Drivers Of Water Quality Research Articles

Prediction of urban surface water quality scenarios using hybrid stacking ensembles machine learning model in Howrah Municipal Corporation, West Bengal

In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splines (MARS), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and their hybrid stacking ensemble RF (SE-RF), as well as stacking Cubist (SE-Cubist), to predict the distribution of water quality in the Howrah Municipal Corporation (HMC) area in West Bengal, India. Additionally, we employed the ReliefF and Shapley Additive exPlanations (SHAP) methods to elucidate the underlying factors driving water quality. We first estimated the water quality index (WQI) to model seven water quality parameters: total hardness (TH), pH, total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD), calcium (Ca), magnesium (Mg). Then six independent factors were utilized (i.e. Precipitation (Pr), Maximum Temperature (Tmax), Minimum Temperature (Tmin), Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and Total Dissolved Solids (TDS)) for predicting the WQI mapping through the different ML models. This study demonstrated that the SE-Cubist model outperforms other ML models. During the testing phase, it achieved the best modeling results with an R2 = 0.975, RMSE = 0.351, and MAE = 0.197. The ReliefF and SHAP analyses identified Pr and Tmax as the most significant factors influencing WQI within the study area.

Response processes to water quality changes driven by the dynamic regeneration of the surface microlayer film in slow-flowing freshwater bodies

The freshwater surface microlayer film (SMF) is a dynamically evolving micro-ecosystem closely related to water quality and microbial growth in water. However, the mechanisms of dynamic regeneration of SMF after their destruction and their impacts on the aquatic environment are still largely unknown. Herein, we modeled the dynamic processes of SMF destruction and recovery in the natural environment by constructing a water-SMF ecosystem. Chemical and biological changes in the dynamic regeneration of SMF were investigated. The results showed that the dynamic regeneration of SMF was very fast, with a regeneration thickness of up to 300±50 μm in two days, and at the same time, it would rapidly enrich organic matter and Fe ions. In addition, the cyclic dynamic regeneration process of SMF is significantly correlated with the surge metabolic growth of microorganisms associated with organic matter metabolism (e.g., Methylophilus, Nevskia) and iron-redox-associated (e.g., Curvibacter). The experimental results suggest that the microbial-mediated process of iron-organic matter coupled oxidation-reduction in SMF may be another important mechanism driving water quality changes. Overall, our study provides valuable theoretical guidance for predicting changes in water quality in slow-flowing water bodies.

Quantifying the relative importance of agricultural land use as a predictor of catchment nitrogen and phosphorus concentrations

Agriculture is a major source of nitrogen (N) and phosphorus (P) in freshwater ecosystems, and different management strategies exist to reduce farmland nutrient losses and thus mitigate freshwater eutrophication. The importance of agricultural sources of N and P as drivers of water quality is known to vary spatially, but quantification of the relative importance of the nutrient sources shaping this variability remains challenging, especially with reference to inputs from waste water treatment works. Addressing this knowledge gap is key for targeting management strategies to where they are likely to have the greatest effect. To advance our understanding in this area, this study assesses the impact of population density as a driver of the relative importance of agricultural land use for predicting mean Total Oxidised Nitrogen (TON) and Reactive Phosphorus (RP) concentrations in rivers in England, using two different data-driven, statistical approaches: a generalised linear model and random forest. Our results show that agricultural N and P sources dominate in catchments with low population density, where stream water concentrations are lower and waste water treatment works are numerous, but smaller in terms of the population equivalent served. Agricultural N and P sources are not important predictors of N and P in catchments with high population density, where contributions from waste water treatment works dominate. These results require cautious interpretation, as model validation outcomes show that high TON and RP concentrations are consistently underpredicted. Altogether, our results suggest that the relative contribution of agricultural sources may be overestimated in densely populated catchments, relative to point sources from waste water treatment works, and that management strategies to reduce the contribution of agriculture to N and P in rivers may be better targeted towards catchments with lower population density, as this is where agricultural land use is the primary source of N and P.

