Water Quality Assessment Research Articles

THE ECOLOGICAL ASSESSMENT OF SMALL RIVERS IN UKRAINE UNDER CONDITIONS OF INTENSIVE WAR IMPACT

Aquatic ecosystems are perpetually exposed to anthropogenic sources of pollution, wherein the quality of subterranean and surface waters undergoes influence from both natural and anthropogenic processes. This interaction results in environmental predicaments and, occasionally, environmental crises. Small rivers, in particular, bear a pronounced impact from human interventions on the environment. The investigation undertaken by the researchers commenced in 2021 with the primary objective of scrutinizing the ecological condition of the Irsha River. Regrettably, due to the military aggression of the Russian Federation, the regions through which the river courses were temporarily occupied, subjecting it to considerable anthropogenic disturbances. Specifically, two bridges spanning the Irsha River in the Malyn area were demolished, and military operations transpired along the riverbanks, leading to the accumulation of damaged military equipment. Consequently, it was deemed imperative during the course of our study to conduct an analysis of the natural water in the Irsha River subsequent to the military activities. The outcomes of a comprehensive assessment of surface water quality, employing a graphical method, revealed that, during the period of 2021-2022, the river waters within the Irsha River exhibited an overall failure to meet quality standards in both monitoring locations. Elevated values of Maximum Permissible Concentration (MPC) exceeding the norm were noted for the Chemical Oxygen Demand (COD) indicator, the dissolved oxygen indicator, as well as the general iron and iron of permanganate oxidizability parameters in both samples. The highest degree of pollution was discerned in the Malyn Reservoir, whereas the Irsha Reservoir manifested comparatively lower pollution levels in the aquatic environment.

Ecological restoration for eutrophication mitigation in urban interconnected water bodies: Evaluation, variability and strategy

Many urban water bodies grapple with low flow flux and weak hydrodynamics. To address these issues, projects have been implemented to form integrated urban water bodies via interconnecting artificial lake or ponds with rivers, but causing pollution accumulation downstream and eutrophication. Despite it is crucial to assess eutrophication, research on this topic in urban interconnected water bodies is limited, particularly regarding variability and feasible strategies for remediation. This study focused on the Loucun river in Shenzhen, comprising an pond, river and artificial lake, evaluating water quality changes pre-(post-)ecological remediation and establishing a new method for evaluating the water quality index (WQI). The underwater forest project, involving basement improvement, vegetation restoration, and aquatic augmentation, in the artificial lake significantly reduced total nitrogen (by 43.58%), total phosphorus (by 79.17%) and algae density (by 36.90%) compared to pre-remediation, effectively controlling algal bloom. Rainfall, acting as a variable factor, exacerbated downstream nutrient accumulation, increasing total phosphorus by 4.56 times and ammonia nitrogen by 1.30 times compared to the dry season, and leading to algal blooms in the non-restoration pond. The improved WQI method effectively assesses water quality status. The interconnected water body exhibits obvious nutrient accumulation in downstream regions. A combined strategy that reducing nutrient and augmenting flux was verified to alleviate accumulation of nutrients downstream. This study provides valuable insights into pollution management strategies for interconnected pond-river-lake water bodies, offering significant reference for nutrient mitigation in such urban water bodies.

Impact of Sewage Discharge on The Community Structure and Gonad Maturity of Fish in The Way Awi River Bandar Lampung

Water pollution refers to the introduction of living organisms, substances, or other components by human activities that can degrade water quality or cause environmental harm. Gonad maturity level indicates the stage of readiness of prospective broodstock for the spawning process, assessed through visual and histological methods. This study aimed to assess water quality in the Way Awi River based on fish community structure and to evaluate the impact of sewage discharge on fish gonad maturity levels in Bandar Lampung's Way Awi River. Conducted between November and December 2023, this research sampled five points along the Way Awi River. Chemical analysis included pH, BOD, COD, and DO, while physical parameters such as temperature and TSS (Total Suspended Solids) were also analyzed. The findings indicate that the Way Awi River is moderately polluted, with no significant impact on the community structure, although there is a noticeable decrease in fish numbers from upstream to downstream. This study underscores the importance of understanding the risks associated with continuous disposal of waste into rivers.

