The CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

The CoastColour project Round Robin (CCRR) project (http://www.coastcolour.org) funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). The datasets mainly consisted of 6,484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll-a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5,000 simulations) and from 3,054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

Surface water quality parameters for monitoring oil shale development /

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Descriptions of Water Quality Parameters for Lakes in the Ambient Lake Monitoring Program

This fact sheet summarizes the water quality data collected as part of the Iowa Department of Natural Resources ambient lake monitoring program.

Modelling the effects of inflow parameters on lake water quality

A one-dimensional lake water quality model which includes water temperature, phytoplankton, phosphorus as phosphate, nitrogen as ammonia, nitrogen as nitrate and dissolved oxygen concentrations, previously calibrated for Lake Calhoun (USA) is applied to Uokiri Lake (Japan) for the year 1994. The model simulated phytoplankton and nutrient concentrations in the lake from July to November. Most of the water quality parameters are found to be the same as for Lake Calhoun. To predict probable lake water quality deterioration from algal blooming due to increased nutrient influx from river inflow, the model was run for several inflow water conditions. Effects of inflow nutrient concentration, inflow volume, inflow water temperatures are presented separately. The effect of each factor is considered in isolation although in reality more than one factor can change simultaneously. From the results it is clear that inflow nutrient concentration, inflow volume and inflow water temperature show very regular and reasonable impacts on lake water quality.

Image calibration to like-values in mapping shallow water quality from multitemporal data

The applicability of image calibration to like-values in mapping water quality parameters from multitemporal images is explored, Six sets of water samples were collected at satellite overpasses over Moreton Bay, Brisbane, Australia. Analysis of these samples reveals that waters in this shallow bay are mostly TSS-dominated, even though they are occasionally dominated by chlorophyll as well. Three of the images were calibrated to a reference image based on invariant targets. Predictive models constructed from the reference image were applied to estimating total suspended sediment (TSS) and Secchi depth from another image at a discrepancy of around 35 percent. Application of the predictive model for TSS concentration to another image acquired at a time of different water types resulted in a discrepancy of 152 percent. Therefore, image calibration to like-values could be used to reliably map certain water quality parameters from multitemporal TM images so long as the water type under study remains unchanged. This method is limited in that the mapped results could be rather inaccurate if the water type under study has changed considerably. Thus, the approach needs to be refined in shallow water from multitemporal satellite imagery.

Assessing ecological impacts of shrimp and sewage effluent: Biological indicators with standard water quality analyses

Despite evidence linking shrimp farming to several cases of environmental degradation, there remains a lack of ecologically meaningful information about the impacts of effluent on receiving waters. The aim of this study was to determine the biological impact of shrimp farm effluent, and to compare and distinguish its impacts from treated sewage effluent. Analyses included standard water quality/sediment parameters, as well as biological indicators including tissue nitrogen (N) content, stable isotope ratio of nitrogen (delta N-15) and amino acid composition of inhabitant seagrasses, mangroves and macroalgae. The study area consisted of two tidal creeks, one receiving effluent from a sewage treatment plant and the other from an intensive shrimp farm. The creeks discharged into the western side of Moreton Bay, a sub-tropical coastal embayment on the east coast of Australia. Characterization of water quality revealed significant differences between the creeks, and with unimpacted eastern Moreton Bay. The sewage creek had higher concentrations of dissolved nutrients (predominantly NO3-/NO2- and PO43-, compared to NH4+ in the shrimp creek). In contrast, the shrimp creek was more turbid and had higher phytoplankton productivity. Beyond 750 m from the creek mouths, water quality parameters were indistinguishable from eastern Moreton Bay values. Biological indicators detected significant impacts up to 4 km beyond the creek mouths (reference site). Elevated plant delta N-15 values ranged from 10.4-19.6 parts per thousand at the site of sewage discharge to 2.9-4.5 parts per thousand at the reference site. The free amino acid concentration and composition of seagrass and macroalgae was used to distinguish between the uptake of sewage and shrimp derived N. Proline (seagrass) and serine (macroalgae) were high in sewage impacted plants and glutamine (seagrass) and alanine (macroalgae) were high in plants impacted by shrimp effluent. The delta N-15 isotopic signatures and free amino acid composition of inhabitant flora indicated that sewage N extended further from the creek mouths than shrimp N. The combination of physical/chemical and biological indicators used in this study was effective in distinguishing the composition and subsequent impacts of aquaculture and sewage effluent on the receiving waters. (C) 2001 Academic Press.

