How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation?

Valdés-Abellán, Javier; Pachepsky, Yakov; Martínez, Gonzalo; Pla, Concepción

doi:10.2136/vzj2018.07.0142

Cited by 7 publications

(5 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different DA intervals have also been applied into other geophysical applications. Valdes‐Abellan et al (2019) studied the impact of assimilation frequency of water content measurements for obtaining soil hydraulic parameters. Using an EnKF with a state augmentation approach to update both model states and parameters, results showed that high frequency update does not provide better soil hydraulic parameters than low frequency update.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances

Lei

Whitaker

et al. 2020

J Adv Model Earth Syst

View full text Add to dashboard Cite

The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land applications. The observing network has been significantly developed, and thus, observations with highly dense temporal resolutions have become available. To better extract information from dense temporal observations, one straightforward strategy is to increase the assimilation frequency. However, more frequent assimilation may exacerbate the model imbalance and result in degraded forecasts. To combat the imbalance caused by ensemble-based data assimilation due to sampling error and covariance localization, three-and four-dimensional incremental analysis update (IAU) were proposed, which gradually introduce the analysis increments into model rather than intermittently updating the state. The trade-off between the assimilation frequency and imbalance is systematically explored here by using an idealized two-layer model and the NOAA GFS. Results from the idealized two-layer model show that increasing assimilation frequency can reduce errors for state variables that are not sensitive to imbalances. For state variable that carries the signal of the external gravity mode and is sensitive to imbalances, increasing assimilation frequency without (with) IAU reduces (increases) errors. Without IAU, more frequent updates result in smaller increments and less insertion noise, while the initialization of IAU cannot effectively mitigate the imbalances with increased assimilation frequency. Results with a low-resolution version of the NOAA GFS demonstrate that increasing assimilation frequency from 6 to 2 h improves the errors and biases of forecasts verified with conventional and radiance observations, although gravity wave noise in the forecast is increased. Plain Language Summary Data assimilation combines observations with prior information like model forecasts to produce the best estimate of the state for a dynamical system. For global prediction, a data assimilation interval of 6-h has typically been used; but for high-resolution regional models with dense spatial and temporal resolution observations such as radar and satellite data, shorter data assimilation intervals are often used. Given the significantly developed observing networks, observations with highly dense temporal resolutions and inhomogeneous spatial distributions have become available. Increasing the assimilation frequency may better extract information from dense temporal observations, but does not give the model enough time to adjust the updated state variables with potentially insertion shocks. This study systematically investigates the trade-off between the data assimilation frequency and imbalance. Results reveal that increasing assimilation frequency can reduce errors for state variables, because more frequent updates can better fit the model to the observation and result in smaller increments/less imbalances to the model. When the incremental analysis update (IAU) is applied, increasing assimilation frequency reduces errors for state variables that are not sensiti...

show abstract

Section: Introductionmentioning

confidence: 99%

Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances

Lei

Whitaker

et al. 2020

J Adv Model Earth Syst

View full text Add to dashboard Cite

show abstract

“…For example, the frequency of EnKF-based water content DA is investigated for soil hydraulic models. 18 The results show that DA with high update frequencies does not provide better results than those obtained using low frequencies. An EnKF-based DA procedure for water quality forecasting is developed by Kim et al 19 The authors suggested that the time interval (they called it window size ) should be chosen carefully: if the window size is too small, the procedure works largely as a filter rather than a smoother, which reduces performance; if the window size is too large, some of the observations being assimilated may be too old and/or redundant to be informative.…”

Section: Introductionmentioning

confidence: 91%

Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions

Huang

Xie

Cho³

et al. 2022

SIMULATION

View full text Add to dashboard Cite

Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.

show abstract

“…At each of the six locations, the soil moisture is taken at three depths, z 1 = 0.33 m, z 2 = 0.75 m, and z 3 = 0.95 m, below the surface, summing up to a total of 12 measurement locations and 6 validation locations. Even though hourly observations are available, the updates are performed only daily to increase the computational efficiency and because it has been shown that higher assimilation frequencies have a negative impact on the soil moisture estimates (Valdes-Abellan et al, 2019). The soil moisture values from the reference runs are perturbed with random white noise with a standard deviation of ε = 0.01 to account for the measurement error.…”

Section: Enkfmentioning

confidence: 99%

Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model

Brandhorst¹,

Neuweiler²

2023

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Variably saturated subsurface flow models require knowledge of the soil hydraulic parameters. However, the determination of these parameters in heterogeneous soils is not easily feasible and subject to large uncertainties. As the modeled soil moisture is very sensitive to these parameters, especially the saturated hydraulic conductivity, porosity, and the parameters describing the retention and relative permeability functions, it is likewise highly uncertain. Data assimilation can be used to handle and reduce both the state and parameter uncertainty. In this work, we apply the ensemble Kalman filter (EnKF) to a three-dimensional heterogeneous hillslope model and investigate the influence of updating the different soil hydraulic parameters on the accuracy of the estimated soil moisture. We further examine the usage of a simplified layered soil structure instead of the fully resolved heterogeneous soil structure in the ensemble. It is shown that the best estimates are obtained when performing a joint update of porosity and the van Genuchten parameters and (optionally) the saturated hydraulic conductivity. The usage of a simplified soil structure gave decent estimates of spatially averaged soil moisture in combination with parameter updates but led to a failure of the EnKF and very poor soil moisture estimates at non-observed locations.

show abstract

How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation?

Cited by 7 publications

References 70 publications

Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances

Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances

Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions

Impact of parameter updates on soil moisture assimilation in a 3D heterogeneous hillslope model

Contact Info

Product

Resources

About