Self-purification in tropical upland rivers as affected by environmental factors: the case of Balili River in Benguet, Philippines

A major data gap in tropical river ecology is the limited documentation of natural water purification of upstream rivers. This study documented the improvement of water quality of downstream Balili River as affected by environmental factors and macrophyte diversity using a mixed-method approach (water quality assessment, plant inventory, field observation, canonical correspondence analysis, community interview). Results showed that the distance from pollution plays a significant role in the self-purification of the river while the floral diversity maintains the riparian from further contaminating the water and at the same time absorbing air pollutants. Generally, the pollution reduction and %change is exponential at 2 and 3 km from point source then taper at 4 and 5 km. At 3 km from the point source, 30% improvement in TDS, BOD and salinity, 25% for EC, 20% in TSS, 35% in WQI, 36% in CPI, and 50% for DO were noted. Key factors that influence the self-purification process in the river were distance from pollution source, elevation, human disturbance (as indicated by %tree canopy) and temperature. These findings highlight the importance of distance from pollution sources and floral diversity in driving water quality improvements, with implications for sustainable water resource management in tropical regions.

Hydro-meteorological factors and inflowing nutrients drive water quality in an impounded lake of China's South-to-North Water Diversion Project.

Freshwater lakes play a vital role in global hydrological and biogeochemical cycles, serving various functions and maintaining ecological balance. However, freshwater resources are more vulnerable to deterioration due to multiple stressors. Gaoyou Lake is one of the impounded lakes of the Eastern route of South-to-North Water Diversion Project in China, and as an important source of drinking water, the lake has been routinely monitored. Long-term monitoring of water quality in Gaoyou Lake showed that concentrations of nutrients and chlorophyll a as well as trophic state in the water column increased while water transparency decreased, indicating that the water quality has declined during the last 12years. Specifically, there was a notable and statistically significant increase in chlorophyll a concentrations, averaging an annual rate of 9.9%. Despite a slight decline in trophic level index until 2014, subsequent years saw an upward trend, ranging from 50.7 to 56.4 and indicating a light eutrophic state. Spatially, the western area displayed higher nutrient and chlorophyll a concentrations. Changes in hydro-meteorological variables and nutrients from inflowing rivers were the main factors correlated with water quality in Gaoyou Lake. Thus, pollution source apportionment and management within Huaihe River basin should be considered to reduce the external loadings of nutrients in order to improve and sustain long-term water quality.

Hydrology Predominates Over Harvest History and Landscape Variation to Control Water Quality and Disinfection Byproduct Formation Potentials in Forested Pacific Coast Watersheds.

Despite the global importance of forested watersheds as sources of drinking water, few studies have examined the effects of forestry on drinking water treatability. Relatively little is known about how the interaction between landscape variation and flow impacts source water quality and what this interaction means for drinking water treatability. To address this knowledge gap, we examined variability in sediments, dissolved organic matter, and disinfection byproduct formation potentials (DBP-FPs) across a range of flow conditions in four small watersheds with contrasting forest harvest histories and soil characteristics on Vancouver Island. Storm event-driven change in streamflow was the primary driver of water quality and DBP-FPs at our sites, with greater changes during stormflow (e.g., a 3-fold increase in dissolved organic carbon concentrations) than those across contrasting watersheds. Flow-driven changes in water quality and DBP-FPs were not significantly different across watersheds with different harvest histories; muted responses may be attributed to widespread second growth forests (i.e., recent harvesting effects may be confounded by historical harvest), forestry practices (e.g., slash burning), or soils with low organic carbon storage. This study suggests that variation in hydrology predominates over harvest history and soil characteristics to drive water quality and DBP-FPs on the east coast of Vancouver Island.

Study on the Spatiotemporal Variation in and Driving Mechanism of Water Quality in Baiyangdian Lake

Analyzing 165 data from five national control sites in Baiyangdian Lake, this study unveils its spatiotemporal pattern of water quality. Utilizing machine learning and multivariate statistical techniques, this study elucidates the effects of rainfall and human activities on the lake’s water quality. The results show that the main pollutants in Baiyangdian Lake are TN, TP, and IMN. Spatially, human activities are the main drivers of water quality, with the poorest quality observed in the surrounding village area. The temporal dynamics of water quality parameters exhibit three distinct patterns: Firstly, parameters predominantly influenced by point source pollution, like TN and NH4+-N, show lower concentrations during flood periods. Secondly, parameters affected by non-point source pollution, such as TP, show higher concentrations during flood periods. Thirdly, irregular variations were observed in pH, DO, and IMN. The evaluation of Baiyangdian Lake’s water quality based on the grey relationship analysis method indicates that its water quality is good, falling within Classes I and II. Time series analysis found that the dilution effect of rainfall and the scouring action of runoff dominate the temporal variation in water quality in Baiyangdian Lake. The major pollution sources were identified as domestic sewage, followed by agricultural non-point source pollution and the release of internal pollutants. Additionally, aquaculture emerged as a significant contributor to the Lake’s pollution. This research provides a scientific basis for controlling the continuous deterioration of Baiyangdian Lake’s water quality and restoring its ecological function.

