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.

Mapping surface water quality in Myanmar using aquatic drones

2021 ◽  
Author(s):  
Rui Pedroso de Lima ◽  
Thom Bogaard ◽  
Robbert De Lange
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Dissolved Oxygen ◽  
Surface Water Quality ◽  
Quality Data ◽  
Water Quality Data ◽  
Low Dissolved Oxygen ◽  
Local Stakeholders ◽  
Quality Maps ◽  
Floating Gardens

&lt;p&gt;Water resources in Myanmar are increasingly affected by anthropogenic pressure and climate change related impacts. At the Inle Lake a unique village is located on the water in close proximity to intense fishing/farming activities. The nearby floating gardens provide invaluable resources for local communities, who are highly vulnerable to changes to water quality in the lake. Diversely, within the city of Yangon, the Kandawgyi lake is a popular recreational area which has become heavily affected by excessive algae proliferation. The deterioration of water quality Is likely caused by uncontrolled untreated wastewater, and poses a risk to the citizens. Finally, rivers such as the Pan Hlaing River, flow through industrial zones and collect waste water discharges.&lt;/p&gt;&lt;p&gt;Monitoring in these regions is scarce and limited to a few point-sampling locations. Local stakeholders lack adequate tools to monitor the needed parameters and are in need of reliable and updated baseline water quality data to support them in setting-up sustainable water management strategies. Tools such as aquatic drones and in-situ sensors are innovative ways of monitoring water quality and ecology that could contribute for effectively gathering valuable environmental data.&lt;/p&gt;&lt;p&gt;In this project, aquatic drones (both underwater and surface) were equipped with water quality sensors and cameras for low-cost and rapid assessment of surface water quality at high spatial resolution. The drones are able to navigate autonomously through way-points while collecting geo-referenced data. This study aims at field-testing of two affordable aquatic drones with sensors to map water quality parameters in different types of water systems (large lake, urban lake, river). This study reports the challenges encountered, and evaluates the resulting dataset/maps are in relation to the cost and value for the local stakeholders (ongoing research).&lt;/p&gt;&lt;p&gt;At the Inle Lake, results show varying concentrations of the different parameters that were measured. Low dissolved oxygen levels were found within the villages and underneath floating gardens, while chlorophyll-a and cyanobacteria levels were low across the whole lake. Underwater images show the presence of fish and provide insights into the aquatic ecosystems. At the Kandawgyi Lake, the generated water quality maps illustrate the spatial distribution of the different parameters, and two main areas of contamination could be identified (high algae content, low dissolved oxygen, high E-coli concentrations). At the Pan Hlaing river, the plotted data show degrading levels of dissolved oxygen concentrations, indicating potential effects caused by industry outlets.&lt;/p&gt;&lt;p&gt;The water quality maps that were generated with this data are very illustrative of the condition of the water bodies and the location of contaminations hotspots. The measurement process was accompanied by stakeholders and local universities, which contributed to stimulate capacity building and to create awareness for water quality related problems. As follow-up activities, these results will be used to draft a long-term water quality monitoring plan for local Myanmar students to continue collecting water quality data at these lakes. The detected issues are being discussed with local stakeholders, as well as the possibilities for establishing a larger scale monitoring campaign using this type of monitoring tools.&lt;/p&gt;

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.

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.

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.

Assessing the potential of remote sensing-derived water quality data to explain variations in fish assemblages and to support fish status assessments in large lakes

Hydrobiologia ◽  
2016 ◽  
Vol 780 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Alfred Sandström ◽  
Petra Philipson ◽  
Anders Asp ◽  
Thomas Axenrot ◽  
Anders Kinnerbäck ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Fish Assemblages ◽  
Quality Data ◽  
Large Lakes ◽  
Water Quality Data

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.

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