Estimating the soil moisture profile by assimilating near-surface observations with the ensemble Kaiman filter (EnKF)

Zhang, Shuwen; Li, Haorui; Zhang, Guoqing; Qiu, Chongjian; Li, Xin

doi:10.1007/bf02918692

Cited by 31 publications

(5 citation statements)

References 24 publications

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“…The statistical analysis consisted of the Statistica 8.0 software packages and Microsoft Excel. The literature contains a series of numerical approaches that describe the soil's humidity values by using Penman models (Shang et al, 2007), different statistical analyses (Reichle et al 2002, Zhang et al 2004, 2006 or by using hydrodynamic equilibrium equations (Shuwen et al 2005). All these approaches use as a main entrance factors the level of precipitations and temperatures from the soil surface and the soil's humidity values recorded at a certain reference depth.…”

Section: Discussionmentioning

confidence: 99%

Monitoring of soil moisture in Long-Term Ecological Research (LTER) sites of Romanian Carpathians

et al. 2018

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Understanding soil moisture and its relationship with different climatic and soil characteristics is essential for better analysing the interactions between forest and soil water dynamics, allowing us to more precisely predict climatic changes. The present paper investigates the temporal variability of soil moisture in three different forest ecosystems (LTER -long term ecological research site) with the same soil type (Eutric Cambisol). Soil moisture was measured daily from 2011 to 2016 by using three sensors at three different depths (20, 40, 70 cm). We identified the interactions between soil properties, vegetation type, local climatic conditions and soil moisture. In order to establish the temporal variability of the soil moisture content, we have applied two procedures, namely the Fourier series and the neural network fitting. A high variability in time and depth for soil volumetric water content was identified. The highest soil moisture levels were recorded at higher depths (70 cm) for almost all surfaces, with the exception of the Fundata surface because of the occurrence of limestone. In the mountainous areas, with higher precipitation (Fundata and Predeal sites), volumetric soil water content was mainly influenced by soil physical characteristics. Soil moisture levels below the drought level were only recorded for the Stalpeni site from September to October 2012. There was a delay between the precipitation event and soil humidification of 0.4-0.8 time units (days). We also found a significant correlation between soil moisture and soil texture and a weak correlation with vegetation type. Temperature influenced soil moisture levels at almost all depths, while precipitation only had an impact when there was a delay of 1 or 2 days. Our results can serve as a scientific base in the monitoring and analysing of soil moisture against the background of a changing climate.

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Section: Discussionmentioning

confidence: 99%

Monitoring of soil moisture in Long-Term Ecological Research (LTER) sites of Romanian Carpathians

et al. 2018

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“…The core of these traditional parametric filters is their reliance on repeated forward integrations of an explicitly known physical model of unsaturated flow, such as the HYDRUS (Šimůnek et al, 2006), Soil and Water Assessment Tool (SWAT) (Van Dam and Feddes, 2000), and Ross models (Ross, 2003;Zha et al, 2013). 50 Currently, the ever-increasing availability of multi-source data from remote sensing (Montzka et al, 2011;Shi et al, 2011), ground-based measurements (Li et al, 2018;Shuwen et al, 2005;Yang et al, 2000), and numerical modeling has paved the way for the development of fully data-driven techniques within the DA framework. In particular, recent advances in machine learning-based DA schemes (Brajard et al, 2020;Brajard et al, 2021;Yamanaka et al, 2019) offer exciting new opportunities for 55 extracting patterns and insights of soil moisture dynamics from data (Ju et al, 2018;Li et al, 2020;Liu et al, 2020;Wang et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Wang¹,

Wang

et al. 2023

Preprint

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Abstract. Conventional data-worth (DW) analysis for soil water problems depends on physical dynamic models. The widespread occurrence of model structural errors and the strong nonlinearity of soil water flow may lead to biased or wrong worth assessment. By introducing the nonparametric data-worth analysis (NP-DWA) framework coupled with ensemble Kalman filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations regarding the reconstruction of fully data-driven soil water flow models prior to data gathering. The DW of real-time soil moisture observations after Gaussian process training and Kalman update was quantified with three representative information metrics, including the trace, Shannon entropy difference, and relative entropy. The sequential NP-DWA framework was examined by a number of cases in terms of the variable of interest, spatial location, observation error, and prior data content. Our results indicated that the overall increasing trend of the DW from the sequential augmentation of additional observations was susceptible to interruptions by localized surges due to never-experienced atmospheric conditions (i.e., rainfall events) within the NP-DWA framework. Fortunately, this performance degradation can be effectively alleviated by enriching training scenarios or the appropriate amplification of observational noise under extreme meteorological conditions. Nevertheless, a substantial expansion of the prior data content may cause an unexpected increase in DW of future potential observations due to the possible introduction of ensuing observation noises. Hence, high-quality and representative “small” data may be a better choice than unfiltered “big” data. Compared with the observations in the surface layer with the strongest time-variability, the soil water content in the middle layer robustly exhibited remarkable superiority in the construction of model-free soil moisture models. An alternative monitoring strategy with a larger data-worth was prone to a higher DW assessment accuracy within the proposed NP-DWA framework. We also demonstrated that the DW assessment performance was jointly determined by ‘3C’, i.e., capacity of potential observation realizations to “capture” actual observations, correlation of potential observations with the variables of interest, and choice of DW indicators. Direct mapping from regular meteorological data to soil water content within the NP-DWA mitigated the adverse effects of nonlinearity-related interference, which thus facilitated the identification of the soil moisture covariance matrix, especially the cross-covariance.

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“…1 DA in its various forms has typically been applied to continuous systems with numerical models. [4][5][6][7] The application of DA to discrete event systems (DESs) and discrete time systems (DTSs) including agent-based models (ABMs) is a recent development.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Xie

Cho³

et al. 2022

SIMULATION

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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.

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Estimating the soil moisture profile by assimilating near-surface observations with the ensemble Kaiman filter (EnKF)

Cited by 31 publications

References 24 publications

Monitoring of soil moisture in Long-Term Ecological Research (LTER) sites of Romanian Carpathians

Monitoring of soil moisture in Long-Term Ecological Research (LTER) sites of Romanian Carpathians

Data worth analysis within a model-free data assimilation framework for soil moisture flow

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

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