The importance of parameter resampling for soil moisture data  assimilation into hydrologic models using the particle filter

Plaza, Douglas; Keyser, Robin De; Lannoy, Gabriëlle J. M. De; Giustarini, Laura; Matgen, Patrick; Pauwels, Valentijn R. N.

doi:10.5194/hess-16-375-2012

Cited by 73 publications

(60 citation statements)

References 39 publications

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“…Another flexible (but potentially more computationally expensive) approach to solving the above filtering problem includes the sequential Monte Carlo (SMC) methods such as particle filtering (PF) (e.g., Arulampalam et al, 2002;Moradkhani et al, 2005a;Weerts and El Serafy, 2006;Noh et al, 2011;Plaza et al, 2012). Similar to the EnKF, particle filtering evolves a sample of the state space forward using the SMC method to approximate the predictive distribution.…”

Section: State Updatingmentioning

confidence: 99%

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

389

296

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Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters.The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, asPublished by Copernicus Publications on behalf of the European Geosciences Union. Y. Liu et al.: Advancing data assimilation in operational hydrologic forecastingwell as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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Section: State Updatingmentioning

confidence: 99%

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

389

296

View full text Add to dashboard Cite

show abstract

“…Yet, this approach requires definition of an importance density for the parameters to avoid parameter impoverishment after several successive assimilation steps. This has been demonstrated numerically by Plaza et al (2012) using a series of data assimilation experiments. In principle, we could corrupt the posterior parameter distribution using the ensemble inflation method of Whitaker and Hamill (2012) detailed in Eq.…”

Section: Residual Resampling Particle Filter (Rrpf) and Parameter Estmentioning

confidence: 90%

“…This approach was used by Qin et al (2009) to avoid degeneracy of the parameter values. Instead, we use the approach described by Plaza et al (2012) and perturb the parameter values of the resampled particles using draws from a zero-mean d-variate Gaussian distribution with diagonal covariance matrix. This d × d matrix has zero entries everywhere (uncorrelated dimensions) except on the main diagonal which stores values of s 2 Var α 1:N 0,j , where s is a scaling factor, Var α 1:N 0,j signifies the prior variance of the j th parameter (at t = 0), and j = {1, .…”

Section: Residual Resampling Particle Filter (Rrpf) and Parameter Estmentioning

confidence: 99%

“…In the absence of theoretical convergence proofs and clear guidelines on the selection of s, the RRPF cannot estimate exactly the posterior state and parameter PDF (Vrugt et al, 2013;Yan et al, 2015). Previous applications of RRPF have suggested a value of s = 0.01 Plaza et al, 2012), but thus far we have used s = 0.1 to avoid sample impoverishment. Table 7 lists RMSE values of VIC-3L for the 5, 20, and 50 cm measurement depths for the calibra- Table 6.…”

Section: Vic-3lmentioning

confidence: 99%

“…Most of these publications use synthetic (or twin) experiments with assimilation of artificially generated data. Examples include studies with simulated measurements of the groundwater table depth or hydraulic head (Franssen and Kinzelbach, 2008;Bailey and Baù, 2012;Kurtz et al, 2014;Shi et al, 2014;Song et al, 2014;Tang et al, 2015), discharge/streamflow (Bailey and Baù, 2012;Moradkhani et al, 2012;Vrugt et al, 2013;Rasmussen et al, 2015), groundwater temperature (Kurtz et al, 2014), soil moisture (Wu and Margulis, 2011;Plaza et al, 2012;Erdal et al, 2014;Shi et al, 2014;Song et al, 2014;Pasetto et al, 2015), brightness temperature from passive remote sensing (Montzka et al, 2013;Han et al, 2014), and contaminant concentration (Gharamti et al, 2013). These studies use a variety of different methods for joint parameter and state estimation, among which the EnKF (Franssen and Kinzelbach, 2008;Wu et al, 2011;Gharamti et al, 2013;Erdal et al, 2014;Kurtz et al, 2014;Shi et al, 2014;Pasetto et al, 2015), the iterative EnKF (Song et al, 2014), the extended KF (Pauwels et al, 2009), the local ensemble transform KF (Han et al, 2014), the ensemble transform KF (Rasmussen et al, 2015), and the normal score EnKF (Tang et al, 2015).…”

Section: Introduction and Scopementioning

confidence: 99%

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State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

Zhang¹,

Franssen²,

Han³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil-atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March-July 2012) of arealaveraged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the "calibrated" LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August-December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for the Rollesbroich site. The large systematic underestimation of water storage at 50 cm depth by VIC-3L during the first few months of the evaluation period questions, in part, the validity of its fixed water table depth at the bottom of the modeled soil domain.

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Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

Fan

Huang

Baetz

et al. 2017

Water Resources Research

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In this study, a copula-based particle filter (CopPF) approach was developed for sequential hydrological data assimilation by considering parameter correlation structures. In CopPF, multivariate copulas are proposed to reflect parameter interdependence before the resampling procedure with new particles then being sampled from the obtained copulas. Such a process can overcome both particle degeneration and sample impoverishment. The applicability of CopPF is illustrated with three case studies using a twoparameter simplified model and two conceptual hydrologic models. The results for the simplified model indicate that model parameters are highly correlated in the data assimilation process, suggesting a demand for full description of their dependence structure. Synthetic experiments on hydrologic data assimilation indicate that CopPF can rejuvenate particle evolution in large spaces and thus achieve good performances with low sample size scenarios. The applicability of CopPF is further illustrated through two real-case studies. It is shown that, compared with traditional particle filter (PF) and particle Markov chain Monte Carlo (PMCMC) approaches, the proposed method can provide more accurate results for both deterministic and probabilistic prediction with a sample size of 100. Furthermore, the sample size would not significantly influence the performance of CopPF. Also, the copula resampling approach dominates parameter evolution in CopPF, with more than 50% of particles sampled by copulas in most sample size scenarios. However, the EnKF can achieve good performances with small ensembles; but related studies have claimed Key Points: A copula-based particle filter approach is developed for hydrological data assimilation Synthetic experiments and real-case studies demonstrate performance of the CopPF method Results suggest that CopPF provides greater opportunities to access new samples which in turn lead to more accurate predictions

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The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter

Cited by 73 publications

References 39 publications

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

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