Hydrologic Data Assimilation for Operational Streamflow Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hydrologic Data Assimilation for Operational Streamflow Forecasting write by Feyera Aga Hirpa. This book was released on 2013. Hydrologic Data Assimilation for Operational Streamflow Forecasting available in PDF, EPUB and Kindle.
Hydrologic Data Assimilation
Hydrologic Data Assimilation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hydrologic Data Assimilation write by Caleb Matthew DeChant. This book was released on 2010. Hydrologic Data Assimilation available in PDF, EPUB and Kindle. 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.
Ensemble Data Assimilation for Flood Forecasting in Operational Settings
Ensemble Data Assimilation for Flood Forecasting in Operational Settings - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Ensemble Data Assimilation for Flood Forecasting in Operational Settings write by . This book was released on 2018. Ensemble Data Assimilation for Flood Forecasting in Operational Settings available in PDF, EPUB and Kindle. The National Water Center (NWC) started using the National Water Model (NWM) in 2016. The NWM delivers state-of-the-science hydrologic forecasts in the nation. The NWM aims at operationally forecasting streamflow in more than 2,000,000 river reaches while currently river forecasts are issued for 4,000. The NWM is a specific configuration of the community WRF-Hydro Land Surface Model (LSM) which has recently been introduced to the hydrologic community. The WRF-Hydro model, itself, uses another newly-developed LSM called Noah-MP as the core hydrologic model. In WRF-Hydro, Noah-MP results (such as soil moisture and runoff) are passed to routing modules. Riverine water level and discharge, among other variables, are outputted by WRF-Hydro. The NWM, WRF-Hydro, and Noah-MP have recently been developed and more research for operational accuracy is required on these models. The overarching goal in this dissertation is improving the ability of these three models in simulating and forecasting hydrological variables such as streamflow and soil moisture. Therefore, data assimilation (DA) is implemented on these models throughout this dissertation. The results show that short-range forecasts are significantly sensitive to the initial condition and its associated uncertainty. It is shown that quantification of this uncertainty can improve the forecasts by approximately 80%. The findings of this dissertation highlight the importance of DA to extract the information content from the observations and then incorporate this information into the land surface models. The findings could be beneficial for flood forecasting in research and operation.
Advances In Data-based Approaches For Hydrologic Modeling And Forecasting
Advances In Data-based Approaches For Hydrologic Modeling And Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Advances In Data-based Approaches For Hydrologic Modeling And Forecasting write by Bellie Sivakumar. This book was released on 2010-08-10. Advances In Data-based Approaches For Hydrologic Modeling And Forecasting available in PDF, EPUB and Kindle. This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each — stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II) - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II) write by Seon Ki Park. This book was released on 2013-05-22. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II) available in PDF, EPUB and Kindle. This book contains the most recent progress in data assimilation in meteorology, oceanography and hydrology including land surface. It spans both theoretical and applicative aspects with various methodologies such as variational, Kalman filter, ensemble, Monte Carlo and artificial intelligence methods. Besides data assimilation, other important topics are also covered including targeting observation, sensitivity analysis, and parameter estimation. The book will be useful to individual researchers as well as graduate students for a reference in the field of data assimilation.
