Existing Water Quality Monitoring Research Articles

Water Quality Analysis Through Satellite Images

This project report presents a comprehensive study on water quality analysis using satellite images. The increasing global demand for water resources and the growing impact of human activities on water ecosystems underscore the need for efficient and scalable methods for water quality assessment. Leveraging remote sensing technology, particularly satellite imagery, provides a promising solution for monitoring large water bodies at a regional and global scale. The project focuses on the development and implementation of a robust methodology for water quality analysis through the utilization of satellite images. Key objectives include the extraction of relevant water quality indicators such as turbidity, chlorophyll-a concentration, and dissolved organic matter. Image processing techniques, including spectral analysis and machine learning algorithms, are employed to derive quantitative information from satellite data. The study employs a multi-sensor approach, utilizing data from various satellite platforms, to enhance the accuracy and reliability of the water quality assessments. Additionally, ground truth data collected from in situ measurements and laboratory analysis are integrated into the analysis to validate and calibrate the remote sensing results. The project contributes to the advancement of remote sensing applications in environmental monitoring, providing a cost-effective and timely method for water quality assessment. The findings of this research have implications for water resource management, environmental conservation, and policy formulation. Furthermore, the developed methodology can be applied to monitor changes in water quality over time, supporting sustainable water resource management practices. The report concludes with recommendations for further research and the potential integration of the developed methodology into existing water quality monitoring systems. Overall, the project highlights the significance of satellite-based approaches in addressing contemporary challenges related to water quality on a global scale.

Geological and anthropogenic contributions of metal(loid)s in the artisanal and small-scale mining-impacted Ocoña watershed of Arequipa, Peru

Strategies to resolve conflicts around water supply and quality in mining-impacted communities include a better understanding of the role and mitigation of mining activities proximal to surface water bodies. The Ocoña watershed of Arequipa (Peru) is one such region where there are conflicts between artisanal miners, fishers, and agriculture over local water resources. The aim of this study is to develop a workflow that leverages existing water quality monitoring and geological mapping to identify major processes that adversely impact water quality with a focus on the relative contribution of mining-related activities on the Ocoña watershed. This study integrates a statistical evaluation of water chemistry data, concentration-discharge relationships, and detailed mineralogy with geochemical modeling to identify sources and mechanisms of contaminant contributions to surface waters in the Ocoña watershed. This revealed that primary deposit mineralogy and seasonal variations exert direct effects on water quality in this mining-active watershed. Multivariate statistical analysis reveals two distinct hydrogeochemical signatures in Ocoña River water that likely represent natural geothermal activity in the northern portion and acid mine drainage contribution in the southern portion of the watershed. In both cases, anthropogenic activities such as mining and agricultural may increase contaminant mobilization. Interestingly, despite perceived challenges, water quality in the Ocoña watershed is better than that of neighbors potentially due to pH-controlled precipitation of Fe and resultant sorption of metals onto precipitants. However, copper released from acid mine drainage, and in particular peak concentrations during the high flow season, exert a potential for adverse ecotoxicological effects. This workflow could potentially be extrapolated to other regions with similar available data to better identify the relative contributions of natural and anthropogenic pressures to the release of toxic compounds in watersheds of concern.

Mapping Irish Water Bodies: Comparison of Platforms, Indices and Water Body Type

Accurate monitoring of water bodies is essential for the management and regulation of water resources. Traditional methods for measuring water quality are always time-consuming and expensive; furthermore, it can be very difficult capture the full spatiotemporal variations across regions. Many studies have shown the possibility of remote-sensing-based water monitoring work in many areas, especially for water quality monitoring. However, the use of optical remotely sensed imagery depends on several factors, including weather, quality of images and the size of water bodies. Hence, in this study, the feasibility of optical remote sensing for water quality monitoring in the Republic of Ireland was investigated. To assess the value of remote sensing for water quality monitoring, it is critical to know how well water bodies and the existing in situ monitoring stations are mapped. In this study, two satellite platforms (Sentinel-2 MSI and Landsat-8 OLI) and four indices for separating water and land pixel (Normalized Difference Vegetation Index—NDVI; Normalized Difference Water Index—NDWI; Modified Normalized Difference Water Index—MNDWI; and Automated Water Extraction Index—AWEI) have been used to create water masks for two scenarios. In the first scenario (Scenario 1), we included all pixels classified as water, while for the second scenario (Scenario 2) accounts for potential land contamination and only used water pixels that were completed surround by other water pixels. The water masks for the different scenarios and combinations of platforms and indices were then compared with the existing water quality monitoring station and to the shapefile of the river network, lakes and coastal and transitional water bodies. We found that both platforms had potential for water quality monitoring in the Republic of Ireland, with Sentinel-2 outperforming Landsat due to its finer spatial resolution. Overall, Sentinel-2 was able to map ~25% of the existing monitoring station, while Landsat-8 could only map ~21%. These percentages were heavily impacted by the large number of river monitoring stations that were difficult to map with either satellite due to their location on smaller rivers. Our results showed the importance of testing several indices. No index performed the best across the different platforms. AWEInsh (Automated Water Extraction Index—no shadow) and Sentinel-2 outperformed all other combinations and was able to map over 80% of the area of all non-river water bodies across the Republic of Ireland. While MNDWI was the best index for Landsat-8, it was the worst performer for Sentinel-2. This study showed that optical remote sensing has potential for water monitoring in the Republic of Ireland, especially for larger rivers, lakes and transitional and coastal water bodies.

