Multi-scale analysis of bias correction of soil moisture

Su, Chun‐Hsu; Ryu, Dongryeol

doi:10.5194/hess-19-17-2015

Cited by 49 publications

(37 citation statements)

References 46 publications

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“…For example, the unrealistically large AMSR-E seasonal cycle at Little Washita caused the variability at SM long and SM short to be overly dampened by the bulk rescaling. This could perhaps be avoided by rescaling the observations separately at each timescale using the decomposed time series produced in this study, or using other methods that distinguish scaling characteristics at different timescales (e.g., Su and Ryu, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the timescale dependence of soil moisture errors may also be problematic for observation rescaling using bulk parameters, intended to correct systematic differences across all timescales. Even within relatively short timescales (up to about 1 month), Su and Ryu (2015) showed that the multiplicative (differences in standard deviation) and additive (differences in mean) components of the systematic differences between modeled and remotely sensed soil moisture differ across timescales. They highlight that this lack of stationarity cannot be adequately addressed by using bulk statistics to estimate observation rescaling parameters.…”

Section: Introductionmentioning

confidence: 99%

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The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Draper

Reichle

2015

Hydrol. Earth Syst. Sci.

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Abstract. A 9 year record of Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SM short ), mean seasonal (SM seas ), and inter-annual (SM long ) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10 −3 m 3 m −3 ) 2 , of which 19 % was in SM long , 26 % in SM seas , and 55 % in SM short . The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SM long , 24 % in SM seas , and 38 % in SM short . For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SM short , assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SM seas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Draper

Reichle

2015

Hydrol. Earth Syst. Sci.

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“…It is worth noting that other rescaling techniques could be used such as the CDF applied to the anomalies; however, the ones chosen for this work appear to be the most used in the literature. We have also to remark that we did not consider any effect of a bias seasonality as underlined by [58] and we applied the RTs to all data regardless of the season. The latter issue represents an additional complexity that may affect the results of the assimilation and will be the subject of future studies.…”

Section: Filtering and Rescaling Techniquesmentioning

confidence: 99%

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

et al. 2015

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Data assimilation (DA) of satellite soil moisture (SM) observations represents a great opportunity for improving the ability of rainfall-runoff models in predicting river discharges. Many studies have been carried out so far demonstrating the possibility to reduce model prediction uncertainty by incorporating satellite SM observations. However, large discrepancies can be perceived between these studies with the result that successful DA is not only related to the quality of the satellite observations but can be significantly controlled by many methodological and morphoclimatic factors. In this article, through an experimental study carried out on the Tiber River basin in Central Italy, we explore how the catchment area, soil type, climatology, rescaling technique, observation and model error selection may affect the results of the assimilation and can be the causes of the apparent discrepancies obtained in the literature. The results show that: (i) DA of SM generally improves discharge predictions (with a mean efficiency of about 30%); (ii) unlike catchment area, the soil type and the catchment specific characteristics might have a remarkable influence on the results; (iii) simple rescaling techniques may perform equally well to more complex ones; (iv) an accurate quantification of the model error is paramount for a correct choice of the observation error and, (v) SM temporal variability has a stronger influence than the season itself. On this basis, we advise that DA of SM may be not a simple task and one should carefully test the optimality of the assimilation experiment prior to drawing any general conclusions. OPEN ACCESSRemote Sens. 2015, 7 11404

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“…Besides linearity, the TC estimation of the error variance is based on additional assumptions, in particular that the random errors are uncorrelated with each other and with the true soil moisture. Several studies have suggested extensions to TC that can account for complex temporal changes at various time scales (e.g., Loew and Schlenz 2011;Zwieback et al 2013;Su and Ryu 2015). Furthermore, the characteristics of the soil moisture products, and hence the relationships between the products, should not change over time, for example, seasonally.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions

Zwieback

Gruber

et al. 2016

Journal of Hydrometeorology

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The error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods-based on error-invariables regression and probabilistic factor analysis-extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products.

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Multi-scale analysis of bias correction of soil moisture

Cited by 49 publications

References 46 publications

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions

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