7. Advancing data assimilation science for operational hydrology

The research community has demonstrated large potential of DA in improving predictive skill and reducing uncertainty in hydrologic forecasts. Within the operational community, however, automatic DA is still a relatively new concept and its adoption and implementation often face considerable challenges. This sub-theme discusses theoretical and practical aspects of hydrologic DA that require additional attention by the research community to advance DA science for operational hydrology and to accelerate research-to-operations transition.

Chair: Hamid Moradkhani, Portland State University
Keynote 1: Milija Zupanski, CIRA: “Advancing Data Assimilation Science for Operational Hydrology: Methodology, Computation, and Algorithms”
Data assimilation (DA) for operational hydrology is an important area of hydrologic development that addresses the transitioning of DA research into cost-effective operational forecasting. This effort requires close collaborations among DA developers, hydrologic modelers, and operational forecasters. Several challenges of DA theory and practice that have an impact on DA for operational hydrology will be discussed in this presentation, including: (a) data assimilation methodology, (b) computational implications, and (c) algorithmic aspects. We will also discuss the development of coupled hydrology-land-atmosphere-chemistry and its relevance to DA for operational hydrology. Finally, a new framework for unified ensemble-variational data assimilation, with a potential for use in operational hydrology, will be presented.
Keynote 2: Hamid Moradkhani, Portland State University: “Combined Data Assimilation and Multimodeling for Seasonal Hydrologic Forecasting- A more Complete Characterization of Uncertainty”
Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This presentation proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate total uncertainty, thus ignoring initial condition and modeling uncertainty. In order to manage all three sources of uncertainty, this study combines ESP with ensemble data assimilation, to quantify initial condition uncertainty, and Sequential Bayesian Combination, to quantify model errors. This gives a more complete description of seasonal hydrologic forecasting uncertainty. Results from this experiment suggest that the proposed method increases the reliability of probabilistic forecasts, particularly with respect to the tails of the predictive distribution. In addition, the presentation includes recent development in advancing data assimilation theory and practice using Particle filter and Markov Chain Monte Carlo method where the objective perturbation approach by means of variable variance multiplier is also elaborated.
Oral 1: Seong Jin Noh, Korea Institute of Construction Technology, Korea: “Impacts of State Updating of Combined Hydrologic and Hydraulic Models for Streamflow Forecasting via Data Assimilation”
Oral 2: Yohei Sawada, The University of Tokyo, Japan: “Improving the Performance of an Eco-Hydrological Model to Estimate Soil Moisture and Vegetation Dynamics by Assimilating Microwave Signal”
Oral 3: Kyunghyun Kim, National Institute of Environmental Research, S. Korea: “Ensemble Data Assimilation of Water Quality Variables”
Oral 4: Arezoo Rafieei Nasab, The University of Texas at Arlington, USA: “Comparative Evaluation of EnKF and MLEF for Assimilation of Streamflow Data into NWS Operational Hydrologic Models”
Oral 5: Ripendra Awal, Prairie View A&M University, USA: “Analysis of Streamflow Trends in San Jacinto River Basin, Texas”