Water Quality Assessment and Monitoring in Pakistan: A Comprehensive Review

Water quality has been a major problem in Pakistan owing to a mix of factors such as population expansion, industrial units in urban areas, and agricultural activities. The purpose of this research is to conduct a comprehensive evaluation of water quality monitoring and assessment in Pakistan. The article begins by examining the water sources of Pakistan (i.e., surface water, groundwater, and rainwater). The paper then discusses the methods used by researchers in Pakistan for water quality monitoring and assessment, including chemical, physical, and biological methods. It has been determined that in certain regions in Pakistan, the concentration of arsenic present in the groundwater exceeds the national and international prescribed maximum limits. The range of arsenic concentrations in the Punjab province can vary from 10 to 200 μg/L, while higher concentrations of up to 1400 μg/L have been recorded in Sindh. In the Punjab province, fluoride concentrations vary from 0.5 to 30 mg/L, while in Sindh, the levels can reach up to 18 mg/L. In addition, some of the research has talked about bacteria. A 2017 study found that the fecal coliform concentrations in certain water in different cities of Pakistan surpassed limits and were as high as 1100 CFU/100 mL. Additionally, natural factors such as geological formations and high salinity in some areas contribute to the contamination of water. The effect of water pollution on public health has the potential to cause harm. It is critical to investigate creative strategies for improving water quality, and it is necessary to make investments in research and development, which could include the implementation of sophisticated technologies and the conception of new treatment processes. The review performed in this paper facilitates an understanding of the current water quality in Pakistan, including the types and magnitudes of contaminants present in the water sources. Subsequently, the assessment emphasizes deficiencies and challenges in the existing water quality monitoring frameworks and provides suggestions for improving them. This review is also of significant benefit to all the stakeholders involved in ensuring clean and safe water for human consumption and other purposes in Pakistan, such as policymakers, water managers, researchers, and other stakeholders.

Changes in bacterioplankton and zooplankton communities in response to Covid-19 forced lockdown at dolphin surfacing sites in the River Ganga

Abstract The spread of Covid-19 has reduced human intervention in aquatic ecosystems, which has shown a discernible improvement in air and water quality. Ganges River, being a historical, economic and cultural icon of India providing multiple ecosystem services as industrial, provisioning, regulatory and cultural services, constitutes an important habitat for Gangetic Dolphin (Platanista gangetica). The surfacing of dolphins in Ganga river is patchy and unpredictable. The present study unravels 25 different chemical, microbiological, biological and meta-genomics of most abundant bacteria at dolphin surfacing sites in Ganges water during October 2017 February 2018 and January 2021 to elicit the impact of Covid-19 lock down at the Garhmukteshwar (28.7601°N, 78.1437°E) stretch in Hapur district of Uttar Pradesh, India. The most abundant bacteria at dolphin surfacing site was Bacillus subtilis in February 2018 which was replaced by Aeromonas sp. in January 2021. The zooplankton community was dominated by rotifers during pre-Covid period however the dominance pattern recorded a shift towards larger herbivorous crustacean species during Covid −19 lock down. Overall bacterial count decreased whereas Chl a level increased during the lock down. The total zooplankton abundance did not show a significant difference, but relative proportion of larger herbivorous crustacean zooplankton increased in January 2021 samples. The present results convincingly establish effects of shutdown on chemical, metagenomics and biological components of the riparian ecosystem and the information of micro to mesoplanktonic community may be useful for characterizing dolphin surfacing sites, enhancing existing water quality monitoring efforts and also for human epidemiological studies associated with recreational use of Ganga water.