“That’S What We Call ‘Aesthetics,’ Not A Public Health Issue”: The Social Construction Of Tap Water Mistrust In An Underbounded Community

In the United States, underbounded communities—urban disadvantaged unincorporated neighborhoods characterized by high-poverty and high residential density lying just outside the border of an incorporated municipality—often lack consistent access to clean and safe water. Poor water quality and inadequate infrastructure shape residents’ risk perceptions, often leading to tap water mistrust, but little is known about the broader social, political, and economic drivers of water quality in these settings or about how such drivers inform the social construction of risk across different stakeholder groups. Using an underbounded African-American/Hispanic neighborhood in the Tampa Bay metropolitan region as a case study, we illustrate how tap water mistrust is socially constructed and how these constructions contrast between neighborhood residents and government officials. Interviews and participant observation with these groups reveal that tap water mistrust emerges from the nexus of inadequate infrastructure, poor housing conditions, challenges relating to the affordability of piped water, and jurisdictional disconnects. We call for interventions that foreground participatory research, integrate social and cultural context into technical solutions, and prioritize equitability in decision making.

Analysis of water quality influencing factors under multi-source data fusion based on PLS-SEM model: An example of East-Liao River in China

Owing to alterations in the environment and human activities, the quality of surface water is declining. Despite a substantial number of studies on the factors that impact water quality, there is still a need for a better understanding of the major causes of water quality degradation. This study fused multi-source data using partial least squares structural equation modeling to evaluate the effects of weather, soil composition, and geographical features on the water quality of the East Liao River (ELR), Jilin Province, China. The impacts of land-use practices on water quality at different buffer scales were analyzed. The most significant correlation between land use and water quality was observed at a distance of 4 km. The severity of water pollution was significantly influenced by soil type, with a path coefficient of 0.689 (p < 0.001). Conversely, landscape factors exhibited a notable adverse effect, indicated by a path coefficient of −0.608 (p < 0.001). Additionally, meteorological factors exhibited a significant impact, with a path coefficient of 0.463 (p < 0.001). The indirect effects of landscape elements on water quality were also examined. Water quality could be indirectly influenced by landscape through soil factors, as evidenced by a path coefficient of −0.572 (p < 0.01). In this study, new ideas for studying water quality drivers using multi-source data fusion are introduced. Managers can leverage the findings of this study to improve their decision-making and effectively address water quality issues in ELR located in Jilin Province, China.

Rise and fall of an avian oasis: Tracking the impacts of land use change in a key coastal wetland in the world's largest megalopolis

Tropical estuaries support wetlands with high biodiversity value and provide essential ecosystem services. Many of these systems, however, are global hotspots for urbanization, particularly in Asia, where this process has resulted in rapid conversion, fragmentation, and degradation of 80 % of the wetlands along the East Asian-Australasian Flyway (EAAF) for migratory birds. However, the impact of such landscape scale changes on migratory birds at a key stopover site along the EAAF has not been evaluated. Here, we used long-term data (> 40 years) from Deep Bay (Hong Kong), a shallow embayment in the Pearl River estuary (PRE) in south China, to investigate the impact of urbanization on (1) catchment land use and water quality, and (2) its impact on the capacity of the wetland to support populations of migratory waterbirds. Deep Bay supports the largest remnants of mangrove forests and tidal mudflats in the PRE and is an important refueling ground along the EAAF. It is also part of the Greater Bay Area (GBA, population 86 million), the world's largest megalopolis. The principal component analysis highlighted the nutrient loading and cleansing effect from seasonal flushing as characterizing variation in water quality in Deep Bay over four decades. Major shifts in water quality during the study period were accompanied by contemporaneous changes in wintering waterbirds numbers. Prior to 2003, the main drivers of water quality were organic nutrients from animal husbandry. Following large-scale reclamation and increases in impervious surface cover post-2003, primarily due to the development of the megacity of Shenzhen (population 17.7 million), the hydrodynamics of Deep Bay have changed, with knock-on effects of sedimentation, input of pollutants, and changes in the macrobenthos. The wintering waterbirds community responded to these changes both in total numbers and the relative importance of feeding guilds. Where total bird counts are positively influenced by benthic biomass, and the benthic biomass is positively correlated to the water quality that is driven by the cleansing effect of tidal flushing. These anthropogenic drivers have negatively impacted migratory birds that use Deep Bay as a refueling station. This study highlights the need for policymakers to control these drivers and limit the level to which sensitive coastlines are urbanized.

