Hydrology and Water Quality Survey Near the Gezhouba Dam in the Winter

2013 ◽  
Vol 726-731 ◽  
pp. 3256-3261
Author(s):  
Jia Fei Zhou ◽  
Cong Feng Wang ◽  
De Fu Liu ◽  
Jing Wen Xiang ◽  
Ping Zhao ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Flow Velocity ◽  
Water Column ◽  
Quality Data ◽  
Chinese Sturgeon ◽  
Water Quality Data ◽  
Gezhouba Dam ◽  
Survival Condition

Filed hydrology and water quality data were collected near the Gezhouba Dam early December of 2012 to analyze the response of Chinese Sturgeon survival condition to water temperature, dissolved oxygen (DO), pH, transparency (SD) and bottom flow-velocity. The results showed that water temperature lag is unconspicuous. The water temperature of Gezhouba Dam Sanjiang (GDS) was lower than that of Gezhouba Dam River (GDR), and it hindered propagation of sturgeon eggs. DO decreased fast in the vertical water column of GDS, pH ranged from 7.5 to 7.71. The hydrology and water quality were suitable for the life condition of sturgeon eggs and fry, except index of bottom flow-velocity.

Modeling water quality in an oil sands compensation lake

2015 ◽  
Vol 42 (11) ◽  
pp. 901-909 ◽  
Author(s):  
Jianhua Jiang ◽  
Jerry Vandenberg ◽  
Ian Halket ◽  
Kasey Clipperton ◽  
Richard J. Kavanagh ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Oil Sands ◽  
Fish Habitat ◽  
Environmental Modeling ◽  
Water Quality Model ◽  
Quality Data ◽  
Quality Model ◽  
Water Quality Data

Surface mining in the oil sands region of Alberta, Canada, often requires that mining operators drain lakes or divert streams to access the underlying ore. “Compensation lakes” can be constructed to create new fish habitat to offset the loss of fish habitat due to mining activity and to satisfy conditions under a project’s Fisheries Act Authorization. The design of these lakes requires prediction of future water temperature and dissolved oxygen levels to determine the suitability of the new habitat for fish. These predictions are made using a calibrated hydrodynamic and water quality model. Until recently, there were not any built compensation lakes in the region with enough measured water quality data that could be used to calibrate such a model. This paper uses measured data from Horizon Lake, a recently built compensation lake, to calibrate Generalized Environmental Modeling System of Surfacewaters (GEMSS), a three-dimensional hydrodynamic and water quality model, used to model the lake. Horizon Lake was built in 2008 by Canadian Natural Resources Ltd. and water quality in the lake has been monitored for the last seven years. The results of the model calibration to observed water temperature and dissolved oxygen provide rates and coefficients, notably sediment oxygen demand, that can be used to improve model applications to other planned compensation lakes.

scholarly journals Visualizing Water Quality Sampling-Events in Florida

2015 ◽  
Vol II-4/W2 ◽  
pp. 73-79 ◽  
Author(s):  
M. D. Bolt
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Data Visualization ◽  
Coastal Water ◽  
Environmental Quality ◽  
Additional Data ◽  
Monitoring Program ◽  
Quality Data ◽  
Water Quality Data ◽  
Environmental Narratives

Water quality sampling in Florida is acknowledged to be spatially and temporally variable. The rotational monitoring program that was created to capture data within the state’s thousands of miles of coastline and streams, and millions of acres of lakes, reservoirs, and ponds may be partly responsible for inducing the variability as an artifact. Florida’s new dissolved-oxygen-standard methodology will require more data to calculate a percent saturation. This additional data requirement’s impact can be seen when the new methodology is applied retrospectively to the historical collection. To understand how, where, and when the methodological change could alter the environmental quality narrative of state waters requires addressing induced bias from prior sampling events and behaviors. Here stream and coastal water quality data is explored through several modalities to maximize understanding and communication of the spatiotemporal relationships. Previous methodology and expected-retrospective calculations outside the regulatory framework are found to be significantly different, but dependent on the spatiotemporal perspective. Data visualization is leveraged to demonstrate these differences, their potential impacts on environmental narratives, and to direct further review and analysis.

scholarly journals Fish Rescue during Streamflow Intermittency May Not Be Effective for Conservation of Rio Grande Silvery Minnow

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3371
Author(s):  
Thomas P. Archdeacon ◽  
Tracy A. Diver ◽  
Justin K. Reale
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Fish Assemblages ◽  
Sublethal Effects ◽  
Rio Grande ◽  
Quality Data ◽  
Water Quality Data ◽  
Low Dissolved Oxygen ◽  
Rio Grande Silvery Minnow ◽  
Oxygen Concentrations

