Variations Of Water Quality Variables Research Articles

Unravelling nutrient fate and CO2 concentrations in the reservoirs of the Seine Basin using a modelling approach

Reservoirs are active reactors for the biogeochemical cycling of carbon (C) and nutrients (nitrogen: N, phosphorus: P, and silica: Si), however, our in-depth understanding of C and nutrient cycling in reservoirs is still limited by the fact that it involves a variety of closely linked and coupled biogeochemical and hydrological processes. In this study, the updated process-based Barman model was applied to three reservoirs of the Seine Basin during 2019–2020, considering the variations of carbon dioxide (CO2) concentrations and key water quality variables. The model simulations captured well the observed seasonal variations of water quality variables, although discrepancies remained for some variables. According to the model, we found that: (1) the three reservoirs are autotrophic ecosystems and showed high removal efficiency of dissolved inorganic carbon and nutrients during 2019-2020; (2) phytoplankton assimilation, benthic denitrification, precipitation and dissolution of calcium carbonate, and gas exchange at the water-air interface are the dominant processes for water quality variations in reservoirs; (3) based on scenarios results, trophic state and mean water depth of reservoir would impact the biogeochemical processes and the retention efficiency of nitrate and dissolved silicate. Finally, we expect that the successful application of Barman model in the reservoirs of the Seine Basin could provide a useful tool for simulating reservoir water quality changes and thus evaluating the impacts of reservoirs on downstream water quality.

Hydro-environmental response to the inter-basin water resource development in the middle and lower Han River, China

Abstract As one of the most important influencing factors, inter-basin water resource development has been exerting an increasingly evident impact on the hydro-environment of river basins. The Han River was selected as a case study to reveal the hydro-environmental response to China's inter-basin water resource development. The hydrological changes and water-quality variations resulted from the middle route of the South-to-North Water Transfer Project (SNWTP) and the Three Gorges Reservoir (TGR) operation were examined based on a hydro-environmental model. The results indicated that the runoff reduction is obvious after the SNWTP operation, and the low-flow duration significantly increased by 4–5 months. Consequently, the flow decrease significantly contributed to the water quality deterioration in the middle and lower Han River, while the Yangtze-Han Water Diversion Project (YHWDP) can not alleviate the situation completely. Moreover, the nutrient assimilative capacity decreased after water diversion, which agrees with the hydrological changes along the middle and lower Han River. The quantitative analysis performed in this study distinguishes the spatiotemporal variation in water quality variables using the integrated model. It provides insights into water quality management under the influences of inter-basin water resource development.

Responses of surface water quality to future land cover and climate changes in the Neka River basin, Northern Iran.

The spatial and temporal dimensions of environmental impacts of climate and land cover changes are two significant factors altering hydrological processes. Studying the effects of these factors on water quality, provides important insight for water resource management and optimizing land planning given increasing water scarcity and water pollution. The impact of land cover and climate changes on surface water quality was assessed for the Neka River basin in Northern Iran. The widely used Soil and Water Assessment Tool (SWAT) was applied for pollutant modeling, and was calibrated using the Sequential Uncertainty Fitting (SUFI-2) algorithm. An ensemble of 17 CMIP5 climate models under two IPCC greenhouse gas emission scenarios were selected, and future land cover change (LCC) was modeled based on the evolution that occurred in the last decades. We simulated the impacts of climate change (CC) and LCC on sediment, nitrate, and phosphate for the 2035-2065 time slice. The annual loads of sediment, phosphate, and nitrate are projected to decrease under both CC scenarios based on the inter-model average, and generally follow a pattern similar to the change in river discharge. Nitrate concentrations show an increase across all seasons, while the sediment and phosphate concentrations increase in winter and autumn under CC conditions. Results indicate that pollutants are expected to increase under LCC alone, mainly due to the expansion of the cultivated areas. Overall, it seems CC has a greater impact than LCC on the variation of water quality variables in the Neka River basin. With a combined change in climate and land cover, the annual nitrate concentrations are expected to increase by + 19.7% and + 17.9%, under RCP 4.5 and RCP 8.5, respectively. The combined impacts of the CC and LCC caused a decline in the annual sediment and phosphate concentrations by -10.1% and -2.2% under RCP 4.5 and -9%, and -3.2% under RCP 8.5, respectively.

A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

AbstractHypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored.

