Water resources summary for the Snake River and Jackson Lake Reservoir in Grand Teton National Park and John D. Rockefeller, Jr. Memorial Parkway: Preliminary analysis of 2016 data

2021 ◽  
Author(s):  
Jana Cram ◽  
Mary Levandowski ◽  
Kaci Fitzgibbon ◽  
Andrew Ray
Keyword(s):  
Water Quality ◽  
National Park ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Snake River ◽  
Peak Flows ◽  
Reservoir Storage ◽  
High Flows ◽  
Daily Flows

This report summarizes discharge and water quality monitoring data for the Snake River and Jackson Lake reservoir levels in Grand Teton National Park and John D. Rockefeller, Jr. Memorial Parkway for calendar year 2016. Annual and long-term discharge summaries and an evaluation of chemical conditions relative to state and federal water quality standards are presented. These results are considered provisional, and may be subject to change. River Discharge: Hydrographs for the Snake River at Flagg Ranch, WY, and Moose, WY, exhibit a general pattern of high early summer flows and lower baseflows occurring in late summer and fall. During much of 2016, flows at the Flagg Ranch monitoring location were similar to the 25th percentile of daily flows at that site. Peak flows at Flagg Ranch were similar to average peak flow from 1983 to 2015 but occurred eleven days earlier in the year compared to the long-term average. Peak flows and daily flows at the Moose monitoring station were below the long-term average. Peak flows occurred four days later than the long-term average. During summer months, the unnatural hydro-graph at the Moose monitoring location exhibited signs of flow regulation associated with the management of Jackson Lake. Water Quality Monitoring in the Snake River: Water quality in the Snake River exhibited seasonal variability over the sampling period. Specifically, total iron peaked during high flows. In contrast, chloride, sulfate, sodium, magnesium, and calcium levels were at their annual minimum during high flows. Jackson Lake Reservoir: Reservoir storage dynamics in Jackson Lake exhibit a pattern of spring filling associated with early snowmelt runoff reaching maximum storage in mid-summer (on or near July 1). During 2016, filling water levels and reservoir storage began to increase in Jackson Lake nearly two weeks earlier than the long-term average and coincident with increases in runoff-driven flows in the Snake River. Although peak storage in Jackson Lake was larger and occurred earlier than the long-term average, minimum storage levels were similar to the long-term average.

Real‐Time Water Quality Monitoring at a Great Lakes National Park

2018 ◽  
Vol 47 (5) ◽  
pp. 1086-1093 ◽  
Author(s):  
Muruleedhara N. Byappanahalli ◽  
Meredith B. Nevers ◽  
Dawn A. Shively ◽  
Ashley Spoljaric ◽  
Christopher Otto
Keyword(s):  
Water Quality ◽  
Great Lakes ◽  
Real Time ◽  
National Park ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals The use of bed sediments in water quality studies and monitoring programs

2017 ◽  
Vol 375 ◽  
pp. 11-17 ◽  
Author(s):  
Arthur J. Horowitz ◽  
Kent A. Elrick
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Water Quality Monitoring ◽  
Flood Plain ◽  
Quality Monitoring ◽  
Sediment Chemistry ◽  
Regulatory Requirements ◽  
Bed Sediments ◽  
Monitoring Programs

Abstract. In most water quality monitoring programs, either filtered water (dissolved) or suspended sediment (either whole water or separated suspended sediment) are the traditional sample media of choice. This results both from regulatory requirements and a desire to maintain consistency with long-standing data collection procedures. Despite the fact that both bed sediments and/or flood plain deposits have been used to identify substantial water quality issues, they rarely are used in traditional water quality monitoring programs. The usual rationale is that bed sediment chemistry does not provide the temporal immediacy that can be obtained using more traditional sample media (e.g., suspended sediment, water). However, despite the issue of temporal immediacy, bed sediments can be used to address/identify certain types of water quality problems and could be employed more frequently for that purpose. Examples where bed sediments could be used include: (1) identifying potential long-term monitoring sites/water quality hot spots, (2) establishing a water quality/geochemical history for a particular site/area, and (3) as a surrogate for establishing mean/median chemical values for suspended sediment.

