Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

Ziliani, Matteo G.; Ghostine, Rabih; Ait‐El‐Fquih, Boujemaa; McCabe, Matthew F.

doi:10.1016/j.jhydrol.2019.123924

Cited by 36 publications

(23 citation statements)

References 51 publications

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“…In general, the uncertainties of GHMs-based flood simulations might come from (1) the biases in climate forcing data as inputs of the GHMs, (2) the errors in describing hydrological processes (representation of hydrological process and discrepancy of models with real hydrological process) at the scale of grid cell (3) the limitation of river routing approach such as model parameters related to physical routing process [10,11,[46][47][48]. The large variations in GHMs' performance in different basins, climate zones and various flood-relevant indices, call for a careful selection of GHMs and improvement in techniques adopted for future flood simulations.…”

Section: Evaluation Of Ghms-based Flood Simulation Under Different CLmentioning

confidence: 99%

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Tao

Sun

Gentine

et al. 2019

Environ. Res. Lett.

111

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A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971-2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics-machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.

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Section: Evaluation Of Ghms-based Flood Simulation Under Different CLmentioning

confidence: 99%

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Tao

Sun

Gentine

et al. 2019

Environ. Res. Lett.

111

View full text Add to dashboard Cite

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“…While this ensemble size is small with respect to the domain size and the size of the state vector, given the results shown in Hostache et al (2018) this was considered an appropriate trade-off for computational speed. Even though studies show that increasing the ensemble size may result in improved assimilation performance (e.g., Ziliani et al, 2019), using large ensemble sizes remains challenging even with current generation computationally efficient hydraulic models and computing power. In theory, the ideal ensemble size for particle filtering should exceed the length of the state vector by several orders of magnitude, which for this study (∼63,500 wet cells) would mean an ensemble size >10 6 (Banister & Nichols, 2010).…”

Section: Ensemble Generationmentioning

confidence: 99%

“…As evident from the comprehensive review of hydraulic data assimilation (DA) studies in Table 7 of Grimaldi et al. (2016), most studies have focused on assimilating synthetic (Garambois et al., 2019; Giustarini et al., 2011; Matgen et al., 2010; Tuozzolo et al., 2019), in situ (Van Wesemael et al., 2019; Ziliani et al., 2019), or remote sensing‐derived water levels (RSD‐WLs) (Giustarini et al., 2012; Lai & Monnier, 2009). Unlike water depth, which is a state variable of hydraulic models, flood extents are derived prognostic variables (Lai et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation

Dasgupta

Hostache

Ramsankaran

et al. 2021

Water Resources Research

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Accurate flood inundation forecasts have the potential to minimize socioeconomic losses, but uncertainties in inflows propagated from the precipitation forecasts result in large prediction errors. Recent studies suggest that by assimilating independent flood observations, inherent uncertainty in hydraulic flood inundation modeling can be mitigated. Satellite observations from Synthetic Aperture Radar (SAR) sensors, with demonstrated flood monitoring capability, can thus be used to reduce flood forecast uncertainties through assimilation. However, researchers have struggled to develop an appropriate cost function to determine the innovation to be applied at each assimilation time step. Thus, a novel likelihood function based on mutual information (MI) is proposed here, for use with a particle filter‐based (PF) flood extent assimilation framework. Using identical twin experiments, synthetic SAR‐based probabilistic flood extents were assimilated into the hydraulic model LISFLOOD‐FP using the proposed PF‐MI algorithm. The 2011 flood event in the Clarence Catchment, Australia was used for this study. The impact of assimilating flood extents was evaluated in terms of subsequent flood extent evolution, floodplain water depths, flow velocities and channel water levels (WLs). Water depth and flow velocity simulations improved by ∼60% over the open loop on an average and persisted for up to 7 days, following the sequential assimilation of two post‐peak flood extent observations. Flood extents and channel WLs also showed mean improvements of ∼10% and ∼80% in accuracy, respectively, indicating that the proposed MI likelihood function can improve flood extent assimilation.

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“…Jointly estimating highly uncertain model parameters alongside the state is an approach commonly found in hydrology (e.g. Vrugt et al, 2006;Gharamti et al, 2015;Abbaszadeh et al, 2018;Ziliani et al, 2019). Updating parameters often increase the complexity of the DA framework (nonlinearity often increases in state-parameters estimation problems) and the computational cost may become prohibitive, especially for spatially varying parameters.…”

Section: Overall Assessmentmentioning

confidence: 99%

Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

Gharamti

McCreight

Noh

et al. 2021

Preprint

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Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short time-scales combined with multiple sources of model error makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Hourly streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of one month. The data assimilation (DA) system developed in this paper explores two novel contributions: (1) Along-The-Stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40% more accurate (RMSE) streamflow estimates than regular, Euclidean distance-based localization. Assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along Pee Dee River in South Carolina and many other locations in the domain, where inflation is able to avoid filter divergence and thereby assimilate significantly more observations.

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Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

Cited by 36 publications

References 51 publications

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Evaluation and machine learning improvement of global hydrological model-based flood simulations

A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation

Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

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