Agricultural land use and morphometry explain substantial variation in nutrient and ion concentrations in lakes across Canada

Declines in freshwater quality resulting from anthropogenic nutrient input remain a persistent issue worldwide. Yet, we still have a limited understanding of the magnitude and scale at which most lakes have been affected by human activities, namely Land Use/Land Cover (LULC) alterations. In response, the NSERC Canadian Lake Pulse Network has compiled the first nationwide systematic database of lake quality metrics by surveying 664 lakes across 12 ecozones over 3 years. To assess the influence of catchment development on water quality and its spatial variation, we built generally additive models and multivariate regressions to quantify the association between watershed LULC and lake temperature, Secchi depth, as well as chlorophyll- a, limiting nutrient, and ion concentrations. We found that agricultural and urban land use explained the greatest proportion of variation in water quality among LULC categories ( R2 = 0.20–0.29). Overall, our study highlights that drivers of water quality are similar across regions; however, baseline conditions vary, so freshwater ecosystem management strategies must consider their geographic context to better predict where water quality thresholds will be surpassed.

Soil ecological stoichiometry synchronously regulates stream nitrogen and phosphorus concentrations and ratios

Whether and how to synchronously regulate stream water nitrogen (N) and phosphorus (P) concentrations and ratios is a major challenge for sustainable aquatic functions. Soil carbon (C):N:P ratios influence soil N and P stocks and biogeochemical processes that elicit subsequent substantial impacts on stream water N and P concentrations and ratios. Therefore, bridging soil and stream water with ecological stoichiometry is one of the most promising technologies for improving stream water quality. Here, we quantified the ecological stoichiometry of soil and stream water relationships across nine catchments. Soil C:P ratio was the main driver of water quality, showing negative correlations with stream water N and P concentrations, and positive correlations with the N:P ratio in P-limited catchments. We revealed that soil C:P ratios higher than 97.8 mol mol−1 are required to achieve the simultaneous regulation of stream water N and P concentrations below the eutrophication threshold and make algal growth P-limited. Furthermore, we found that the relationships between catchment landscape and soil ecological stoichiometry likely provided practical options for regulating soil ecological stoichiometry. Our work highlights that soil ecological stoichiometry can effectively indicate the amount and proportion of soil N and P losses, and can be intervened through rational landscape planning to achieve sustainable aquatic ecosystems in catchments.

High-frequency fecal indicator bacteria (FIB) observations to assess water quality drivers at an enclosed beach.

Fecal indicator bacteria (FIB) are monitored at beaches to assess water quality and associated health risk from recreational exposure. However, monitoring is generally conducted infrequently (i.e. weekly or less often), potentially leading to inaccurate assessment of water quality at a beach at the time of use. While some work has shown that FIB in marine environments can vary over short (e.g. subhourly) time scales, that work has been mainly focused on 'open' beaches. 'Enclosed' beaches-those that are partially barriered from exchange with offshore water and thus have different residence times and mixing dynamics in the nearshore environment-have been less studied. Here we present results from a high-frequency (once per 30 minutes) FIB sampling event conducted within a Central California, USA, harbor over 48 hours. FIB concentrations at this enclosed site were more variable at high-frequencies than what has been reported at open beach sites. Correlation and regression analyses showed FIB concentrations were most strongly associated with chlorophyll a concentration, turbidity, wind speed, and tide level. Results indicate the importance of measuring FIB concentrations and explanatory environmental parameters at appropriate temporal resolutions when conducting water quality monitoring or source tracking studies. Overall, this work highlights how high-frequency sampling can effectively provide information about water quality dynamics at beaches of interest.