Unbiased Estimates and Confidence Intervals of Riverine Loads for Low‐Frequency Water Quality Monitoring Strategies

AbstractAccurate estimates of riverine constituent loads are essential for water pollution control and erosion control in watersheds, but an accurate estimation method for commonly employed water quality monitoring strategies, such as calendar‐based sampling with high‐flow sampling, has not been established. Therefore, we propose methods for both unbiased load estimation and confidence interval construction for annual riverine loads using the Horvitz‐Thompson estimator based on limited samples (12 to 20 per year) collected with commonly used water quality monitoring strategies. In addition, we propose an uncertainty reduction method to calculate efficient confidence intervals using probability resampling based on a simple rating curve due to the small sample sizes. The effectiveness of the proposed method is verified by load estimates based on 150 annual data sets of daily pairs of discharge and concentration observations of six water quality parameters from different watersheds and years. The tested sampling strategies include random sampling over time as a proxy for calendar‐based sampling, random sampling with high‐flow sampling, and flow‐proportional sampling. The results show that the proposed methods provide unbiased load estimates for all the data sets and appropriate confidence intervals. The uncertainty reduction in the confidence interval widths obtained with probability resampling is effective for calendar‐based sampling. We also explain the importance of defining proper sampling probabilities for load estimation based on samples with missing observations. The proposed method can be used to evaluate and improve existing water quality monitoring strategies for more efficient load estimation and the retrieval of accurate load estimates from existing data sets.

The Mobile Water Quality Monitoring System Based on Low-Power Wide Area Network and Unmanned Surface Vehicle

The increasingly serious water pollution problem makes efficient and information-based water quality monitoring equipment particularly important. To cover the shortcomings of existing water quality monitoring methods, in this paper, a mobile water quality monitoring system was designed based on LoRa communication and USV. In this system, the USV carrying water quality sensors was used as a platform. Firstly, the LoRa network is used to monitor water quality over a large area. Secondly, the unmanned surface vessel controls the position error within ±20 m and the velocity error within ±1 m/s based on the Kalman filter algorithm. Thirdly, the genetic algorithm based on improved crossover operators is used to determine the optimal operational path, which effectively improves the iterative efficiency of the classical genetic algorithm and avoids falling into local convergence. In the actual water surface test, its packet loss probability within a working range of 1.5 km was below 10%, and the USV could accurately navigate according to the preset optimal path. The test results proved that the system has a relatively large working range and high efficiency. This study is of high significance in water pollution prevention and ecological protection.

Real-time prediction of river chloride concentration using ensemble learning

Real-time river chloride prediction has received a lot of attention for its importance in chloride control and management. In this study, an artificial neural network model (i.e., multi-layer perceptron, MLP) and a statistical inference model (i.e., stepwise-cluster analysis, SCA) are developed for predicting chloride concentration in stream water. Then, an ensemble learning model based on MLP and SCA is proposed to further improve the modeling accuracy. A case study of hourly river chloride prediction in the Grand River, Canada is presented to demonstrate the model applicability. The results show that the proposed ensemble learning model, MLP-SCA, provides the best overall performance compared with its two ensemble members in terms of RMSE, MAPE, NSE, and R2 with values of 11.58 mg/L, 27.55%, 0.90, and 0.90, respectively. Moreover, MLP-SCA is more competent for predicting extremely high chloride concentration. The prediction of observed concentrations above 150 mg/L has RMSE and MAPE values of 9.88 mg/L and 4.40%, respectively. The outstanding performance of the proposed MLP-SCA, particularly in extreme value prediction, indicates that it can provide reliable chloride prediction using commonly available data (i.e., conductivity, water temperature, river flow rate, and rainfall). The high-frequency prediction of chloride concentration in the Grand River can supplement the existing water quality monitoring programs, and further support the real-time control and management of chloride in the watershed. MLP-SCA is the first ensemble learning model for river chloride prediction and can be extended to other river systems for water quality prediction.

