Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme

Yuan Li, Dongryeol Ryu, Andrew W. Western, Q. J. Wang

Research output: Contribution to journalArticleResearchpeer-review

25 Citations (Scopus)


Real-time discharge observations can be assimilated into flood models to improve forecast accuracy; however, the presence of time lags in the routing process and a lack of methods to quantitatively represent different sources of uncertainties challenge the implementation of data assimilation techniques
for operational flood forecasting. To address these issues, an integrated error parameter estimation and lagaware data assimilation (IEELA) scheme was recently developed for a lumped model. The scheme combines an ensemble-based maximum a posteriori (MAP) error estimation approach with a lag-aware ensemble Kalman smoother (EnKS). In this study, the IEELA scheme is extended to a semi-distributed model to provide for more general application in flood forecasting by including spatial and temporal correlations in model uncertainties
between subcatchments. The result reveals that using a semidistributed model leads to more accurate forecasts than a lumped model in an open-loop scenario. The IEELA scheme improves the forecast accuracy significantly in both lumped and semidistributed models, and the superiority of the semidistributed model remains in the data assimilation scenario. However, the improvements resulting from IEELA are confined to the outlet of the catchment where the discharge observations are assimilated. Forecasts at ‘‘ungauged’’ internal locations are not improved, and in some instances, even become less accurate.
Original languageEnglish
Pages (from-to)3238 - 3258
Number of pages21
JournalWater Resources Research
Issue number5
Publication statusPublished - 8 May 2015


  • Ensemble Kalman smoother
  • Flood forecasting
  • Maximum a posteriori estimation
  • Semidistributed model
  • Temporally and spatially correlated error

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