Automatic Rainwater Quality Monitoring System Using Low-Cost Technology

Water quality monitoring serves diverse purposes, such as assessing water safety for drinking or agriculture. However, traditional methods are often time-consuming and costly. Additionally, assessing water quality on demand is not always feasible, and parameter variations are not consistently documented. With the advent of affordable equipment capable of efficient performance with minimal resources, remote monitoring sans physical access to the environment has become viable. This has led to reduced inspection and control costs. Understanding the applications, characteristics, and limitations of these devices is crucial for maximizing their benefits. Rainwater quality remains understudied in many areas, leaving its physical, chemical, and microbiological conditions unknown. These conditions are influenced by various atmospheric factors, including both human and natural activities. This research paper develops monitoring stations employing low-cost technology to assess rainwater quality in Morelia City. A prototype was developed based on low-cost technology implementation. Additionally, basic parameters measured include pH, total dissolved solids, turbidity, and temperature, utilizing an Arduino microcontroller for data processing. Data collected from these stations are transmitted via a Wi-Fi communication module to a web platform, enabling real-time visualization of measurements both spatially and temporally. Utilizing such a device offers several advantages compared to standard methods of assessing rainwater quality. It enables continuous measurements and provides a record of how rainwater quality is affected over time by human and natural activities.

Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies

AbstractIdentifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating water quality index (WQI). In this context, machine learning–based models are introduced to predict the WQ factors of Southeastern Black Sea Basin. The data comprising monthly samples of different WQ factors were collected for 12 months at eight locations of the Türkiye region in Southeastern Black Sea. The traditional evaluation with WQI of surface water was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) and pi-sigma artificial neural networks (PS-ANNs) were used to predict WQI, and the accuracy of the proposed algorithms were compared. SMN model and PS-ANNs were used for WQ prediction modeling for the first time in the literature. According to the results obtained from the proposed ANN models, it was found to provide a highly reliable modeling approach that allows capturing the nonlinear structure of complex time series and thus to generate more accurate predictions. The results of the analyses demonstrate the applicability of the proposed pi-sigma model instead of using other computational methods to predict WQ both in particular and other surface water resources in general.

Origin of spring water chemistry and its appropriateness for drinking requirements in a semi-arid environment (Ain Larbi, Algeria)

ABSTRACT A comparative assessment of water quality for drinking purposes was conducted in Ain Larbi area using the Weighted Arithmetic Water Quality Index (WA-WQI). The origin of minerals is assessed by a saturation index approach and the Gibbs diagram. Also, mineral correlation process and ion exchange mechanism are detected by the Principal Component Analysis (PCA). The PCA results indicate that the groundwater quality in the Ain Larbi study area primarily results from weathering and geochemical alteration of rock. The saturation index and Gibbs diagram support these findings. The WA-WQI was calculated for each station based on physicochemical parameters. During the wet season, three out of five classes of WQI were observed in the present study, including excellent (25%), good (70%), and poor (5%). In the dry season, the classes excellent (25%), good (30%), and poor (45%) were represented. The present situation demonstrates how hydrogeological regimes and water–rock interactions affect groundwater ion concentrations. Our results contribute to identifying drinking water requirements in the Ain Larbi region, promoting sustainable water management.

A Compact, Low-Cost, and Low-Power Turbidity Sensor for Continuous In Situ Stormwater Monitoring.

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist, their high cost prohibits their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor that is designed specifically for long-term stormwater in-field monitoring. Its low cost (USD 23.50) enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. By testing the sensor in the lab with standard turbidity solutions, the proposed low-cost turbidity sensor demonstrated a strong linear correlation between a low-cost sensor and a commercial hand-held turbidimeter. In the field, the low-cost sensor measurements were statistically significantly correlated to a standard high-cost commercial turbidity sensor. Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing that both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the GreenSpan sensor.