Water quality in lakes and reservoirs in China

Dissertation elaborated for the partial fulfilment of the requirements of the Master Degree in Civil Engineering in the Speciality Area of Hydarulics

Mapping of use and occupation of the soil and irrigation water quality in the city of Salto do Lontra-Paraná, Brazil

The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Paraná, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm - MAXVER, and the water quality parameters analyzed were pH, EC, HCO3-, Cl-, PO4(3-), NO3-, turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality.

Assessment of Water Quality along the Coastal Areas of Kerala: A Study of the Tsunami Impact on Groundwater

Everywhere, on the coastal belt it is proved without doubt that the pristine ground water quality was severely deteriorated after the 26 December 2004 Indian Ocean Tsunami. But how far is more relevant, as it is decided by the so-called pre-tsunamic situation of the region. In water quality studies it is this reference finger print which earmarks regional ground water chemistry based on which the monthly variability could rationally be interpreted. This Ph D thesis comprises the testing and evaluation of the facts: whether there is any significant difference in the water quality parameters under study between stations and between months in Tsunami Affected Dug Wells (TADW). Whether the selected water quality parameters vary significantly from BIS and WHO standards. Whether the water quality index (WQI) differ significantly between Tsunami Affected Dug Wells (TADW) and Bore Wells (BW). Whether there is any significant difference in the water quality parameters during December 2005 and December 2008. Is there any significant change in the Water Quality Parameters before 2001 and after tsunami (2005) in TADW.

Calibration and sensitivity analysis of a river water quality model under unsteady flow conditions

Water quality models generally require a relatively large number of parameters to define their functional relationships, and since prior information on parameter values is limited, these are commonly defined by fitting the model to observed data. In this paper, the identifiability of water quality parameters and the associated uncertainty in model simulations are investigated. A modification to the water quality model `Quality Simulation Along River Systems' is presented in which an improved flow component is used within the existing water quality model framework. The performance of the model is evaluated in an application to the Bedford Ouse river, UK, using a Monte-Carlo analysis toolbox. The essential framework of the model proved to be sound, and calibration and validation performance was generally good. However some supposedly important water quality parameters associated with algal activity were found to be completely insensitive, and hence non-identifiable, within the model structure, while others (nitrification and sedimentation) had optimum values at or close to zero, indicating that those processes were not detectable from the data set examined. (C) 2003 Elsevier Science B.V. All rights reserved.

Development of a water quality index using a fuzzy logic: A case study for the sorocaba river

Due to growing urbanization and industrialization, the environment is suffering from pollution of rivers, degradation of soils and deteriorated air quality. Quality indices appear to be useful to evaluate the conditions of these media. The aim of this study was the development of a water quality index using a fuzzy inference system, since such an approach has proved advantageous in addressing problems that are subjective by nature or for which the data are uncertain. The methodology employed was based on this inference system, and considered the nine water quality parameters employed by CETESB (Companhia de Tecnologia de Saneamento Ambiental, São Paulo, Brazil) to evaluate water quality. After assessment of the data using the index, a comparison was made with the WQI (Water Quality Index), which is used for the monitoring of various water bodies, including in the study region. The results obtained using the index developed on the basis of fuzzy inference were found to be more useful than those derived from the method currently used by CETESB, since losses and/or omissions concerning individual parameters were minimized. © 2010 IEEE.

Water Quality Variability in a Bioswell and Concrete Drainage Pipe, Southwest Lincoln, Nebraska