Comparison between self‐organizing map and principal component analysis for water quality assessment and hydro‐geochemical characterization in dyke intruded complex geological settings

AbstractHydro‐geochemical characterization is challenging in dyke intruded complex geological setting. The comparison between self‐organizing map (SOM) classification and principal component analysis (PCA) is used for better understanding of hydrogeological process surrounding Amarpur dyke in Dhanbad district, Jharkhand. Total 30 water samples were collected and tested for 12 physicochemical parameters. The K‐means clustering with SOM grouped the water quality data into cluster 1 (46.67%, low mineralization), cluster 2 (36.67%, moderate mineralization) and cluster 3 (16.67%, high mineralization). The clusters of the majority of samples identified by PCA analysis is almost same as identified by SOM with little difficulty in discriminating between cluster 2 and cluster 3. The transformation of Ca‐HCO3 to Ca‐Cl‐SO4 occurred because of exchange of Ca2+ with Na+ adsorbed in the aquifer leading excess of sulphate ions. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality.

Dynamics in impervious urban and non-urban areas and their effects on run-off, nutrient emissions, and macroinvertebrate communities

Urbanization is a key factor driving water quality and biological communities in freshwater systems. While urbanization impacts on water resources were well-studied in urban areas, few studies have examined the growing impacts in non-urban areas. Here we investigate the spatiotemporal trends of urbanization using high-resolution impervious surface data of both urban and non-urban areas across Germany, and evaluate the urbanization impacts on changes of run-off, nutrient emissions, and macroinvertebrate communities. The emission changes were modelled using the MONERIS-PCRaster tool, while the macroinvertebrate community responses to urbanization were determined using Generalized Least Square models. We find that urbanization expanded by 3.2% from 2006 to 2015 nationwide, and primarily occurred in less-populated regions. The non-urban areas contributed 19.3–19.6% of the imperviousness expansion nationwide, which were equivalent to the ‘unaccounted’ increases of 15.5–15.9% of run-off, 10.6–11.0% of total nitrogen, and 12.5–12.9% of total phosphorus when only emissions from urban areas were modelled. Incorporating the emissions from both urban and non-urban areas revealed significant impacts of urbanization on macroinvertebrate community composition changes, with increases in more pollution-resistant and non-native species (particularly in large rivers), and reductions of more sensitive taxa. The community responses were jointly determined by local catchment characteristics and urbanization stressors. Our results suggest neglected effects of non-urban impervious surfaces and the importance of using impervious surface data instead of urban land classes to better represent urbanization processes and impacts in long-term planning and management of freshwater systems.

Macroinvertebrate Community Composition in Wetlands of the Desert Southwest is Driven by wastewater-associated Nutrient Loading Despite Differences in Salinity.

The online version contains supplementary material available at 10.1007/s13157-022-01647-2.

Carp stocking and climate change are potentially more important factors than nutrient enrichment driving water quality deterioration in subtropical freshwater lakes in China

AbstractFreshwater lakes across the world have undergone dramatic changes in biological components and water quality over the past several decades. Previous studies focused on potential drivers mainly on nutrient enrichment in the catchment. However, the relative importance of climate change and the top‐down cascade effects of fish stocking on water quality is not fully understood. Here, by compiling 155 lakes data with four periods of field investigation in subtropical lakes in China, we found no significant changes in water total nitrogen and total phosphorus in the past two decades. However, the phytoplankton abundance increased significantly, and the water clarity declined by 44.1%. We further found that carp stocking and climate change are potentially more important than nutrients driving water quality change, which is also evidenced in two lakes (Lake Donghu and Qiandao) with long‐term monitoring histories. Specifically, carp stocking can decrease the water clarity directly by stirring up sediment and indirectly by trophic cascade along the food web. For climatic factors, mean annual temperature (MAT) has a positive effect on phytoplankton abundance, while mean annual precipitation has a negative one, with climates overall having little effect on water clarity. In addition, nutrient enrichment and climate change also have strong interactions with carp stocking, which may enhance the top‐down effects on water quality. Our findings highlight that either MAT or carp stocking may become an overwhelming driver of water clarity decline, which provides new insights into the conservation strategy for water quality management in the subtropical lakes in China.

How anthropogenic factors influence the dissolved oxygen in surface water over three decades in eastern China?

