutrients. The linkages of algal biomass (Chl a) with the drivers indicated TP as the limiting nutrient. Stream dissolved oxygen was most strongly influenced by the adjacent groundwater depth and watershed land uses, respectively, in the wet and dry seasons. The estimated relative linkages and insights would be useful to identify the management targets and priorities to achieve healthy coastal-urban stream ecosystems in southeast Florida and around the world.

Tracking Nitrogen Sources, Transformation, and Transport at a Basin Scale with Complex Plain River Networks

This research developed an innovative approach to reveal nitrogen sources, transformation, and transport in large and complex river networks in the Taihu Lake basin using measurement of dual stable isotopes of nitrate. The spatial patterns of δ¹⁵N corresponded to the urbanization level, and the nitrogen cycle was associated with the hydrological regime at the basin level. During the high flow season of summer, nonpoint sources from fertilizer/soils and atmospheric deposition constituted the highest proportion of the total nitrogen load. The point sources from sewage/manure, with high ammonium concentrations and high δ¹⁵N and δ¹⁸O contents in the form of nitrate, accounted for the largest inputs among all sources during the low flow season of winter. Hot spot areas with heavy point source pollution were identified, and the pollutant transport routes were revealed. Nitrification occurred widely during the warm seasons, with decreased δ¹⁸O values; whereas great potential for denitrification existed during the low flow seasons of autumn and spring. The study showed that point source reduction could have effects over the short-term; however, long-term efforts to substantially control agriculture nonpoint sources are essential to eutrophication alleviation for the receiving lake, which clarifies the relationship between point and nonpoint source control.

Modelling macroinvertebrate and fish biotic indices: From reaches to entire river networks

We modelled three macroinvertebrate (IASPT, EPT number of families and LIFE) and one fish (percentage of salmonid biomass) biotic indices to river networks draining a large region (110,000km²) placed in Northern and Eastern Spain. Models were developed using Random Forest and 26 predictor variables (19 predictors to model macroinvertebrate indices and 22 predictors to model the fish index). Predictor variables were related with different environmental characteristics (water quality, physical habitat characteristics, hydrology, topography, geology and human pressures). The importance and effect of predictors on the 4 biotic indices was evaluated with the IncNodePurity index and partial dependence plots, respectively. Results indicated that the spatial variability of macroinvertebrate and fish indices were mostly dependent on the same environmental variables. They decreased in river reaches affected by high mean annual nitrate concentration (>4mg/l) and temperature (>12°C), with low flow water velocity (<0.4m/s) and aquatic plant communities being dominated by macrophytes. These indices were higher in the Atlantic region than in the Mediterranean. This study provides a continuous image of river biological communities used as indicators, which turns very useful to identify the main sources of change in the ecological status of water bodies and assist both, the integrated catchment management and the identification of river reaches for recovery.

Water management practices exacerbate nitrogen retention in Mediterranean catchments

Nitrogen (N) retention sensu lato refers to all processes preventing new reactive nitrogen brought into watersheds through agricultural or industrial activities to be exported by river systems to the sea. Although such processes protect marine systems from the threat of eutrophication and anoxia, they raise other environmental issues, including the acidification of soils, the emission of ammonia and greenhouse gases, and the pollution of aquifers. Despite these implications, the factors involved in N retention are still poorly controlled, particularly in arid and semi-arid systems. The present study evaluates the N fluxes of 38 catchments in the Iberian Peninsula with contrasting climatic characteristics (temperate and Mediterranean), land uses, and water management practices. This diversity allows addressing the contribution of physical and socioecological factors in N retention, and more specifically, exploring the relation between N retention and water regulation. We hypothesise that the extreme flow regulation implemented in the Mediterranean enhances the high N retention values associated with arid and semi-arid regions. The results show that reservoirs and irrigation channels account for >50% of the variability in N retention values, and above a certain regulation threshold, N retention peaks to values >85-90%. Future climate projections forecast a decrease in rainfall and an increase in agricultural intensification and irrigation practices in many world regions, most notably in arid and semi-arid areas. Increased water demand will likely lead to greater flow regulation, and the situation in many areas may resemble that of Iberian Mediterranean catchments. High N retention and the associated environmental risks must therefore be considered and adequately addressed.

Micro and Macroscale Drivers of Nutrient Concentrations in Urban Streams in South, Central and North America

Global metrics of land cover and land use provide a fundamental basis to examine the spatial variability of human-induced impacts on freshwater ecosystems. However, microscale processes and site specific conditions related to bank vegetation, pollution sources, adjacent land use and water uses can have important influences on ecosystem conditions, in particular in smaller tributary rivers. Compared to larger order rivers, these low-order streams and rivers are more numerous, yet often under-monitored. The present study explored the relationship of nutrient concentrations in 150 streams in 57 hydrological basins in South, Central and North America (Buenos Aires, Curitiba, São Paulo, Rio de Janeiro, Mexico City and Vancouver) with macroscale information available from global datasets and microscale data acquired by trained citizen scientists. Average sub-basin phosphate (P-PO₄) concentrations were found to be well correlated with sub-basin attributes on both macro and microscales, while the relationships between sub-basin attributes and nitrate (N-NO₃) concentrations were limited. A phosphate threshold for eutrophic conditions (>0.1 mg L^-1 P-PO₄) was exceeded in basins where microscale point source discharge points (eg. residential, industrial, urban/road) were identified in more than 86% of stream reaches monitored by citizen scientists. The presence of bankside vegetation covaried (rho = –0.53) with lower phosphate concentrations in the ecosystems studied. Macroscale information on nutrient loading allowed for a strong separation between basins with and without eutrophic conditions. Most importantly, the combination of macroscale and microscale information acquired increased our ability to explain sub-basin variability of P-PO₄ concentrations. The identification of microscale point sources and bank vegetation conditions by citizen scientists provided important information that local authorities could use to improve their management of lower order river ecosystems.

Water quality assessment of a small peri-urban river using low and high frequency monitoring

The biogeochemical behaviors of small rivers that pass through suburban areas are difficult to understand because of the multi-origin inputs that can modify their behavior. In this context, a monitoring strategy has been designed for the Marque River, located in Lille Metropolitan area of northern France, that includes both low-frequency monitoring over a one-year period (monthly sampling) and high frequency monitoring (measurements every 10 minutes) in spring and summer. Several environmental and chemical parameters are evaluated including rainfall events, river flow, temperature, dissolved oxygen, turbidity, conductivity, nutritive salts and dissolved organic matter. Our results from the Marque River show that (i) it is impacted by both urban and agricultural inputs, and as a consequence, the concentrations of phosphate and inorganic nitrogen have degraded the water quality; (ii) the classic photosynthesis/respiration processes are disrupted by the inputs of organic matter and nutritive salts; (iii) during dry periods, the urban sewage inputs (treated or not) are more important during the day, as indicated by higher river flows and maximal concentrations of ammonium; (iv) phosphate concentrations depend on oxygen contents in the river; (v) high nutrient concentrations result in eutrophication of the Marque River with lower pH and oxygen concentrations in summer. During rainfalls, additional inputs of ammonium, biodegradable organic matter as well as sediment resuspension result in anoxic events; and finally (vi) concentrations of nitrate are approximately constant over the year, except in winter when higher inputs can be recorded. Having better identified the processes responsible for the observed water quality, a more informed remediation effort can be put forward to move this suburban river to a good status of water quality.