Abstract The goal of this project is to evaluate the effectiveness of bioswells in protecting water quality from urban runoff. The hypothesis tested in this project is that water in bioswells improves water quality. Water quality in both a bioswell and an underground concrete lined ditch, both containing ground and surface water, were tested for certain water quality parameters. These parameters consisted of: Dissolved Oxygen, pH, water temperature, weather temperature, Total Dissolved Solids, Specific Conductivity, Alkalinity, Total Dissolved Carbon, Chemical Oxygen Demand, and depth and width of the sampling site. An additional contaminant that was looked at was motor oil. This was measured by comparing Total Organic Carbon with Chemical Oxygen Demand. A variety of different methods to measure the water quality parameters were utilized. The concrete site had more stable readings, but much higher water temperatures. However, the bioswell water is mainly from surface water runoff, and the underground concrete lined pipe is from underground water, so the two cannot be directly compared. The bioswell had high readings, especially pertaining to Oxygen Demand, Total Organic Carbon, and Specific Conductivity in early test dates. But, these readings improved as they were filtered though the bioswell. As plant activity increased and the weather began to warm up there were more stable readings. It is concluded that bioswells are an effective way to reduce problems associated with urban runoff pertaining to certain water quality parameters.

Spatiotemporal monitoring of water quality in coral reefs of Tayrona National Natural Park, Colombian Caribbean, 2011-2013

Tayrona National Natural Park (TNNP; 11°17' - 11°22' N and 73°53' - 74°12' W) is a hotspot of coral reef biodiversity in the Colombian Caribbean, located between the city of Santa Marta (>455,000 inhabitants) and several smaller river mouths (Rio Piedras, Mendihuaca, Guachaca). The region experiences a strong seasonal variation in physical parameters (temperature, salinity, wind, and water currents) due to alternating dry seasons with coastal upwelling and rainy seasons. Here, a range of water quality parameters relevant for coral reef functioning is provided. Water quality was measured directly above local coral reefs (~10 m water depth) by a monthly monitoring for up to 25 months in the four TNNP bays (Chengue, Gayraca, Neguanje, and Cinto) and at sites with different degree of exposition to winds, waves and water currents (exposed vs. sheltered sites) within each bay. The water quality parameters include: inorganic nutrient (nitrate, nitrite and soluble reactive phosphorus), chlorophyll a, particulate organic carbon and nitrogen concentrations (with a replication of n=3) as well as oxygen availability, biological oxygen demand, seawater pH, and water clarity (with a replication of n=4). This is by far the most comprehensive coral reefs water quality dataset for the region. A detailed description of the methods can be found within the referenced publications.

Tropical seagrass-associated macroalgae distributions and trends relative to water quality

Tropical coastal marine ecosystems including mangroves, seagrass beds and coral reef communities are undergoing intense degradation in response to natural and human disturbances, therefore, understanding the causes and mechanisms present challenges for scientist and managers. In order to protect our marine resources, determining the effects of nutrient loads on these coastal systems has become a key management goal. Data from monitoring programs were used to detect trends of macroalgae abundances and develop correlations with nutrient availability, as well as forecast potential responses of the communities monitored. Using eight years of data (1996–2003) from complementary but independent monitoring programs in seagrass beds and water quality of the Florida Keys National Marine Sanctuary (FKNMS), we: (1) described the distribution and abundance of macroalgae groups; (2) analyzed the status and spatiotemporal trends of macroalgae groups; and (3) explored the connection between water quality and the macroalgae distribution in the FKNMS. In the seagrass beds of the FKNMS calcareous green algae were the dominant macroalgae group followed by the red group; brown and calcareous red algae were present but in lower abundance. Spatiotemporal patterns of the macroalgae groups were analyzed with a non-linear regression model of the abundance data. For the period of record, all macroalgae groups increased in abundance (Abi) at most sites, with calcareous green algae increasing the most. Calcareous green algae and red algae exhibited seasonal pattern with peak abundances (Φi) mainly in summer for calcareous green and mainly in winter for red. Macroalgae Abi and long-term trend (mi) were correlated in a distinctive way with water quality parameters. Both the Abi and mi of calcareous green algae had positive correlations with NO3−, NO2−, total nitrogen (TN) and total organic carbon (TOC). Red algae Abi had a positive correlation with NO2−, TN, total phosphorus and TOC, and the mi in red algae was positively correlated with N:P. In contrast brown and calcareous red algae Abi had negative correlations with N:P. These results suggest that calcareous green algae and red algae are responding mainly to increases in N availability, a process that is happening in inshore sites. A combination of spatially variable factors such as local current patterns, nutrient sources, and habitat characteristics result in a complex array of the macroalgae community in the seagrass beds of the FKNMS.

Parameters of water quality,

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