Sequential data assimilation for real-time probabilistic flood inundation mapping

Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision-making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential data assimilation (DA) techniques are well known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a DA hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the ensemble Kalman filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach; then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5 %–7 %, while it also provides the basis for sequential updating and real-time flood inundation mapping.

Sequential Data Assimilation for Real-Time Probabilistic Flood Inundation Mapping

Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision making during the emergency of an upcoming flood event. Considering high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential Data Assimilation (DA) techniques are well-known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a Data Assimilation (DA)-hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the Ensemble Kalman Filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach, then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point-source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5–7% while it also provides the basis for sequential updating and real-time flood inundation mapping.

The irreplaceable utility of sequential data assimilation for numerical weather prediction system development: Lessons learned from an experimental HWRF system

Limited-area numerical weather prediction models currently run operationally in the United States follow a “partially-cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data pre-processing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

2D sequential data assimilation in Elmer/Ice with Stokes

<p>Providing suitable initial states is a long-standing problem in numerical modelling of glaciers and ice sheets. Models often require lengthy relaxation periods to dissipate incompatibilities between input datasets gathered over different timeframes, which may lead to the modelled initial state diverging significantly from the real state of the glacier, with consequent effects on the accuracy of the simulation. Sequential data assimilation offers one possibility for resolving this issue: by running the model over a period for which various observational datasets are available and loading observations into the model at the time they were gathered, the model state can be brought into good agreement with the real glacier state at the end of the observational window. This assimilated model state can then be used to initialise prognostic runs without introducing model artefacts or a distorted picture of the actual glacier.</p><p>In this study, we present a framework for conducting sequential data assimilation in a 2D, flowline setting of the open-source, finite-element glacier flow model, Elmer/Ice, and solving the Stokes equations rather than using the shallow shelf approximation. Assimilation is undertaken using the open-source PDAF library developed at the Alfred Wegener Institute. We demonstrate that the set-up allows us to accurately retrieve the bed of a synthetic glacier and present our progress in extending it to a full 3D simulation.</p>