A novel method for assessing water quality status using MODIS images: A case study of large lakes and reservoirs in China

The changing climate and economic development have exerted significant pressure on the water quality of global lakes and reservoirs (hereinafter referred to as lakes). Existing sporadic in-situ monitoring limits a comprehensive understanding of water quality changes at large spatiotemporal scales. Although remote sensing techniques offer efficient water quality observations, they have primarily focused on monitoring single optical active substances, making it difficult to assess water quality changes caused by chemical indicators such as total phosphorus and total nitrogen. However, changes in chemical indicators within a certain range can cause responses to physical indicators, which are comprehensively reflected in the water-leaving reflectance. Based on this physical mechanism, this study obtained the water quality status categories (from excellent to severe pollution) by calculating the water quality index (WQI) and developed a new remote sensing assessment model of water quality status using moderate resolution imaging spectroradiometer (MODIS) imagery. This model, combining fractional-order derivatives and various-dimensional spectral indices, significantly enhanced the sensitivity of spectral reflectance to water quality states, achieving a maximum correlation of 0.72. To address the applicability of the model at large spatiotemporal scales, this study proposed a classification modeling scheme based on the optimal optical water categories, resulting in a 20.69%–34.48% improvement in model accuracy after classification. Additionally, this study proposed an improved ensemble learning model combining the concepts of Stacking and Bagging, achieving an average accuracy of 82% and enhancing accuracy by 5%–15% compared to the original single model. Subsequently, the model was used for the first time to assess the water quality status of 180 large lakes in China from 2000 to 2022. The results revealed that 76.11% of the lakes exhibited excellent and good water quality, with a spatial distribution pattern showing a “better in the west, worse in the east” pattern. Over the 23-year period, 28.33% and 58.89% of the lakes showed improvement and stability trends in water quality status, with stability and improvement predominating in the western and eastern regions, respectively. The research results provide technical support for the rapid assessment of water quality status and sustainable resource management, highlighting the potential of remote sensing technology in water quality monitoring.

Integrated water quality and performance assessment of seawater desalination plants along two coasts in Egypt

This study aims to assess feedwater, brine, and drinking water quality related to eight full-scale Egyptian seawater desalination plants. Collected water quality records from plants on the Mediterranean and Red Seas were monthly analyzed to identify different physical, chemical, and biological parameters during 2022. Statistical analysis and water quality indices (WQIs) approaches were applied to evaluate the various water quality states based on the available international and Egyptian guidelines. Analyzed seawater quality properties showed compliance with the most operating permissible limits except for high concentrations of turbidity, fats, oils, and greases, total bacteria in one plant, and iron, manganese, and silica in another plant. The monthly measured Weighted Arithmetic WQI classified 71 % and 100 % of desalination plants as having “Excellent” water rank according to the standard limits of the World Health Organization and the Egyptian Water Quality Standard, respectively. However, 65 % and 52 % of desalination plants were projected into the “good” water quality rank based on the Canadian Council of Ministers of the Environment WQI for the same guidelines. The results indicate that the feed intake type and the location of a desalination plant can optimize its performance efficiency, minimize contamination sources, and mitigate the potential environmental impacts of brine.

Estimated dissolved oxygen in a river located in the south of Rio Grande do Sul

The present study aimed to estimate the dissolved oxygen concentrations in the Piratini River Watershed (in Brazilian Portuguese: BHRP), located in the northwest region of the State of Rio Grande do Sul. DO is a crucial parameter in assessing water quality, as it is essential for the maintenance of aquatic ecosystems. To estimate this, global linear regression models were applied, statistical tools capable of evaluating the effect that one or more variables exert on another. The analyzed data come from collections carried out by Environmental Protection Foundation (FEPAM) between the years 2005 and 2013, period that, due to the rigorous protocol followed and sampling periodicity, revealed a robust and consistent data set. For the selection of parameters, we sought those that had the smallest sampling failures in the historical data series, namely: Thermotolerant Coliforms, Total Phosphorus, Ammoniacal Nitrogen and Total Nitrogen, Turbidity, Total Solids and Hydrogenionic Potential. After applying the linear regressions, the best global models generated were able to explain 91.90% of the variations in dissolved oxygen in the BHRP, once all the analyzed parameters were incorporated, consolidating the applicability of the method. It is also conclude that, with the exception of Total Phosphorus, all training periods are within the limits tolerated by legislation.