Mineral composition modelling of natural surface water

ABSTRACT We developed a computational environment that allows us to model the mineral composition of natural water according to the elemental and cation-anion content of the sample. The mineral composition modelling of natural water may be considered as the first step of express water monitoring in real-time. The calculated models were experimentally checked using model solutions and natural objects. The integral parameters of simulated water quality are compared with the calculated values and natural water samples. The limitations of the proposed algorithms for the mineral composition modelling of polluted waters have been discussed. For the first time, an automated approach for modelling the mineral and ionic composition of natural water based on the total water quality indicators is proposed. The presented algorithm is intended to be integrated with existing water quality monitoring systems to enhance the capabilities to control the wide range of inorganic ions in freshwaters.

Modeling nutrient release with compiled data in a typical Midwest watershed

There is a critical need for an easy-to-adopt approach to estimate how different land use and management practices affect regional nutrient release into water bodies. As a typical agriculture dominated watershed in the Midwest, Indiana’s White River Watershed has observed large amounts of nutrient release from diffusive sources. This situation will likely worsen with population growth, climate change, and land use change.In this study, we integrated land use change scenarios, experimental data compiled from literature, and an easy-to-adopt nutrient simulation model to examine the effects of land use on nutrient load and delivery, and identify the sensitive areas of nutrient release in the White River Watershed. Nutrient Delivery Ratio (NDR) model and the Scenario Generator tool within the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) ecosystem modeling framework, were adopted in this study, due to its capacity for scenario-based assessments and the simplified nutrient load and delivery functionality. Region-specific nutrient export coefficient values were compiled from literature and applied to model nutrient load and delivery in the White River Watershed, and the model was loosely calibrated with existing water quality monitoring databases.Results showed that the agricultural area releases more nutrients per unit area, while the forest area releases much less nutrients per unit area. Urban area is more sensitive to nitrogen release than phosphorus. Under the AgExpansion scenario, total nitrogen and phosphorus release would be 2.1% and 2.6% higher than the current situation, while under the Pre-settlement scenario, total nitrogen and total phosphorus release would decrease by 73.7% and 88.0%. The approach developed is helpful for watershed protection that avoids computation of complex social and biophysical processes, to inform and guide conservation practices by revealing areas with the greatest effects on nutrients in streams under different land use scenarios. The experiment database that was compiled could serve as the basis of similar studies in the future. The approach will be beneficial to local community and may easily be applied to other agriculture dominate Midwest regions.

Verification of water environment monitoring network representativeness under estuary backwater effects.

The multi-functional weirs constructed as part of the Four Major River Restoration Project in Korea are operated for water level management and may have a backwater effect in estuaries. If the main channel of the Nakdong River flows backward and affects the estuary water, the water quality in the estuaries may not be representative of the tributary water quality. In this study, we confirmed the representativeness of the existing water quality monitoring networks using spatiotemporally disperse electrical conductivity observations, self-organizing maps (SOMs) for monthly pattern analysis, and the LOcally WEighted Scatter plot Smoother (LOWESS) technique for trend analysis. The results show that the Namgang 4-1 site, which is located in the Nam River estuary, is not affected by the Nakdong River, while the Baekcheon (Sunwongyo) site in the Baekcheon estuary is always affected by the Nakdong River. Therefore, it is necessary to relocate the existing monitoring network or establish a new monitoring network for locations affected by mainstream backflow, as is seen in Baekcheon (Sunwongyo). The methods proposed in this study, including spatiotemporally diverse electrical conductivity measurement, dimensionless fluctuation values, SOMs, and LOWESS, can be used to verify the representativeness of water quality measurement networks in other regions.

Optimization of water quality monitoring stations using genetic algorithm, a case study, Sefid-Rud River, Iran

Water quality monitoring network needs periodic evaluations based on environmental demands and financial constraints. We used a genetic algorithm to optimize the existing water quality monitoring stations on the Sefid-Rud River, which is located in the North of Iran. Our objective was to optimize the existing stations for drinking and irrigation purposes, separately. The technique includes two stages called data preparation and the optimization. On the data preparation stage, first the basin was divided into four sections and each section was consisted of some stations. Then, the score of each station was computed using the data provided by the water Research Institute of the Ministry of energy. After that, we applied a weighting method by providing questionnaires to ask the experts to define the significance of each parameter. In the next step, according to the scores, stations were prioritized cumulatively. Finally, the genetic algorithm was applied to identify the best combination. The results indicated that out of 21 existing monitoring stations, 14 stations should remain in the network for both irrigation and drinking purposes. The results also had a good compliance with the previous studies which used dynamic programming as the optimization technique.

