Evaluation of a coupled hydrodynamic-closed ecological cycle approach for modelling dissolved oxygen in surface waters

Abstract

The description of intertwined ecological processes in surface waters requires a holistic approach that accounts for spatially distributed hydrological/water quality processes. This study describes a new approach to model dissolved oxygen (DO) based on linked hydrodynamic and closed nutrient cycle ecological models. Long term datasets from the River Dommel (Netherlands) are used to determine: 1) if this methodology is suitable for modelling DO concentrations, 2) the model sensitivity to various levels of nutrients input, and 3) the DO production and consumption processes and their response to nutrient input changes. Results show that seasonal dynamics of DO are well quantified at long timescales; the sensitivity of DO to different pollutant sources exhibits significant seasonal variation and the largest influences on DO are aeration and mineralization of organic material. The approach demonstrates an ability to consider the impacts of nutrient input and long term vegetation maintenance on ecological quality. Software Requirements • Wageningen Lowland Runoff Simulator (WALRUS) developed by Brauer et al.

Modeling the Impact of Extreme River Discharge on the Nutrient Dynamics and Dissolved Oxygen in Two Adjacent Estuaries (Portugal)

“…Regarding the observed differences between predicted and observed data of dissolved oxygen, the model reproduced the theoretical annual standard of dissolved oxygen since, in the summer months, there were lower concentrations of dissolved oxygen (higher consumption of oxygen than production), while in winter, there were higher concentrations of dissolved oxygen (higher production of oxygen than respiration (consumption)), and deviations were similar to other studies [88,89]. Another justification for this theoretical standard is that the higher the water temperature and salinity, the lower the concentration of oxygen; thus, in the summer months, where the water is warmer and more saline, lower concentrations of O 2 are observed, but this could not be found in our observations.…”

“…According to these results, the hydrodynamic and salt and heat transport modules had a good agreement between model predictions and observations. The water quality module demonstrated a reliable fit between predictions and observations, representing well the expected annual patterns of the substances under study, and the deviations of dissolved oxygen were very close to studies carried out by other authors [88,89].…”

Metrology in Urban Drainage and Stormwater Management: Plug and pray

“…Hence, this project is still ongoing, with further improvements to the RTC strategy, as well as its robustness, currently being tested. Moreno-Rodenas et al (2017) andCamacho-Suarez et al (2019) describe research on checking uncertainty in the models used and the impact of spatial and temporal variability of input data and models used to describe the system.…”

Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR

“…of algal growth, especially excessive concentration of nitrogen and phosphorus (Crossman et al 2019). In this regard, a combination of several in situ measurements of historical data was largely used to build robust models for predicting and forecasting DO concentration (Heddam 2017;Suarez et al 2019). Past and present works conducted worldwide proved that models based on intelligent data analytic techniques have accurately estimated DO, and over the years alternative models have been developed.…”

“…Past and present works conducted worldwide proved that models based on intelligent data analytic techniques have accurately estimated DO, and over the years alternative models have been developed. For example, see the studies of (Banerjee et al 2019;Elkiran et al 2019;Mitrović et al 2019;Cao et al 2019;Shi et al 2019;Yang et al 2019;Liu et al 2019;Ross and Stock 2019;Csábrági et al 2019;Suarez et al 2019;Rahman et al 2019;Yahya et al 2019;Tao et al 2019;Zounemat-Kermani et al 2019;Antanasijević et al 2019). Banerjee et al (2019) employed deep artificial neural network (DNN) and standard multiple linear regression (MLR) for predicting DO using sixteen water quality variables as predictors using data from three stations in the Bakreswar Reservoir, India.…”