Long-term water quality monitoring in Lake Simcoe based on the empirical method and machine learning

2021 ◽  
Author(s):  
Bo Wang ◽  
Jinhui Huang ◽  
Hongwei Guo
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Total Phosphorus ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Empirical Method ◽  
Quality Monitoring ◽  
Lake Simcoe

<p><strong>Abstract:</strong> The traditional water quality monitoring methods are time-consuming and laborious, which can only reflect the water quality status of single point scale, and have some problems such as irregular sampling time and limited sample size. Remote sensing technology provides a new idea for water quality monitoring, and the temporal resolution of MODIS is one day, which is suitable for long-term, continuous real-time large-scale monitoring of lakes. In this study, Lake Simcoe (located in Ontario, Canada) was selected as the research area. The long-term spatiotemporal changes of chlorophyll-a, transparency, total phosphorus and dissolved oxygen were analyzed by comparing the empirical method, multiple linear regression, random forest and neural network with MODIS data. Finally, the water quality condition of Lake Simcoe is evaluated. The results show that the overall retrieval results of two machine learning models are better than that of the empirical method. The optimal retrieval accuracy R² for four water quality parameters are 0.976, 0.988, 0.943, 0.995, and RMSE are 0.13μg/L, 0.3m, 0.002mg/L and 0.14mg/L, respectively. On the annual scale, the annual mean values of the four water quality parameters during the 10-year period from 2009 to 2018 were 1.37μg/L, 6.9m, 0.0112mg/L and 10.17mg/L, respectively. On the monthly scale, chlorophyll a, total phosphorus and dissolved oxygen first decreased and then increased at the time of year. The higher concentrations of chlorophyll a and total phosphorus in the south and east of Lake Simcoe are related to the input of nutrients from the surrounding residents and farmland.</p><p><strong>Key words: </strong>water quality monitoring; MODIS; empirical method; machine learning</p>

A Nonparametric Long-Term Trend Analysis Using Water Quality Monitoring Data in Nam-River

2018 ◽  
Vol 27 (11) ◽  
pp. 1029-1048
Author(s):  
Kang-Young Jung ◽  
Myojeong Kim ◽  
Kwang Duck Song ◽  
Kwon Ok Seo ◽  
Seong Jo Hong ◽  
...  
Keyword(s):  
Water Quality ◽  
Trend Analysis ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Term Trend ◽  
Long Term Trend

Combining Ecohydrological Catchment Modelling and Water Quality Monitoring to Assess Surface Water Pollution in the Swist River Basin

2020 ◽  
Author(s):  
Alexander Ahring ◽  
Marvin Kothe ◽  
Christian Gattke ◽  
Ekkehard Christoffels ◽  
Bernd Diekkrüger
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Surface Waters ◽  
Water Quality Monitoring ◽  
Pollutant Emissions ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Quality Data

<p>Inland surface waters like rivers, streams, lakes and reservoirs are subject to anthropogenic pollutant emissions from various sources. These emissions can have severe negative impacts on surface water ecology, as well as human health when surface waters are used for recreational activities, irrigation of cropland or drinking water production. In order to protect aquatic ecosystems and freshwater resources, the European Water Framework Directive (WFD) sets specific quality requirements which the EU member states must meet until 2027 for every water body.</p><p>Implementing effective measures and emission control strategies requires knowledge about the important emission pathways in a given river basin. However, due to the abundance of pollution sources and the heterogeneity of emission pathways in time and space, it is not feasible to gain this knowledge via water quality monitoring alone. In our study, we aim to combine SWAT ecohydrological modelling and long term water quality monitoring data to establish a spatially differentiated nitrogen emission inventory on the sub-catchment scale. SWAT (short for Soil and Water Assessment Tool) is a semi-distributed, dynamic and process-driven watershed model capable of simulating long term hydrology as well as nutrient fluxes on a daily time step.</p><p>The study area is the Swist river basin in North Rhine-Westphalia (Germany). Belonging to the Rhine river system, the Swist is the largest tributary of the Erft River and drains a basin area of approximately 290 km². As part of its legal obligations and research activities, the Erftverband local waterboard collects a large variety of long term monitoring data in the Swist river catchment, which is available for this study. This includes operational data from the wastewater treatment plants in the watershed, discharge data from four stream gauging stations, river water quality data from continuous and discontinuous monitoring, groundwater quality data as well as quality data from surface, sub-surface and tile drainage runoff from various land uses.</p><p>Our contribution will be made up of two equal parts: First, we will present our water quality monitoring activities in the catchment and the related data pool outlined above, with special emphasis on recent monitoring results from agricultural tile drainages. Apart from nutrients and other pollutants, the data suggests considerable inputs of herbicide transformation products like Chloridazon-Desphenyl (maximum concentration measured: 15 µg/l) via this pathway. Second, we will explain how we integrate the monitoring data into the SWAT simulations and how we tackle related challenges like parameter equifinality (meaning that multiple parameter sets can yield similar or identical model outputs). The overall goal is to take all possible emission pathways into consideration, including those often neglected in past SWAT studies, like tile drainages and combined sewer overflows (CSO). As the Swist catchment is affected by groundwater extraction due to lignite mining in the Lower Rhine Bay area, we will discuss how this is considered during SWAT model setup and calibration, and will present first simulation results concerning catchment hydrology.</p>

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