Three-dimensional water-quality-transport model compared with field observations (original) (raw)
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Modelling the effects of inflow parameters on lake water quality
2003
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.
Developing Hydrodynamic and Sediment Transport Modelling on Lakes: A Preliminary Study
International Journal of Environmental Science and Development, 2018
Lakes in Universitas Indonesia (UI) have several problems to overcome. Water quality of UI's lakes degrades in the last decade. One of the problems is high turbidity. The longer turbidity in lakes, the more dangerous the lakes environment life would be. So, this research focuses on sediments problem or physical assessment. The main research aims to develop hydrodynamic and sediment transport modelling of Agathis and Mahoni Lakes in Universitas Indonesia. The authors want to increase the quality of water in UI's lakes and make both Agathis and Mahoni Lakes as the representative preliminary model. Research design or methodology is needed to concept the research to achieve goals. Unfortunately, research methodology has not been developed yet for this case. This paper goals to construct methodology as a preliminary study of the main research. The authors use extensive literature review to model novel methodology. Hydrodynamic and sediment transport modelling could be modelled by Resources Modelling Associates (RMA). Hydrodynamic phenomena in fluid and sediment transport modelling have the suitable governing equation concept with RMA's governing equation. As a thinking result, the main research would be conducted by RMA program modelling, field sampling, and laboratory testing. Field sampling and laboratory could figure the amount of sediment concentration in lakes as data input. The final simulation of this following research is making sensitivity and scenario analysis to prove several hypotheses. Index Terms-Hydrodynamic modelling, RMA program, sediment transport. I. INTRODUCTION Lakes are life, so their existences are important to notice. Lakes include raw water resources category [1]. The quality of raw water is a critical indicator for environment [2], [3]. However, people today have less water for themselves, livestock, and plants. Raw water is two percent of earth's groundwater [4]. In addition, it is the most decreasing and changing ecosystem [5]. Lakes ecosystem consists of physical, chemical, and biological characteristics in the water body. The interaction among them could be studied, understood, and used for effective lakes management [1]. Recent days, the effort to do lakes conservation is difficult
Hydrodynamic and Water Quality Modeling of Lake Eğirdir
CLEAN - Soil, Air, Water, 2014
The main objective of this study was to determine the impact of current point and nonpoint pollution loads on the water quality of Lake E girdir. For this purpose, hydrodynamics and water quality status of Lake E girdir were investigated by using the Delft3D model. The monthly monitoring of pH, dissolved oxygen (DO), chlorophyll-a (Chl-a), and forms of nitrogen and phosphorus was carried out, as was the seasonal monitoring of total organic carbon. In all, seven different sampling points were chosen from the lake and influent streams in order to monitor the lake during the period from December 2010 to November 2011, with the aim of calibrating the model. The performance of the dynamic water quality model was evaluated using the relative root mean square error (RRMSE) values for both the calibration and the validation periods. The results of the model showed that the DO had the lowest, and Chl-a had highest associated RRMSE value. The simulation results showed that Segments 1 and 7 of the system had relatively high concentrations of almost all the pollutants. It was also found that increasing the nonpoint nutrient loads from agricultural sources has a greater effect than increasing the nonpoint nutrient loads from the inhabited areas around the lake.
Ecological Modelling, 2010
Fundamental hydrodynamic and ecological processes of a lake or reservoir could be adequately depicted by one-dimensional (1D) numerical simulation models. Whereas, lakes with significant horizontal water quality and hydrodynamic gradients due to their complex morphometry, inflow or water level fluctuations require a three-dimensional (3D) hydrodynamics and ecological analyses to accurately simulate their temporal and spatial dynamics. In this study, we applied a 3D hydrodynamic model (ELCOM) coupled with an ecological model (CAEDYM) to simulate water quality parameters in three bays of the morphologically complex Lake Minnetonka. A considerable effort was made in setting up the model and a systematic parameterization approach was adopted to estimate the value of parameters based on their published values. Model calibration covered the entire length of the simulation periods from March 29 to October 20, 2000. Sensitivity analysis identified the top parameters with the largest contributions to the sensitivity of model results. The model was next verified with the same setup and parameter values for the period of April 25 to October 10, 2005 against field data. Spatial and temporal dynamics were well simulated and model output results of water temperature (T), dissolved oxygen (DO), total phosphorus (TP) and one group of algae (Cyanobacteria) represented as chlorophyll a (Chla) compared well with an extensive field data in the bays. The results show that the use of the model along with an accurate bathymetry, a systematic calibration and corroboration (verification) process will help to analyze the hydrodynamics and geochemical processes of the morphologically complex Lake Minnetonka. An example of an ecological application of the model for Lake Minnetonka is presented by examining the effect of spatial heterogeneity on coolwater fish habitat analysis in 3D and under a scenario where horizontal spatial heterogeneity was eliminated (1D). Both analyses captured seasonal fish habitat changes and the total seasonal averages differed moderately. However, the 1D analysis did not capture local and short duration variabilities and missed suitable fish habitat variations of as much as 20%. The experiment highlighted the need for a 3D analysis in depicting ecological hot spots such as unsuitable fish habitats in Lake Minnetonka.