Accelerated urban river water quality assessment in Southern India using visible and near-infrared spectroradiometry

Optical indicators of water quality offer resource managers a swift and cost-effective means of monitoring water bodies. Remote sensing presents a promising avenue to overcome limitations, enabling comprehensive evaluation across geographical and temporal scales. This study presents initial findings utilizing hyperspectral sensing to analyze the water quality of the urban river Cooum in Chennai, Tamil Nadu. Lab analysis of 40 water samples included examination for key ions such as chloride, calcium, fluoride, sulphate, nitrate, and bicarbonates, as well as assessment of solids and colloidal components. Spectral analysis using a handheld Visible Near-Infrared (VNIR) spectroradiometer yielded reflectance curves, depicting the response of various water types and pollutants at different wavelengths. Integration of spectroradiometer data with water quality parameters facilitated the identification of empirical models tailored to the parameters of interest. The developed models exhibited an average R2 value of 0.7, underscoring the efficacy of hyperspectral remote sensing in rapidly assessing water quality in polluted rivers.

Assessment of Water Quality in Saline Tract of Purna Valley in the Vidarbha Region

Assessment of Water Quality in Saline Tract of Purna Valley in the Vidarbha Region

Unmanned surface vehicle for intelligent water quality assessment to promote sustainable human health

ABSTRACT Deteriorating water quality poses a substantial risk to human health, with billions at risk of waterborne diseases due to contamination. Insufficient water quality data augment risks as conventional monitoring methods lack comprehensive coverage. Technologies like the Internet of Things and machine learning offer real-time water quality monitoring and classification. IoT nodes often provide point data insufficient for monitoring the quality of entire water bodies. Remote sensing, though useful, has limitations such as measuring only optically active parameters and being affected by climate and resolution issues. To address these challenges, an unmanned surface vehicle named `AquaDrone' has been developed. AquaDrone traverses water bodies, collecting data of four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. The data is transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like data tables, trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time classification. A comparative analysis of various oversampling techniques has been conducted in the context of water quality classification. The AquaDrone offers a feasible solution for monitoring quality of small to medium-sized water bodies, crucial for safeguarding public health.

Analysis of Deforestation and Water Quality in the Talgua River Watershed (Honduras): Ecosystem Approach Based on the DPSIR Model

With increasing urbanization and industrialization, soil and forest resources are facing considerable pressure, as well as the demand for water for domestic, agricultural, and industrial activities. Therefore, it is essential to conduct regular assessments of water quality and ensure that water is consistently maintained in the context of ecosystem services (ESs). Our objective was to apply the driving forces–pressures–state–impacts–responses (DPSIR) model to understand the cause–effect relationships and interactions with anthropogenic pressures on deforestation and water quality in the Talgua River watershed and associated valley and plain areas in central-eastern Honduras. Physicochemical and microbiological analyses were conducted to determine the water quality index (NSF–WQI) and other contamination indexes. The results identified high contamination by coliforms, up to 920.00 NPM/100 mL, and high levels of contamination by organic matter (ICOMO, 0.65), solids (ICOSUS, 0.79), mineralization (ICOMI, 0.99), and the presence of bacteria (BPI, 8.50), as well as the development of eutrophication processes (ICOTRO), resulting in generally low water quality. These problems were caused by the socio-demographic and economic growth of the area, as well as the high demand for water, vulnerability to climate change, and intense agro-livestock and industrial activity, which led to deforestation processes, changes in land use, and contamination of natural water bodies that impacted the overexploitation of aquifers. After applying the DPSIR model, strategies are proposed for the management and administration of the watershed aimed at preserving the water, soils, and forest resources, while promoting stakeholder, business, education sector, and public administration participation.