Spatio-temporal variation of water quality variables and hydrography in a seabream cage culture farm off the coast of Oman

Mariculture cage farming in Oman is in its infancy stage. This study provides important baseline information about the initial state of mariculture in Oman and for the sustainable management of future local cage farming. Our main objective was to evaluate the spatio-temporal variations of water quality and hydrography around a gilthead seabream (Sparus aurata) cage farm in Quriyat (Sea of Oman). Starting in July 2018, we conducted a monitoring program over one year in which physico-chemical variables and nutrient levels were regularly measured at the farm cages and at reference sites away from the farm. Vertical flow profiles were recorded at the farm and analysed together with remotely sensed data. The results showed no significant differences among physico-chemical variables and nutrient levels between cages and reference sites. However, there were clear seasonal as well as significant short-term variations in the measurements. Winter conditions are usually homogeneous over the water column without reaching extremes. In summer we recorded surface temperatures of up to 32 °C and extended periods of hypoxia below 35 m depth. Periods of pronounced stratification were interrupted by energetic irregular flow pulses that triggered short up or down-welling events which lead to strong variations of temperature and oxygen. We did not measure a significant impact of the cage farm on the local environment. Our results rather point to the particular importance of monitoring temperature and oxygen levels. Both variables can approach threshold levels for fish farming, especially during summer. We determined the relevant characteristics of the local system and defined requirements for adequate monitoring. The findings of this study provide a timely baseline for future research on the interactions between local cage farms and the marine ecosystem and will assist in the planning and management of mariculture in Oman.

Water quality variability in the middle and down streams of Han River under the influence of the Middle Route of South-North Water diversion project, China

The middle and down streams of Han River are complex river systems influenced by hydrologic variations, population distributions, and the engineering projects. The Middle Route of China’s South-to-North Water Transfer (MSNW) project planned to transfer 95 billion m3 annually from Han River to north China. The operation of the MSNW project may alter the flow rate and further influence the water quality of Han River. This study used min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) to analyze spatio-temporal variations of the water quality variables in three typical tributary-mainstream intersection zones in Han River from June 2014 to April 2017. MAFA results showed that chlorophyll-a (Chl-a), chemical oxygen demand (COD), suspend solid (SS) and phosphate (PO43−) (represented as trophic dynamics) are main concerned water quality variables in densely populated zones (Zones 1 and 3), and total nitrogen (TN), nitrate nitrogen (NO3−), COD, and PO43− (regarded as nutrient formations dynamics) represent the underlying water quality variations in agricultural cultivation zone (Zone 2). DFA results indicated that domestic and municipal effluent pollutants influence the organic concentrations and nutrient formations in the mainstream in Zones 1 and 2. The non-point source nitrogen and phosphorus discharged from the tributaries Tangbai and Hanbei Rivers elevate the nutrient concentrations and increase Chl-a concentration (i.e. promote the algal growth) in densely populated zones. In addition, controlling the flow rates in low and middle flow rate conditions can avoid degrading water quality. The flow rate should be elevated to more than 700 cm (cubic meters per second) in the middle stream and to more than 800 cm in the downstream of Han River for preventing water quality deterioration from high loadings of organic pollutants and nutrients. The integrated MAFA and DFA method establishes an efficient analysis distinguishing spatio-temporal variation of water quality variables and provides useful site-specific management to control water quality in various flow conditions.

Spatio-temporal aspects of the environmental factors affecting water quality in boreal rivers

River water quality is the outcome of multiple processes and factors, which vary spatially and temporally. In this study, the key spatial and temporal scales and the most important environmental factors explaining river water quality at these scales were analysed by generalized additive models (GAMs). Water quality was studied through total phosphorus (median = 61.9 µg l−1) and nitrogen (1388.1 µg l−1), pH (6.7) and water colour (143.3 mg Pt l−1). Environmental factors covered variables from land use/cover, climate and other landscape characteristics. The spatial scales used were the closest 50, 100, 200, 500 and 1000 m buffer zones around the river channel, in addition to the entire catchment area. Temporality was studied through the entire year and four periods, which were determined by the natural variation in discharge. In the comparison of spatial scales of environmental factors, the variation in phosphorus, nitrogen and water colour was best explained using environmental data from the broadest scale, the entire catchment. In contrast, the variation in pH was best explained using data from the closest (50 m) buffer zone, the riparian area. In modelling temporal scales, the variation in water quality variables was best explained during discharge maximum periods and when the environmental data covering the entire year were considered. Nutrients were related specifically to agriculture, water colour to lake percentage and pH to pastures. The results showed the suitability of GAMs in water quality studies.

The Influence of Model Structure Uncertainty on Water Quality Assessment

Physically-based mathematical water quality models are known as potentially effective tools to simulate the temporal and spatial variations of water quality variables along rivers. Each model relies on specific sets of assumptions and equations to simulate the physico-biochemical processes, which influence on its simulation results. This paper aims to improve the insight in the uncertainties related to state–of–the–art river physico-biochemical water quality modelling. Sensitivity analysis is applied to the processes implemented in three most popular commercial software packages: MIKE11, InfoWorks RS and InfoWorks ICM. This is done for the Molse Neet river case study. Firstly, the physico-biochemical processes are screened to obtain a preliminary assessment on the critical processes and to determine the processes that require more detailed comparison. Then, local sensitivity analysis is carried out to specify the sensitive parameters and processes. Results show that the hydrodynamic results, heat transfer rate and reaeration simulations cause large differences in model simulation outputs for water temperature and dissolved oxygen concentrations. The ignorance of processes related to sediment transport, phytoplankton and bacteria has a significant influence on the higher values of organic matter and lower values of dissolved oxygen concentrations. The three models show consensus on the main pollutant sources explaining organic matter and nitrate concentrations, but disagree on the main factors explaining the DO concentrations.

