Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model

Clark, Martyn P.; Rupp, David E.; Woods, Ross; Zheng, Xunhua; Ibbitt, Richard P.; Slater, A. G.; Schmidt, Jochen; Uddstrom, Michael

doi:10.1016/j.advwatres.2008.06.005

Cited by 424 publications

(449 citation statements)

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“…Changes in water velocity as a result of the growth of in-stream vegetation or changes in cross-sectional area as a result of scouring or deposition in the streambed can cause the rating curve to shift (Bedjaoui et al, 2008). Extrapolation of the rating curve to flows higher than those used in its derivation can also lead to a high degree of uncertainty in streamflow measurement (Clark et al, 2008). The estimation of streamflow data during periods when observation is not possible because of ice cover or instrument failure is another source of error (Stahl et al, 2008).…”

Section: Uncertainty In Streamflow Model Outputmentioning

confidence: 99%

“…Unfortunately, 4-D VAR seeks only to minimize uncertainty in state estimations; it does not provide any measure of predictive uncertainty. This method also assumes that model errors have a Gaussian distribution, which is rarely true in hydrological modelling (Clark et al, 2008).…”

Section: Framework For the Anticipation Of Forecast Uncertaintymentioning

confidence: 99%

“…Variations on the original KF algorithm have made it suitable for use with non-linear problems such as NWP and hydrological modelling. A commonly used variant is the Ensemble Kalman Filter (EnKF; Evensen, 1994) in which an ensemble of initial conditions is used to run the (non-linear) hydrological model until an observation becomes available (e.g., Andreadis and Lettenmaier, 2006;Clark et al, 2008). At this time, the model states are updated based on relative errors in the model and observations.…”

Section: Framework For the Anticipation Of Forecast Uncertaintymentioning

confidence: 99%

See 2 more Smart Citations

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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Section: Uncertainty In Streamflow Model Outputmentioning

confidence: 99%

Section: Framework For the Anticipation Of Forecast Uncertaintymentioning

confidence: 99%

Section: Framework For the Anticipation Of Forecast Uncertaintymentioning

confidence: 99%

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Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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“…Popular data assimilation methods include ensemble Kalman Filter (EnKF) method, the 3-dimensional and 4-dimensional variational methods (3dVAR and 4dVAR) (Evensen, 1997;Wang et al, 2008;Huang et al, 2009). The effects of using data assimilation methods to merge observational data and model state variables have shown to be significant in improving hydrological forecasting (Clark et al, 2008;.…”

Section: Uncertainties In Hydrological Forecastingmentioning

confidence: 99%

Hydrological monitoring and seasonal forecasting: Progress and perspectives

et al. 2016

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Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment, preparedness and mitigation. In recent decades, developments in ground and satellite measurements have made the hydrometeorological information readily available, and advances in information technology have facilitated the data analysis in a real-time manner. New progress in climate research and modeling has enabled the prediction of seasonal climate with reasonable accuracy and increased resolution. These emerging techniques and advances have enabled more timely acquisition of accurate hydrological fluxes and status, and earlier warning of extreme hydrological events such as droughts and floods. This paper gives current state-of-the-art understanding of the uncertainties in hydrological monitoring and forecasting, reviews the efforts and progress in operational hydrological monitoring system assisted by observations from various sources and experimental seasonal hydrological forecasting, and briefly introduces the current monitoring and forecasting practices in China. The grand challenges and perspectives for the near future are also discussed, including acquiring and extracting reliable information for monitoring and forecasting, predicting realistic hydrological fluxes and states in the river basin being significantly altered by human activity, and filling the gap between numerical models and the end user. We highlight the importance of understanding the needs of the operational water management and the priority to transfer research knowledge to decision-makers.

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“…A multiplier of 0.1 was chosen based on estimates adopted for similar gauges in hydrologic data assimilation (DA) studies (e.g., Clark et al, 2008;Weerts and El Serafy, 2006;Xie et al, 2014). Several studies have noted that a major source of rainfall uncertainty arises from scaling point rainfall to the catchment scale (Villarini and Krajewski, 2008;McMillan et al, 2011) and that multiplicative error models are suited to describing such errors (e.g., Kavetski et al, 2006).…”

Section: Application To the Nam Muc Catchmentmentioning

confidence: 99%

Time-varying parameter models for catchments with land use change: the importance of model structure

Pathiraja

Anghileri

Burlando

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a mediumsized catchment (2880 km 2 ) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a timevarying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.

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Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model

Cited by 424 publications

References 42 publications

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Hydrological monitoring and seasonal forecasting: Progress and perspectives

Time-varying parameter models for catchments with land use change: the importance of model structure

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