Quantifying heavy metal and radionuclide contamination in fish and water proximal to a uranium tailings facility: A Linshui River basin investigation, China

BackgroundThe objective of this study is to evaluate the concentrations of heavy metals and radionuclides in water and fish samples collected from six designated sampling stations along the Linshui River, in close proximity to a Uranium Tailing Pond situated in China. Additionally, it seeks to estimate the bioaccumulation of heavy metals and conduct risk assessments, both carcinogenic and non-carcinogenic, for consumers. MethodsWater and fish samples (yellowhead catfish and common carp) were systematically collected from six stations along the river from January to June 2023, adhering to ethical standards and standard protocols for assessing water quality. Samples underwent chemical preparation and analysis for heavy metals using Graphite Furnace Atomic Absorption Spectrophotometry and Cold Vapor Atomic Absorption Spectroscopy, and for radionuclides using gamma spectrometry, with all methods validated for accuracy. ResultsThe water samples showed metal and radionuclide concentrations within acceptable limits, except for higher levels of U and Th compared to background values. Heavy metal concentrations were higher in common carp compared to yellowhead catfish, with both species exhibiting a similar trend. While non-carcinogenic health risk, as indicated by target hazard quotients, was low for consumers, the health risk data emphasized the carcinogenic threats posed by U238 and Th234. ConclusionsThe study highlights the importance of implementing comprehensive river restoration measures. Additionally, the bioconcentration factor values indicate minimal accumulation of heavy metals in the muscle tissue of fish.

Advanced Machine Learning and Water Quality Index (WQI) Assessment: Evaluating Groundwater Quality at the Yopurga Landfill

As industrial development and population growth continue, water pollution has become increasingly severe, particularly in rapidly industrializing regions like the area surrounding the Yopurga landfill. Ensuring water resource safety and environmental protection necessitates effective water quality monitoring and assessment. This paper explores the application of advanced machine learning technologies and the Water Quality Index (WQI) model as a comprehensive method for accurately assessing groundwater quality near the Yopurga landfill. The methodology involves selecting water quality indicators based on available data and the hydrochemical characteristics of the study area, comparing the performance of Decision Trees, Random Forest, and Xgboost algorithms in predicting water quality, and identifying the optimal algorithm to determine indicator weights. Indicators are scored using appropriate sub-index (SI) functions, and six different aggregation functions are compared to find the most suitable one. The study reveals that the Xgboost model surpasses Decision Trees and Random Forest models in water quality prediction. The top three indicator weights identified are pH, Manganese (Mn), and Nickel (Ni). The SWM model, with a 0% overestimation eclipsing rate and a 34% underestimation eclipsing rate, is chosen as the most appropriate WQI model for evaluating groundwater quality at the Yopurga landfill. According to the WQI results from the SWM aggregation function, the overall water quality in the area ranges from moderately polluted to slightly polluted. These assessment results provide a scientific basis for regional water environment protection.

Water Quality Assessment, Eutrophication, and Pollution Index in Lake Edku (Nile Delta of Egypt)

Water Quality Assessment, Eutrophication, and Pollution Index in Lake Edku (Nile Delta of Egypt)

Hydrochemical Characteristics and Evolution under the Influence of Multiple Anthropogenic Activities in Karst Aquifers, Northern China

The intensification of anthropogenic activities (agriculture, industry, and exploitation of water resources) during urbanization has posed significant challenges to the aquatic environment, particularly in karst regions. Karst aquifers are highly susceptible to surface contaminants and exhibit minimal natural remediation capabilities. Our understanding of the anthropogenic activities involved in these sensitive karst systems remains limited. To address this gap, we conducted a comprehensive study, collecting 285 groundwater samples in Feicheng, northern China, from 1996 to 2015. The overexploitation of karst groundwater has resulted in several concerns. The whole dataset was classified into four groups according to land use. Water quality assessments revealed a yearly decline, particularly in industrial and agricultural areas. The water chemistry transitioned from Ca-Mg-HCO3 to Ca-Mg-HCO3-SO4. Such evolution was attributed to natural hydrogeochemical processes, atmospheric precipitation, and anthropogenic inputs. Natural factors included water-rock interactions (the mineral dissolution) and ion exchange. Absolute principal component scores with multiple linear regression (APCS-MLR) were used to quantitatively estimate the sources of pollution. The results showed that hydrogeological settings (recharge, runoff, and discharge) were crucial in the hydrochemistry evolution of karst groundwater systems. In agricultural areas, it is recognized that much of the NO3− accumulation in aquifers came from upstream inputs in the groundwater system, not just irrigation and fertilization. Urban areas were affected by Cl− pollution, primarily due to domestic waste. Industrial regions of recharge zones were more susceptible to atmospheric precipitation and industrial waste, with pollutants infiltrating through rainfall and degrading water quality. Mining areas exhibit higher SO42− and lower pH due to the oxidation of sulfur-containing minerals. Therefore, the rapid response and low self-purification capacity of groundwater in karst regions necessitate caution in urban planning to mitigate impacts on these fragile systems.

