Implications Of Hydrologic Data Assimilation In Improving Suspended Sediment Load Estimation In Lake Tahoe California
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Implications Of Hydrologic Data Assimilation In Improving Suspended Sediment Load Estimation In Lake Tahoe California
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Author : Marc Leisenring
language : en
Publisher:
Release Date : 2011
Implications Of Hydrologic Data Assimilation In Improving Suspended Sediment Load Estimation In Lake Tahoe California written by Marc Leisenring and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Suspended sediments categories.
Pursuant to the federal Clean Water Act (CWA), when a water body has been listed as impaired, Total Maximum Daily Loads (TMDLs) for the water quality constituents causing the impairment must be developed. A TMDL is the maximum daily mass flux of a pollutant that a waterbody can receive and still safely meet water quality standards. The development of a TMDL and demonstrating compliance with a TMDL requires pollutant load estimation. By definition, a pollutant load is the time integral product of flows and concentrations. Consequently, the accuracy of pollutant load estimation is highly dependent on the accuracy of runoff volume estimation. Runoff volume estimation requires the development of reasonable transfer functions to convert precipitation into runoff. In cold climates where a large proportion of precipitation falls as snow, the accumulation and ablation of snowpack must also be estimated. Sequential data assimilation techniques that stochastically combine field measurements and model results can significantly improve the prediction skill of snowmelt and runoff models while also providing estimates of prediction uncertainty. Using the National Weather Service's SNOW-17 and the Sacramento Soil Moisture Accounting (SAC-SMA) models, this study evaluates particle filter based data assimilation algorithms to predict seasonal snow water equivalent (SWE) and runoff within a small watershed in the Lake Tahoe Basin located in California. A non-linear regression model is then used that predicts suspended sediment concentrations (SSC) based on runoff rate and time of year. Runoff volumes and SSC are finally combined to provide an estimate of the average annual sediment load from the watershed with estimates of prediction uncertainty. For the period of simulation (10/1/1991 to 10/1/1996), the mean annual suspended sediment load is estimated to be 753 tonnes/yr with a 95% confidence interval about the mean of 626 to 956 tonnes/yr. The 95% prediction interval for any given year is estimated to range from approximately 86 to 2,940 tonnes/yr.
Selected Water Resources Abstracts
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Author :
language : en
Publisher:
Release Date : 1990
Selected Water Resources Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Water categories.
Hydrologic Data Assimilation
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Author : Caleb Matthew DeChant
language : en
Publisher:
Release Date : 2010
Hydrologic Data Assimilation written by Caleb Matthew DeChant and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Hydrologic models categories.
This thesis is a combination of two separate studies which examine hydrologic data assimilation techniques: 1) to determine the applicability of assimilation of remotely sensed data in operational models and 2) to compare the effectiveness of assimilation and other calibration techniques. The first study examines the ability of Data Assimilation of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF), is made using a coupled SNOW17 and the Microwave Emission Model for Layered Snowpack model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the Advanced Microwave Scanning Radiometer-Earth Observing System at the 36.5GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. The second study compares the effectiveness of different calibration techniques in hydrologic modeling. Currently, the most commonly used methods for hydrologic model calibration are global optimization techniques. While these techniques have become very efficient and effective in optimizing the complicated parameter space of hydrologic models, the uncertainty with respect to parameters is ignored. This has led to recent research looking into Bayesian Inference through Monte Carlo methods to analyze the ability to calibrate models and represent the uncertainty in relation to the parameters. Research has recently been performed in filtering and Markov Chain Monte Carlo (MCMC) techniques for optimization of hydrologic models. At this point, a comparison of the effectiveness of global optimization, filtering and MCMC techniques has yet to be reported in the hydrologic modeling community. This study compares global optimization, MCMC, the PF, the Particle Smoother, the EnKF and the Ensemble Kalman Smoother for the purpose of parameter estimation in both the HyMod and SAC-SMA hydrologic models.
Spatial And Temporal Dynamics Of Watershed Sediment Delivery Lake Tahoe California
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Author : Andrew Phillip Stubblefield
language : en
Publisher:
Release Date : 2002
Spatial And Temporal Dynamics Of Watershed Sediment Delivery Lake Tahoe California written by Andrew Phillip Stubblefield and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Sediment transport categories.
Improved Uncertainty Assessment Of Hydrologic Models Using Data Assimilation And Stochastic Filtering
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Author : Hamid Moradkhani
language : en
Publisher:
Release Date : 2004
Improved Uncertainty Assessment Of Hydrologic Models Using Data Assimilation And Stochastic Filtering written by Hamid Moradkhani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Estimation theory categories.