Streamflow intermittency can reshape fish assemblages and present challenges to recovery of imperiled species. During streamflow intermittency, fish can be subjected to a variety of stressors, including exposure to crowding, high water temperatures, and low dissolved oxygen, resulting in sublethal effects or mortality. Rescue of fishes is often used as a conservation tool to mitigate the negative impacts of streamflow intermittency. The effectiveness of such actions is rarely evaluated. Here, we use multi-year water quality data collected from isolated pools during rescue of Rio Grande silvery minnow Hybognathus amarus, an endangered minnow. We examined seasonal and diel water quality patterns to determine if fishes are exposed to sublethal and critical water temperatures or dissolved oxygen concentrations during streamflow intermittency. Further, we determined survival of rescued Rio Grande silvery minnow for 3–5 weeks post-rescue. We found that isolated pool temperatures were much warmer (>40 °C in some pools) compared to upstream perennial flows, and had larger diel fluctuations, >10 °C compared to ~5 °C, and many pools had critically low dissolved oxygen concentrations. Survival of fish rescued from isolated pools during warmer months was <10%. Reactive conservation actions such as fish rescue are often costly, and in the case of Rio Grande silvery minnow, likely ineffective. Effective conservation of fishes threatened by streamflow intermittency should focus on restoring natural flow regimes that restore the natural processes under which fishes evolved.

scholarly journals River Dissolved Oxygen Prediction Based on Random Forest and LSTM

2021 ◽  
Vol 37 (5) ◽  
pp. 901-910
Author(s):  
Juan Huan ◽  
Bo Chen ◽  
Xian Gen Xu ◽  
Hui Li ◽  
Ming Bao Li ◽  
...  
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Water Quality Management ◽  
Quality Data ◽  
Mean Square ◽  
Water Quality Data ◽  
Better Than

HighlightsRandom Forest (RF) and LSTM were developed for river DO prediction.PH is the most important feature affecting DO prediction.The model base on RF is better than the model not on RF, and the dimensionality of the input data is reduced by RF.RF-LSTM model is outperformed SVR, RF-SVR, BP, RF-BP, LSTM, RNN models in DO prediction.Abstract. In order to improve the prediction accuracy of dissolved oxygen in rivers, a dissolved oxygen prediction model based on Random Forest (RF) and Long Short Term Memory networks (LSTM) is proposed. First, the Random Forest performs feature selection, which reduces the input dimension of the data and eliminates the influence of irrelevant variables on the prediction of dissolved oxygen. Then build the LSTM river dissolved oxygen prediction model to fit the relationship between water quality data and dissolved oxygen, and finally use real water quality data in the river for verification. The experimental results show that the mean square error (MSE), absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of the RF-LSTM model are 0.658, 0.528, 13.502, 0.811, 0.744, respectively, which are better than other models. The RF-LSTM model has good predictive performance and can provide a reference for river water quality management. Keywords: Dissolved oxygen prediction, LSTM, Random forest, Time series, Water quality management.

Modelling sub-daily phytoplankton dynamics and analysing primary production controls in the lower Thames catchment, UK

2020 ◽  
Author(s):  
Devanshi Pathak ◽  
Michael Hutchins ◽  
François Edwards
Keyword(s):  
Water Quality ◽  
Primary Production ◽  
Water Temperature ◽  
High Frequency ◽  
Phytoplankton Growth ◽  
Quality Data ◽  
Phytoplankton Dynamics ◽  
Water Quality Data ◽  
River Thames ◽  
The Impact

&lt;p&gt;River phytoplankton provide food for primary consumers, and are a major source of oxygen in many rivers. However, high phytoplankton concentrations can hamper river water quality and ecosystem functioning, making it crucial to predict and prevent harmful phytoplankton growth in rivers. In this study, we modify an existing mechanistic water quality model to simulate sub-daily changes in water quality, and present its application in the River Thames catchment. So far, the modelling studies in the River Thames have focused on daily to weekly time-steps, and have shown limited predictive ability in modelling phytoplankton concentrations. With the availability of high-frequency water quality data, modelling tools can be improved to better understand process interactions for phytoplankton growth in dynamic rivers. The modified model in this study uses high-frequency water quality data along a 62 km stretch in the lower Thames to simulate river flows, water temperature, nutrients, and phytoplankton concentrations at sub-daily time-steps for 2013-14. Model performance is judged by percentage error in mean and Nash-Sutcliffe Efficiency (NSE) statistics. The model satisfactorily simulates the observed diurnal variability and transport of phytoplankton concentrations within the river stretch, with NSE values greater than 0.7 at all calibration sites. Phytoplankton blooms develop within an optimum range of flows (16-81 m&lt;sup&gt;3&lt;/sup&gt;/s) and temperature (11-18&amp;#176; C), and are largely influenced by phytoplankton growth and death rate parameters. We find that phytoplankton growth in the lower Thames is mainly limited by physical controls such as residence time, light, and water temperature, and show some nutrient limitation arising from phosphorus depletion in summer. The model is tested under different future scenarios to evaluate the impact of changes in climate and management conditions on primary production and its controls. Our findings provide support for the argument that the sub-daily modelling of phytoplankton is a step forward in better prediction and management of phytoplankton dynamics in river systems.&lt;/p&gt;

scholarly journals The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1124 ◽  
Author(s):  
Zaher Yaseen ◽  
Mohammad Ehteram ◽  
Ahmad Sharafati ◽  
Shamsuddin Shahid ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Bat Algorithm ◽  
Specific Conductance ◽  
Least Square ◽  
Quality Data ◽  
Support Vector ◽  
Water Quality Data ◽  
Water Quality Variables ◽  
Do Concentration