Fate and partition of perfluoroalkyl acids (PFAAs) driven by particles in mangrove ecosystems: Mechanistic insights and environmental implications

A critical gap in understanding emerging pollutants in mangrove ecosystems should be addressed. Despite their ecological importance, the state of environmental quality in fragmented mangrove patches, especially emerging contaminants, remains largely uncharted. Our research presented an in-depth analysis of the distribution of perfluoroalkyl acids (PFAAs) across porewater, suspended particles, and sediment in these small, fragmented mangroves, revealing the mechanisms behind their distribution. The findings indicated that PFAA concentrations in sediments were generally lower than in suspended particles, except for C12–C14 perfluorinated carboxylic acids (PFCAs) and C4–C6 perfluorinated sulfonic acids (PFSAs). A comparison of contamination levels in different mangrove patches concurred with conventional water quality assessments and crab burrow surveys, showing that large artificially planted patches were less contaminated, while areas with intense human activity were more polluted. Notably, no significant differences were observed in the concentrations of long-chain PFAAs and PFCAs between patches (p > 0.1). Elevated crab burrowing activity and higher concentrations of perfluorobutanoic acid (PFBA) and perfluorohexane sulfonic acid (PFHxS) in suspended particles in low intertidal zones suggested that seafood, particularly crabs and shellfish, might be at risk of PFAA pollution. Rainfall-runoff was identified as the primary source of PFAA pollution in porewater and suspended particle systems in these areas. The transport of PFAAs and organic carbon in mangroves was influenced by the concentrations and properties of suspended particles. These insights were critical for developing targeted environmental management strategies to mitigate the impact of PFAAs and preserve the ecological integrity of mangrove ecosystems.

Impacts of hydrologic management on the eutrophication of shallow lakes in an intensive agricultural landscape (Saskatchewan, Canada)

Abstract Hydrologic management of shallow lakes is often undertaken to prevent fluctuations in lake level, and to ensure sufficient water volume for economic, domestic, and recreational uses, but there is inconsistent evidence of whether lake‐level stabilisation through hydrological management promotes or hinders eutrophication. Here we used multi‐proxy paleolimnological assessments of water quality (sedimentary carbon, nitrogen, total phosphorus, fossil pigments), and zooplankton community ecology (fossil Cladocera assemblages), combined with Landsat‐derived estimates of lake surface area in two shallow eutrophic lakes, in the Prairies of southern Saskatchewan, Canada, to quantify how 8 decades of contrasting hydrological management strategies (continuous or intermittent) affect primary production and phytoplankton composition. Analysis revealed that irregular hydrological management of Pelican Lake led to sharp increases in primary production concomitant with lake‐level decline. In contrast, continuously managed Buffalo Pound Lake, a drinking water reservoir for regional cities, exhibited slow, persistent eutrophication over decades despite active regulation of water levels. In both lakes, strong correlations of δ 15N values with pigments from diazotrophic cyanobacteria (canthaxanthin) showed that N2‐fixation increased during eutrophication irrespective of the timing of change. Finally, variation in fossil cladoceran density and composition reflected changes in pelagic and littoral habitats (e.g., reduced macrophyte cover) due to changes in both lake level and water quality. Basin comparison shows that while hydrologic management can moderate water quality degradation due to lake‐level change, it does not prevent eutrophication when nutrient influx remains high. Given that regional water availability is forecast to decline in coming decades, we anticipate that continued hydrological management will be unavoidable and will be unable to improve water quality unless nutrient influx is also controlled.