(Invited) Real-Time, Selective Detection of Lead Ions in Water Using a Graphene-Based Field-Effect Transistor Sensing Platform

Water plays an important role in the economic world; only 3% of available water is potable, and with increasing demand, the need for safe drinking water is rising. The National Academy of Engineering identified “providing access to clean water” as one of the top 10 grand challenges for engineering in the 21st century. Heavy metal ions, e.g., lead, mercury, and arsenic, are widely present in water systems. These heavy metal ions are poisonous and may lead to serious damage to organs, tissues and bones, and nervous systems of humans. Existing water quality monitoring mostly occurs at the water supply intake or water treatment plant, instead of along water distribution lines and at the point of use, which is considered inadequate given potential changes in water quality and associated risks within water distribution systems. Current detection methods for aqueous heavy metal ions are often too expensive or unsuitable for in-situ and real-time detection (an unmet need). A field-effect transistor (FET) device-based sensor is developed to specifically detect Pb2+ ions in an aqueous environment. Reduced graphene oxide (rGO), as the semiconducting channel material, is utilized in the FET device through a self-assembly method. An L-glutathione reduced (GSH) is employed as the capture probe for the label-free detection. By monitoring the electrical characteristics of the FET device, the performance of the sensor is measured and investigated. Compared with conventional detection technologies, this sensor enables real-time detection with a response time of 1-2 seconds. A lower detection limit for Pb2+ ions as low as 10 nM is achieved, which is much lower than the maximum contaminant level (MCL) for Pb2+ ions in drinking water advised by the World Health Organization (WHO). Furthermore, the rGO FET sensor is able to distinguish Pb2+ from other metal ions. Without any sample pretreatment, the platform is user-friendly.

Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River.

Knowledge of the diversity and ecological function of the microbial consortia of James River in Virginia, USA, is essential to developing a more complete understanding of the ecology of this model river system. Metagenomic analysis of James River's planktonic microbial community was performed for the first time using an unamplified genomic library and a 16S rDNA amplicon library prepared and sequenced by Ion PGM and MiSeq, respectively. From the 0.46-Gb WGS library (GenBank:SRR1146621; MG-RAST:4532156.3), 4 × 106 reads revealed >3 × 106 genes, 240 families of prokaryotes, and 155 families of eukaryotes. From the 0.68-Gb 16S library (GenBank:SRR2124995; MG-RAST:4631271.3; EMB:2184), 4 × 106 reads revealed 259 families of eubacteria. Results of the WGS and 16S analyses were highly consistent and indicated that more than half of the bacterial sequences were Proteobacteria, predominantly Comamonadaceae. The most numerous genera in this group were Acidovorax (including iron oxidizers, nitrotolulene degraders, and plant pathogens), which accounted for 10 % of assigned bacterial reads. Polaromonas were another 6 % of all bacterial reads, with many assignments to groups capable of degrading polycyclic aromatic hydrocarbons. Albidiferax (iron reducers) and Variovorax (biodegraders of a variety of natural biogenic compounds as well as anthropogenic contaminants such as polycyclic aromatic hydrocarbons and endocrine disruptors) each accounted for an additional 3 % of bacterial reads. Comparison of these data to other publically-available aquatic metagenomes revealed that this stretch of James River is highly similar to the upper Mississippi River, and that these river systems are more similar to aquaculture and sludge ecosystems than they are to lakes or to a pristine section of the upper Amazon River. Taken together, these analyses exposed previously unknown aspects of microbial biodiversity, documented the ecological responses of microbes to urban effects, and revealed the noteworthy presence of 22 human-pathogenic bacterial genera (e.g., Enterobacteriaceae, pathogenic Pseudomonadaceae, and ‘Vibrionales') and 6 pathogenic eukaryotic genera (e.g., Trypanosomatidae and Vahlkampfiidae). This information about pathogen diversity may be used to promote human epidemiological studies, enhance existing water quality monitoring efforts, and increase awareness of the possible health risks associated with recreational use of James River.

An Efficient Method for Assessing Water Quality Based on Bayesian Belief Networks

A new methodology is developed to analyse existingwater quality monitoring networks. This methodology incorporates different aspects ofmonitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy redu ction. The work starts with a formulation of a conceptual framework for groundwater quality monitoringto represent the methodology’s context. This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of al available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India).

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. The effectiveness of existing river water quality monitoring network is assessed. Significance of seasonal redesign of the monitoring network is demonstrated. Rationalization of water quality parameters is performed in a statistical framework.