The aquatic environment, linked to the sustainable development of human existence and ecological environment, is influenced comprehensively by anthropogenic and natural activities. In light of the continuously low concentration of dissolved oxygen (DO) in surface water in plain river networks and the phenomenon of delay in the improvement of surface water quality, this research aims to introduce a method that may be utilized in identifying the critical driving forces of DO in surface water and their lagging characteristics, which will contribute to the assessment and adjustment of water quality drivers and/or policies. The research analyzes a typical small watershed in a river network region of the Yangtze River Delta plain as the study area, collecting 35-year (1986–2020) data on several water quality parameters, decades of anthropogenic activities, and two natural factors. The time series methods of vector autoregressive model, Granger causality tests, forecast error variance decompositions, and impulse response functions (hereinafter referred to as VAR+), which are rarely applied in related research, were employed in this study and proved helpful for screening out pivotal drivers and capturing the lagging responses of DO level to driving forces at each lagged time. Results show that there exists a fluctuating drop in DO level in surface water from 1986 to 2008 and a steady climb from 2008 to 2020, with the lowest DO level being present in 2008. The impulsive perturbations of phosphate fertilizer consumption (PFC), motor vessel number, and precipitation minimally increase DO concentration, while the impulsive perturbation of gross domestic product (GDP) causes the sharpest drop in DO level. With these perturbations, the driving force of PFC persists for approximately seven years, and the driving forces of water temperature, permanent population, and GDP persist for only five years. Future research could be conducted with spatial hysteresis, selection of lag order and variable quantity within the model, as well as intermediate variables between drivers and DO level for exploring driving pathways and mechanisms.

Pattern recognition describing spatio-temporal drivers of catchment classification for water quality

Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder decision-making guided by those models. This paper explores whether the temporal variation of water quality drivers, such as season and flow, influence inconsistency in the classification, and whether variability is captured in spatial datasets that include original vegetation to represent the variability of biotic responses in areas mapped with the same land use. An Artificial Neural Network Pattern Recognition (ANN-PR) method is used to match catchments by Dissolved Inorganic Nitrogen (DIN) patterns in water quality datasets partitioned into Wet vs Dry Seasons and Increasing vs Retreating flows. Explainable artificial intelligence approaches are then used to classify catchments via spatial feature datasets for each catchment. Catchments matched for sharing patterns in both spatial data and DIN datasets were corroborated and the benefit of partitioning the observed DIN dataset evaluated using Kruskal Wallis method. The highest corroboration rates for spatial data classification with DIN classification were achieved with seasonal partitioning of water quality datasets and significant independence (p < 0.001 to 0.026) from non-partitioned datasets was achieved. This study demonstrated that DIN patterns fall into three categories suited to classification under differing temporal scales with corresponding vegetation types as the indicators. Categories 1 and 3 included dominance of woodlands in their datasets and catchments suited to classify together change depending on temporal scale of the data. Category 2 catchments were dominated by vineforest and classified catchments did not change under different temporal scales. This demonstrates that including original vegetation as a proxy for differences in DIN patterns will help guide future classification where only spatially mapped data is available for ungauged catchments and will better inform data needs for water modelling.

Key factors determining water quality, fish community dynamics, and the ecological health in an Asian temperate lotic system

Identifying the key determinants and their impacts on water quality and fish community structure is imperative to environmental assessment and ecosystem conservation. The main objectives of this study were to assess the factors driving water quality, fish assemblages, and ecological health in a temperate lotic ecosystem using a long-term (12 years) dataset. The results indicate that sewage treatment plants significantly negatively impact river water quality and are primary pollution sources. Temporal fluctuations showed that the summer monsoon adversely influenced TN and EC due to dilution effects while increasing the COD and TSS in the water. Annual variations and the Mann–Kendall test demonstrated increasing trends in COD, TN:TP ratio, and CHL-a but a decreasing trend of TP with the impoundment. Artificial barriers (weirs) in the river created more favorable algal growth conditions due to increased water residence time. COD and TSS levels in the river were significantly affected by soil erosion. The environmental disturbance measures index indicated that all sites were severely degraded. Fish composition analysis suggested that exotic fish species such as, Lepomis macrochirus and Micropterus salmoides, dominated the community structure and negatively impacted native species after the installation of weirs. Our findings indicate that fish guilds and the IBI model are controlled by nutrient enrichment, organic matter, algal production, and land use/land cover. Water quality governs the biological health of the river. Overall, evaluation of the river's ecological health based on the multi-metric WPI and the IBI revealed that the river has a “poor” to “very poor” ecological status. The outcomes of this study may aid policymakers in managing and restoring the river.