The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.

scholarly journals Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN)

2019 ◽  
Vol 55 (1) ◽  
pp. 106-118 ◽  
Author(s):  
Senlin Zhu ◽  
Salim Heddam
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Three Gorges Reservoir ◽  
Model Performance ◽  
Quality Data ◽  
Urban Rivers ◽  
Three Gorges ◽  
Water Quality Data ◽  
The Three Gorges

Abstract In the present study, two non-linear mathematical modelling approaches, namely, extreme learning machine (ELM) and multilayer perceptron neural network (MLPNN) were developed to predict daily dissolved oxygen (DO) concentrations. Water quality data from four urban rivers in the backwater zone of the Three Gorges Reservoir, China were used. The water quality data selected consisted of daily observed water temperature, pH, permanganate index, ammonia nitrogen, electrical conductivity, chemical oxygen demand, total nitrogen, total phosphorus and DO. The accuracy of the ELM model was compared with the standard MLPNN using several error statistics such as root mean squared error, mean absolute error, the coefficient of correlation and the Willmott index of agreement. Results showed that the ELM and MLPNN models perform well for the Wubu River, acceptably for the Yipin River and moderately for the Huaxi River, while poor model performance was obtained at the Tributary of Huaxi River. Model performance is negatively correlated with pollution level in each river. The MLPNN model slightly outperforms the ELM model in DO prediction. Overall, it can be concluded that MLPNN and ELM models can be applied for DO prediction in low-impacted rivers, while they may not be appropriate for DO modelling for highly polluted rivers. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

scholarly journals Prediction of the Concentration of Dissolved Oxygen in Running Water by Employing A Random Forest Machine Learning Technique

2020 ◽  
Author(s):  
Mohammad Hafez Ahmed
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Random Forest ◽  
Dissolved Oxygen ◽  
Water Temperature ◽  
Quality Data ◽  
Linear Regression Method ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Input Variables

Dissolved oxygen (DO) is a key indicator in the study of the ecological health of rivers. Modeling DO is a major challenge due to complex interactions among various process components of it. Considering the vital importance of it in water bodies, the accurate prediction of DO is a critical issue in ecosystem management. Given the intricacy of the current process-based water quality models, a data-driven model could be an effective alternative tool. In this study, a random forest machine learning technique is employed to predict the DO level by identifying its major drivers. Time-series of half-hourly water quality data, spanning from 2007 to 2019, for the South Branch Potomac River near Springfield, WV, are obtained from the United States Geological Survey database. Key drivers are identified, and models are formulated for different scenarios of input variables. The model is calibrated for each input scenario using 80% of the data. Water temperature and pH are found to be the most influential predictors of DO. However, satisfactory model performance is achieved by considering water temperature, pH, and specific conductance as input variables. The model validation is made by predicting DO concentrations for the remaining 20% of the data. The comparison with the traditional multiple linear regression method shows that the random forest model performs significantly better. The study insights are, therefore, expected to be useful to estimate stream/river DO levels at various sites with a minimum number of predictors and help build a sturdy framework for ecosystem health management across an environmental gradient.

Long-term variations of water quality in a reservoir in China

2012 ◽  
Vol 65 (8) ◽  
pp. 1454-1460 ◽  
Author(s):  
Y. Y. Chen ◽  
C. Zhang ◽  
X. P. Gao ◽  
L. Y. Wang
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Quality Data ◽  
Water Quality Data ◽  
Yuqiao Reservoir

To study the spatial and temporal trends of water quality in the Yuqiao Reservoir (Ji County, Tianjin) in China, water quality data for ten physical and chemical parameters from three monitoring stations (S1, S2 and S3) was collected from 1989 to 2007 and from an other three stations (S4, S5 and S6) during the period of 1999–2007. A one-way ANOVA was employed to evaluate the spatial variation of water quality for each station. The results showed that there were statistically significant spatial differences for most water quality parameters except temperature and dissolved oxygen in the entire reservoir, and the concentrations of most parameters were higher in the uppermost part of the reservoir. The temporal trend study was conducted using the Seasonal–Kendall's test. The results revealed improving trends of water quality from 1989 to 2007, including a reduction of total phosphorous, temperature and biochemical oxygen demand and an increase of dissolved oxygen. High N:P ratios, ranging from 52.61 to 78.75, indicated that the reservoir was a phosphorous-limited environment. This study suggests long-term spatial and temporal variations of water quality in the Yuqiao Reservoir, which could be informative for water quality managers and scientists.

Sign in / Sign up

Export Citation Format

Share Document