Three-dimensional management model for Lake Mead, Nevada, Part 2: Findings and applications

Preston, A.; Hannoun, Imad A.; List, E. John; Rackley, Ira; Tietjen, Todd

doi:10.1080/10402381.2014.927942

Cited by 7 publications

(10 citation statements)

References 7 publications

(16 reference statements)

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“…Because elevated SC is an indication of the presence of treated-wastewater-rich water from Las Vegas Wash at the SNWA drinking water intake, this comparison shows, unsurprisingly, that seasonal variation in WDOCs at the bottom of Lake Mead tracks closely with the presence of wastewater there. The seasonal variation in SC at the bottom of Lake Mead depends on circulation patterns that are described in detail elsewhere (e.g., LaBounty and Burns 2005; Preston et al 2014b). Two of these circulation patterns are most important.…”

Section: Research Task 5: Evaluating Seasonal Influences On the Chemimentioning

confidence: 96%

“…Refining this assumption and testing the assumption that chemicals might mix completely in Boulder Basin over time scales of many months is a lake science question that is beyond the scope of this exercise. Much progress on these topics has been made using three-dimensional hydrodynamic modeling (Preston et al 2014a(Preston et al , 2014b. If the instructor or students are uncomfortable with the assumption that relative volume changes of Boulder Basin will mirror those of Lake Mead or that chemicals will mix throughout Boulder Basin as they reach the sampling point at its bottom, the evaluation of the claims of Benotti et al (2010) can be done using the lake elevation time series included in "Lake_Mead_volume_SC_elevation_time_ series_monthly.csv.…”

Section: Research Task 4: Evaluation Of Dilution As a Mechanism For Tmentioning

confidence: 99%

“…When the temperature of the water of Las Vegas Wash, which is rich in treated wastewater, differs from that of Lake Mead, the inflowing water of Las Vegas Wash will float on top of Lake Mead water or plunge to the bottom of the reservoir. The former occurs in spring and early summer when the wash has warmed more rapidly than the reservoir, and the latter occurs in autumn and early winter when the wash has cooled more rapidly than the reservoir (Preston et al 2014b). While these processes are seasonal, their timings and magnitudes are not consistent between years and they are most uncertain in winter.…”

Section: Research Task 5: Evaluating Seasonal Influences On the Chemimentioning

confidence: 99%

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Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition

Wildman

2017

Journal of Statistics Education

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A recent paper published time series of concentrations of chemicals in drinking water collected from the bottom of Lake Mead, a major American water supply reservoir. Data were compared to water level using only linear regression. This creates an opportunity for students to analyze these data further. This article presents a structured introduction to time series decomposition that compares long-term and seasonal components of a time series of a single chemical (meprobamate) with those of two supporting datasets (reservoir volume and specific conductance). For the chemical data, this must be preceded by estimation of missing datum points. Results show that linear regression analyses of time series data obscure meaningful detail and that specific conductance is the important predictor of seasonal chemical variations. To learn this, students must execute a linear regression, estimate missing data using local regression, decompose time series, and compare time series using cross-correlation. Complete R code for these exercises appears in the supplementary information. This article uses real data and requires that students make and justify key decisions about the analysis. It can be a guided or an individual project. It is scalable to instructor needs and student interests in ways that are identified clearly in this article.

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Section: Research Task 5: Evaluating Seasonal Influences On the Chemimentioning

confidence: 96%

Section: Research Task 4: Evaluation Of Dilution As a Mechanism For Tmentioning

confidence: 99%

Section: Research Task 5: Evaluating Seasonal Influences On the Chemimentioning

confidence: 99%

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Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition

Wildman

2017

Journal of Statistics Education

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“…However, the results of hydrodynamic models can be scaled to the desired spatial and temporal resolution of an entire study area [11,12]. Many studies have shown that there is significant value in performing comprehensive hydrodynamic simulations of lakes and reservoirs around the world to evaluate possible pollutant concentration levels and protect and manage water resources [7,[13][14][15]. Given the importance of understanding Poyang Lake's hydrodynamic patterns, it is urgent that a reliable hydrodynamic model be established as a basis for the effective study of the water quality of the lake.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic and Inundation Modeling of China’s Largest Freshwater Lake Aided by Remote Sensing Data

Zhang

Feng

et al. 2015

Remote Sensing

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China's largest freshwater lake, Poyang Lake, is characterized by rapid changes in its inundation area and hydrodynamics, so in this study, a hydrodynamic model of Poyang Lake was established to simulate these long-term changes. Inundation information was extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and used to calibrate the wetting and drying parameter by assessing the accuracy of the simulated inundation area and its boundary. The bottom friction parameter was calibrated using current velocity measurements from Acoustic Doppler Current Profilers (ADCP). The results show the model is capable of predicting the inundation area dynamic through cross-validation with remotely sensed inundation data, and can reproduce the seasonal dynamics of the water level, and water discharge through a comparison with hydrological data. Based on the model results, the characteristics of the current velocities of the lake in the wet season and the dry season of the lake were explored, and the potential effect of the current dynamic on water quality patterns was discussed. The model is a OPEN ACCESSRemote Sens. 2015, 7 4859 promising basic tool for prediction and management of the water resource and water quality of Poyang Lake.

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“…Ultimately, the calibrated and validated LMM was used to investigate a variety of potential changes to Lake Mead with respect to these primary drivers, as well as to inform management decisions regarding construction of new intake and diffuser infrastructures with total project costs >US$1.4B. These applications of the LMM are presented in a companion paper (Preston et al 2014), whereas the focus of this paper is the development, calibration, and validation of the 3-D hydrodynamic and water quality model.…”

mentioning

confidence: 99%

Three-dimensional management model for Lake Mead, Nevada, Part 1: Model calibration and validation

Preston¹,

Hannoun²,

List³

et al. 2014

Lake and Reservoir Management

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Lake Mead, the largest-volume man-made reservoir in the United States, faces a variety of challenges, including increasing demands for municipal water, 10 years of drought in the Colorado River system, lower water surface elevations, discharges of highly treated wastewater effluent, invasive mussels, and climate change. Lake Mead is an important source of water for 25 million people in the southwest U.S. and is also a National Recreation Area. Thus, it is imperative that the lake be adequately protected and managed to meet the often competing needs of the multiple users. A well-calibrated and validated three-dimensional hydrodynamic and water quality model of Lake Mead has been a key component of this management strategy, enabling hydrodynamics and water quality within the reservoir to be predicted and assessed for a wide range of anticipated conditions. The model was developed using the ELCOM and CAEDYM simulation codes, and has been calibrated and validated for the 2000-2008 period using measured field data for temperature, conductivity, perchlorate, bromide, chlorophyll a, nutrients (phosphorus and nitrogen), total organic carbon, pH, and dissolved oxygen. The model captured the hydrodynamics and water quality of this complex system well, and the standard errors of the model results for selected parameters were found to be larger than, but of the same order of magnitude, as the accuracy of the measured field data.

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Three-dimensional management model for Lake Mead, Nevada, Part 2: Findings and applications

Cited by 7 publications

References 7 publications

Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition

Long-Term and Seasonal Trends of Wastewater Chemicals in Lake Mead: An Introduction to Time Series Decomposition

Hydrodynamic and Inundation Modeling of China’s Largest Freshwater Lake Aided by Remote Sensing Data

Three-dimensional management model for Lake Mead, Nevada, Part 1: Model calibration and validation

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