6B.5 Introducing a Hybrid Ensemble and Variational Data Assimilation Method for Improved Hydrologic Predictability

Tuesday, 14 January 2020: 11:30 AM
253A (Boston Convention and Exhibition Center)
Hamid Moradkhani, Univ. of Alabama, Tuscaloosa, AL; and P. Abbaszadeh and K. Gavahi

A number of studies have shown that multivariate data assimilation into the land surface models would improve model predictive skills. Soil moisture, streamflow and Evapotranspiration are among those environmental variables that greatly affect flood forecasting, drought monitoring/prediction, and agricultural production that collectively control the land and atmospheric system. However, land surface models most often do not provide accurate and reliable estimates of fluxes and storages and are subject to large uncertainties stemming from hydrometeorological forcing, model parameters, boundary or initial condition and model structure. Here, we present the state of the art data assimilation methods, covering the evolution of methods, discussing their pros and cons and introduce a novel approach that couples a deterministic four‚Äźdimensional variational (4DVAR) assimilation method with the particle filter that benefits from MCMC and Genetic algorithm, and altogether shows to significantly enhance the hydrologic forecasting. The Evolutionalry Particle Filter with MCMC (EPFM) uses the Genetic Algorithm (GA) to effectively sample the particles to better represent the posterior distribution of model prognostic variables and parameters. This is followed by coupling EPFM and 4DVAR which results in a superior DA approach, the so-called Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN). The method explicitly characterizes the model structural uncertainty during the assimilation process. The application of methods is presented for both flood and drought forecasting while utilizing the remotely sensed observations.
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