Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods

Fan, Yurui; Huang, Guohe; Baetz, Brian W.; Li, Y. P.; Huang, Kai; Chen, X.; Gao, Mingyu

doi:10.1016/j.jhydrol.2017.05.010

Cited by 32 publications

(27 citation statements)

References 59 publications

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“…These more complex methods can also account for potential errors in the observations used to update the model and to spatially correlate errors to improve model performance at sites without observations (Reichle & Koster, 2003). DA has a rich history of applications in hydrologic (e.g., Clark et al, 2008;Fan et al, 2017;Moradkhani et al, 2012;Rasmussen et al, 2016;Reichle et al, 2002) and atmospheric models (e.g., Auligné et al, 2007;Harris & Kelly, 2001). Many of the more advanced DA approaches are based on derivatives of the Kalman filter (Kalman, 1960), which assumes that model responses are relatively linear.…”

Section: Introductionmentioning

confidence: 99%

The Bias‐Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics‐Based, Multilayer Snow Models

Winstral¹,

Magnusson

Schirmer³

et al. 2019

Water Resources Research

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The reliance on distributed energy‐ and mass‐balance snow models as runoff forecasting tools has been increasing. Compared to traditional, conceptual forecasting approaches, these physics‐based tools are robust to conditions that deviate from historic norms and offer improved performance in potentially dangerous rain‐on‐snow events. The physics‐based simulations, however, depend on a large suite of accurate forcing data. Current numerical weather prediction products are capable of supplying the full range of required data, but systematic biases are often present. Data assimilation presents a means of compensating for such errors as well as potential snow model errors, yet currently available data assimilation techniques have limited usefulness in these snow models. This study introduced an alternative technique that similarly uses observations to update and improve simulations. As such, it is the first method to incorporate point snow observations in a fully distributed, physics‐based, multilayer snow model while conserving mass and maintaining physically consistent layer states that are in accord with observations. At the core of this technique is an ensemble of predetermined perturbations to the model forcings termed the bias‐detecting ensemble. Ensemble members were evaluated using observed snow depths to ascertain potential biases at nearly 300 sites across Switzerland. The bias assessments were distributed to 38 independent sites and incorporated into the model. Tests were conducted over three winter seasons using two numerical weather prediction‐based products with varying quality. Averaged across the 38 sites and 3 seasons, the Nash‐Sutcliffe efficiency score for bias‐detecting ensemble‐corrected snow depth was 0.98 compared to 0.81 without the bias‐detecting ensemble method.

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Section: Introductionmentioning

confidence: 99%

The Bias‐Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics‐Based, Multilayer Snow Models

Winstral¹,

Magnusson

Schirmer³

et al. 2019

Water Resources Research

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“…The main data assimilation techniques used to combine remotely sensed data with hydrological models include the Kalman filter and its variants, particle filters (PF), and variational methods [15,119]. Each technique has its own advantages and weaknesses [190,191]. Among them, the ensemble Kalman filter (EnKF) is the most widely used technique in hydrology [21,48,108,119,121,142,179,185,192], because it can not only account for nonlinearities (and partially nonGaussianity) with few restrictive assumptions [119,188], but can also continuously update hydrological state variables and parameters when new measurements are available with simple implementation [108,119].…”

Section: The Application Of Data Assimilation For Merging Satellite-bmentioning

confidence: 99%

The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Jiang

Wang

2019

Water

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A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing may provide a valuable contribution for hydrological predictions due to its wide coverage and increasing tempo-spatial resolutions. In this review, we provide an overview of the role of satellite-based remote sensing in streamflow simulation. First, difficulties in hydrological modeling over highly regulated basins are further discussed. Next, the performance of satellite-based remote sensing (e.g., remotely sensed data for precipitation, evapotranspiration, soil moisture, snow properties, terrestrial water storage change, land surface temperature, river width, etc.) in improving simulated streamflow is summarized. Then, the application of data assimilation for merging satellite-based remote sensing with a hydrological model is explored. Finally, a framework, using remotely sensed observations to improve streamflow predictions in highly regulated basins, is proposed for future studies. This review can be helpful to understand the effect of applying satellite-based remote sensing on hydrological modeling.

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“…Shi et al (2014) presented multiple parameter estimation using multivariate observations via the ensemble Kalman filter (EnKF) for a physically based land surface hydrologic model. Fan et al (2017a) developed two integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods. However, due to the local complex characteristics of the watershed, some parameters in the hydrologic model may be not quite identifiable and showed slow convergence (Moradkhani et al, 2005b.…”

Section: Uncertainty Quantification Of Hydrologic Modelsmentioning

confidence: 99%

Uncertainty Quantification in Hydrologic Predictions: A Brief Review

Fan¹

2019

J ENVIRON INFORM LET

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This study provides a brief review for uncertainty quantification in hydrological predictions. The major approaches for hydrologic predictions are firstly introduced, including the widely used data-driven and process-based modelling approaches. The major uncertainties resulting from inputs, model structures, parameters and outputs are then briefly illustrated. The major review is then conducted for various uncertainty quantification approaches. In detail, the approaches for quantifying uncertainties in model parameters, structures and states are mainly reviewed, such as the Markov chain Monte Carlo, sequential data assimilation and model average approaches. Potential issues to be addressed in future are then concluded, summarizing some unclear issues which may be further investigated in further studies.

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Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods

Cited by 32 publications

References 59 publications

The Bias‐Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics‐Based, Multilayer Snow Models

The Bias‐Detecting Ensemble: A New and Efficient Technique for Dynamically Incorporating Observations Into Physics‐Based, Multilayer Snow Models

The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Uncertainty Quantification in Hydrologic Predictions: A Brief Review

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