Assessing model state and forecasts variation in hydrologic data assimilation

Samuel, Jos; Coulibaly, Paulin; Dumedah, Gift; Moradkhani, Hamid

doi:10.1016/j.jhydrol.2014.03.048

Cited by 71 publications

(52 citation statements)

References 53 publications

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“…It is applicable to a variety of nonlinear problems (Evensen, 2003;Weerts and El Serafy, 2006) and has been widely applied to hydrological models (Abaza et al, 2014;DeChant and Moradkhani, 2014;Delijani et al, 2014;Samuel et al, 2014;Tamura et al, 2014;Xue and Zhang, Deng et al, 2015). Furthermore, the EnKF has been successfully used in time-invariant parameter estimations for hydrological models (Moradkhani et al, 2005;Wang et al, 2009;Zhang, 2010, 2013).…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

“…The ensemble size, uncertainties in input and output have significant impacts on the assimilation performance of the EnKF, and they are specified following the previous studies (Moradkhani et al, 2005;Wang et al, 2009;Xie and Zhang, 2010;Nie et al, 2011;Lü et al, 2013;Samuel et al, 2014). The ensemble size is set to 1000 for the synthetic experiment and the two case studies.…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

“…The synthetic C series were assumed to be periodic when many peak values exist, whereas the variation of SC series is less. The time lag be- (Clark et al, 2008;Samuel et al, 2014). The results for the scenario of constant parameters are shown in Fig.…”

Section: Datamentioning

confidence: 99%

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Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

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Section: Ensemble Kalman Filtermentioning

confidence: 99%

Section: Ensemble Kalman Filtermentioning

confidence: 99%

See 1 more Smart Citation

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…For example, the median correlation coefficient of a simple linear regression between each reservoir for each whole square and the streamflow at the outlet is 0.12, 0.49 and 0.15 for SML, GWL and LWL respectively. Samuel et al (2014) and Trudel et al (2014) found that updating soil moisture with streamflow observations actually deteriorated soil moisture simulation compared with real soil moisture observations. However, there are notable differences between these studies and the present one.…”

Section: Streamflow Data Assimilationmentioning

confidence: 99%

“…Multivariate DA applications in hydrology are becoming more frequent, but generally focus on streamflow and soil moisture (Samuel et al, 2014;Trudel et al, 2014;Lee et al, 2011), omitting the snow water equivalent. In snowdominated watersheds, the key initial states include not only information about the hydric state, such as soil moisture and streamflow, but also the snow cover state, such as SWE and snow cover area (SCA).…”

Section: Introductionmentioning

confidence: 99%

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Bergeron

Trudel

Leconte

2016

Hydrol. Earth Syst. Sci.

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Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the ensemble Kalman filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British Columbia, Canada. Synthetic data include daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector due to the limitations imposed by the EnKF. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than their individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

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Hydrologic modeling in dynamic catchments: A data assimilation approach

Pathiraja

Marshall

Sharma

et al. 2016

Water Resources Research

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The transferability of conceptual hydrologic models in time is often limited by both their structural deficiencies and adopted parameterizations. Adopting a stationary set of model parameters ignores biases introduced by the data used to derive them, as well as any future changes to catchment conditions. Although time invariance of model parameters is one of the hallmarks of a high quality hydrologic model, very few (if any) models can achieve this due to their inherent limitations. It is therefore proposed to consider parameters as potentially time varying quantities, which can evolve according to signals in hydrologic observations. In this paper, we investigate the potential for Data Assimilation (DA) to detect known temporal patterns in model parameters from streamflow observations. It is shown that the success of the DA algorithm is strongly dependent on the method used to generate background (or prior) parameter ensembles (also referred to as the parameter evolution model). A range of traditional parameter evolution techniques are considered and found to be problematic when multiple parameters with complex time variations are estimated simultaneously. Two alternative methods are proposed, the first is a Multilayer approach that uses the EnKF to estimate hyperparameters of the temporal structure, based on apriori knowledge of the form of nonstationarity. The second is a Locally Linear approach that uses local linear estimation and requires no assumptions of the form of parameter nonstationarity. Both are shown to provide superior results in a range of synthetic case studies, when compared to traditional parameter evolution techniques.PUBLICATIONS characteristics of the time series of system states (such as peak flow, base flows, volume of runoff, soil moisture storage fluctuations etc.) [Moussa and Chahinian, 2009;Efstratiadis and Koutsoyiannis, 2010;Westerberg et al., 2011]. Multiobjective criterion type approaches have shown promise in defining parameter spaces which provide improved simulations over a range of characteristics, but trade-offs are almost always required [Madsen, 2003;Efstratiadis and Koutsoyiannis, 2010].One possible approach to improve the applicability of hydrologic models over long time scales is to allow the model parameters to evolve with time. Time varying parameter systems have been investigated in fundamental systems theory over the last few decades [e.g., Richards, 1983;Schwartz and Ozbay, 1990;Mohammadpour and Scherer, 2012], the most common being Linear Time Variant systems where the state space transition matrix is time varying [e.g., Khalil, 1996]. A well-known example of a time varying parameter system is the variation in aerodynamic coefficients during take-off, cruising and landing for high speed aircraft [e.g., Tomas-Rodriguez and Banks, 2010]. In hydrologic applications, model parameterizations would adjust to varying climatic regimes and/or changes in catchment conditions based on signals in the input and observed data under such a framework. It can be argued that time var...

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Assessing model state and forecasts variation in hydrologic data assimilation

Cited by 71 publications

References 53 publications

Identification of hydrological model parameter variation using ensemble Kalman filter

Identification of hydrological model parameter variation using ensemble Kalman filter

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

Hydrologic modeling in dynamic catchments: A data assimilation approach

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