Sentinel Site Development of a Major Salt Marsh System in the Mid-Atlantic Region (USA)

The Tuckerton Peninsula, a large expanse (~2000 ha) of highly inundated Spartina alterniflora salt marsh habitat, forms one of the most extensive coastal wetland systems in New Jersey (USA). It is projected to be among the first salt marsh systems in New Jersey to be lost by rising sea level driven by climate change. The changes that occur in the demographic, ecologic, and ecogeomorphic characteristics of the salt marsh habitat at this location will be vital to understanding future habitat change in coastal wetlands in the Mid-Atlantic region. As a consequence, the Tuckerton Peninsula salt marsh system is designated as a sentinel site of the Jacques Cousteau National Estuarine Research Reserve (JCNERR) for the detection, monitoring, and assessment of climate change effects, most notably sea-level rise and inundation. Development of the Tuckerton Peninsula as a sentinel site requires a high accuracy local geodetic control network to connect existing water quality monitoring stations, vegetation transects and monitoring surface elevation tables, ground water wells, and digital elevation models on the same vertical datum. The integration of these monitoring infrastructure components is crucial to effective operation of the sentinel site. It will enable the collection of essential data to assess rates of marsh migration and upland transgression, as well as delineate key natural and anthropogenic drivers influencing habitat condition and change. The JCNERR sentinel site designation supports the National Oceanic and Atmospheric Administration’s long-term goal of assessing coastal vulnerability in the USA to climate change and generating data useful for forging climate adaptation and mitigation initiatives for coastal communities. Habitat and vertical control data collected at this sentinel site will be made available to decision makers across the nation and will enable them to determine how the loss of critical salt marsh habitat will affect coastal communities, their adaptation to future habitat loss, and their development of mitigation plans to address impacts and enhance resiliency.

Evaporative isotope enrichment as a constraint on reach water balance along a dryland river

Deuterium and oxygen-18 enrichment in river water during its transit across dryland region is found to occur systematically along evaporation lines with slopes of close to 4 in 2H–18O space, largely consistent with trends predicted by the Craig–Gordon model for an open-water dominated evaporating system. This, in combination with reach balance assessments and derived runoff ratios, strongly suggests that the enrichment signal and its variability in the Barwon–Darling river, Southeastern Australia is acquired during the process of evaporation from the river channel itself, as enhanced by the presence of abundant weirs, dams and other storages, rather than reflecting inherited enrichment signals from soil water evaporation in the watershed. Using a steady-state isotope mass balance analysis based on monthly 18O and 2H, we use the isotopic evolution of river water to re-construct a perspective of net exchange between the river and its contributing area along eight reaches of the river during a drought period from July 2002 to December 2003, including the duration of a minor flow event. The resulting scenario, which uses a combination of climatological averages and available real-time meteorological data, should be viewed as a preliminary test of the application rather than as a definitive inventory of reach water balance. As expected for a flood-driven dryland system, considerable temporal variability in exchange is predicted. While requiring additional real-time isotopic data for operational use, the method demonstrates potential as a non-invasive tool for detecting and quantifying water diversions, one that can be easily incorporated within existing water quality monitoring activities.

WATER QUALITY MONITORING NETWORK DESIGN: A PROBLEM OF MULTI‐OBJECTIVE DECISION MAKING1

ABSTRACT: Hydrologic data network design is a fairly complicated problem where questions as to the number of gages required, time frequencies to be selected, and benefits/costs of monitoring still remain unresolved. These issues are intensified in case of water quality variables as they are more error‐prone, costly, and time consuming to sample. The basic difficulty underlying the design and evaluation of monitoring systems is the lack of an objective criterion to assess: (a) the efficiency, and (b) cost‐effectiveness of a network. A statistical procedure based on the entropy principle of information theory is proposed to address the evaluation of both factors. Efficiency is measured quantitatively in terms of the information produced by a network. Similarly, benefits of monitoring are described by informative measures for an objective evaluation of cost‐effectiveness. The study presented demonstrates the applicability of the entropy method in assessing the efficiency and the benefits of an existing water quality monitoring network. The method is applied for temporal and spatial features of monitoring, handled as both separate and combined problems. The results are shown in the case of the highly polluted Porsuk River in Turkey. The strengths and shortcomings of the proposed methodology are discussed, with recommendations for future research on the application of the entropy principle in network design.