Water Quality, Fish Production and Economics of Nile Tilapia, Oreochromis niloticus, and African Catfish, Clarias gariepinus, Monoculture and Polycultures

Twelve 400 m2 earthen ponds were used to test the effect of Nile tilapia, Oreochromis niloticus, African catfish, Clarius gariepinus, monoculture and polyculture on water quality, fish production and economic performance across a range of stocking ratios: 100% tilapia + 0% catfish, 75% tilapia + 25% catfish, 50% tilapia + 50% catfish, and 0% tilapia +100% catfish. All ponds were stocked at the rate of 3 fish/m2. Two-way ANOVA showed that culture period explained more than 60% of variation in water quality variables while treatment was responsible only for about 11%. Net fish yields were 5.71, 9.84, 11.03, and 11.35 tones/ha corresponding with daily weight gains of 36.60, 63.08, 70.69, and 72.75 kg/ha/d in the 0% catfish, 25% catfish, 50% catfish, and 100% catfish treatments, respectively. Feed consumed was 7.59, 10.84, 13.48, and 17.03 tones/ha for the four treatments, respectively. Fish yield and feed consumption were highest in the catfish monoculture (P < 0.05) followed by the 50% catfish, 25% catfish, and 0% catfish treatments. Net profit for both polyculture treatments was significantly higher than both monoculture treatments, although no significant differences were observed among polyculture or between the two monoculture treatments. The 25% catfish polyculture treatment generated the highest economic returns.

Assessment of Water Quality and Identification of Pollution Sources of Plateau Lakes in Yunnan (China)

The plateau lakes of Yunnan are important both ecologically and economically in China. Nevertheless, the human impact on water quality in these lakes has become increasingly highlighted. The water quality of 10 plateau lakes was monitored regularly over the period of 2000 through 2004 for 24 parameters. Multivariate statistical techniques, including cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA), were employed to better interpret information about the water quality and its pollution sources. No obvious data reduction from CA/FA was found because three principal components (PCs) needed 14 variables to explain 85.01% of the total variance. However, three latent factors accounted for pollution mainly from the following sources: agricultural activities, residential activities and anthropogenic-toxic pollution from industrial effluents, or other special activities. Box-whiskers plots were employed to visually interpret the spatiotemporal variations of water quality variables, which were highly correlated with three PCs. Three types of water quality (i.e., low-, medium-, and high-polluted lakes) were determined through CA based on the similarity of water quality variables. Our results may provide helpful information for the authorities to effectively manage the water quality and make sound policies.

Criteria for selection of spatial dimension in the application of one- and two-dimensional water quality models

A critical aspect of modelling water quality in lakes and reservoirs is to select a model with a spatial representation that is appropriate for the dominant mixing processes and the observed variation in water quality variables. One-dimensional modelling is frequently applied to small-to-medium size lakes and reservoirs to encompass variations that are most pronounced in the vertical as a result of seasonal or permanent density stratification. Two- and three-dimensional models are generally applied to larger water bodies where both horizontal and vertical variations are important. Horizontal variations may result from the local effects of inflows and outflows, basin scale seiching and wind mixing. Increased dimensional capabilities in water quality models produce a large increase in computational time and may therefore impose severe constraints on the length of the simulation. This problem is most frequently averted by increasing the time step, which in itself may introduce problems associated with numerical diffusion. Therefore when longer simulations of water quality in a lake are required, as is often the case for management applications, it is desirable to select a model with the least spatial dimension required to adequately encompass the observed variation in water quality variables and the most important mixing processes. This study proposes a set of criteria that can be used a priori to select the appropriate dimensional representation when applying a water quality model to a lake. The critical factors that influence the application of a one- or two-dimensional model are the size of the lake, the volume of the inflows and the settling velocity of particulates in the inflows.

Effects of Sampling Technique on Precision Estimates for Water Quality Variables in Fish Culture Ponds

Three sampling experiments were conducted to assess variation in water quality variables in ponds: samples from individual ponds on single dates; samples from individual ponds on different dates during a growing season; and samples from replicate ponds. Data on dissolved oxygen, temperature, Secchi disk visibility, total hardness, total particulate matter, filtrable orthophosphate, and pH exhibited wide variation and covered ranges generally expected for fish ponds. A two-stage nested (hierarchical) linear statistical model was used to estimate components of variance for the three experiments. Pond size (0.04 to 10.32 ha) and management scheme (fertilized ponds and ponds receiving daily applications of fish feed) had no influence on variation. In general, viability of water quality components increased in the order: samples within ponds, among sampling dates, and between replicate ponds. Variation was greatest for total particulate matter and filtrable orthophosphate, intermediate for dissolved oxygen, Secchi disk visibility, and total hardness, and lowest for pH and temperature. The components of variance were used to estimate expected standard errors of pond means, seasonal means, and replicate pond means for all water quality variables. Figures prepared from these estimates may be used to determine the numbers of samples, sampling dates, or replicate ponds necessary to achieve a given level of precision.