IJERT-Water Quality Modeling and Regression Analysis of Vembanad Lake and its Primary Inflows
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/water-quality-modeling-and-regression-analysis-of-vembanad-lake-and-its-primary-inflows https://www.ijert.org/research/water-quality-modeling-and-regression-analysis-of-vembanad-lake-and-its-primary-inflows-IJERTV10IS060185.pdf Water quality modeling involves the prediction of water pollution using mathematical simulation techniques. A typical water quality model consists of a collection of formulations representing physical mechanisms that determine position and momentum of pollutants in a water body. Modeling can be used to assess and predict future water quality situations resulting from different management strategies. Surface water quality models can be useful tools to simulate and predict the levels, distributions, and risks of chemical pollutants in a given water body.Water quality modeling using Artificial Neural Network (ANN) is a computational method animated by the studies of the brain and nervous system. Our project aims to predict the accuracy of water quality models of Vembanad Lake and its primary inflows by performing regression analysis. And it also aims to develop a relationship between water quality parameter and water quality index of Vembanad Lake and its primary inflows.
Journal of Marine Systems, 2004
Helsinki is located on the southern coast of Finland by the Baltic Sea. The ecological state of the archipelago in front of Helsinki is affected by several factors. The effects of local point and scattered loads are mixed with the transboundary effects from neighbouring countries and atmospheric deposition. Municipal waste waters of 800 000 inhabitants of the capital area are released outside the archipelago into the open Gulf of Finland (GoF). In addition, river Vantaanjoki is transporting load originating from agriculture, industry and settlements of the drainage area to the shores of Helsinki. Toxic algal blooms and annoying filamentous algal mats have become a problem to the users of the coastal zone.
Hydrobiologia, 1997
This study assesses the effects of external and internal loading on the nutrient concentrations in an agriculturally loaded shallow lake. Using 13 years of observations of the lake's input and outflow, we calculated the long-term balances of Tot-P and Tot-N. A more detailed balance, which included dissolved nutrients and suspended solids, was estimated for an ice-free period of one year. The contribution of the external load was assessed using a mass-balance model. The internal load was estimated from the nutrient balances and on the basis of sedimentation measurements and bioassays. The drainage basin of the lake provided most of the external nutrient input; the remaining load was derived from atmospheric deposition to the lake. The proportions of river-transported P and N in dissolved form were 25% and 77%, respectively. The lake retained >80% of the external load. Particulate nutrients settled to the bottom and were probably resuspended several times before permanent sedimentation. Dissolved nutrients were bound by primary producers and a high proportion of dissolved P was removed with the fish catch. Dissolved N was also lost via denitrification. The mass-balance model showed that external loading only partly regulated the mean annual nutrient concentrations in the lake. The regulation was probably due to internal loading, which was high despite the efficient net retention of nutrients. During the ice-free period, the temporal variations in nutrient concentrations were controlled almost solely by internal processes, such as resuspension of inorganic and organic bottom matter. Although the internal load of bioavailable P may, under favourable conditions, exceed the external load, the mechanism by which bioavailable P is translocated from the bottom sediments to the water could not be fully identified.
Mathematical Modeling of Trophic State and Nutrient Flows of Lake Karla using the PCLake Model
In the present article, we simulate Lake Karla, an important natural ecosystem under restoration in Greece, operating also as a reservoir. The lake trophic state is characterized as hypertrophic with the expected negative effects on biodiversity. The simulation of Lake Karla is a significant tool in terms of understanding, predicting and managing the ecosystem. We perform simulations using PCLake, a software package for shallow lakes, which provides a full set of parameters, modeling a wide range of physical, chemical and biological variables. The model is calibrated based on the time series of six variables for year 2012 and validated using data of years 2014–2015. We present the nutrient flow dynamics for year 2012 on a trimester basis, and investigate the interrelations of nutrient cycling and trophic state, through observed variables such as phycocyanin and chlorophyll-a concentrations, Carlson Trophic State Index and ratio of Dissolved Inorganic Nitrogen to Soluble Reactive Phosphorus.