Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization

Khaki, Mehdi; Schumacher, Maike; Forootan, Ehsan; Kühn, Michael; Awange, Joseph; Dijk, Albert I. J. M. van

doi:10.1016/j.advwatres.2017.07.024

Cited by 40 publications

(38 citation statements)

References 95 publications

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“…The ensemble Kalman filter (EnKF) is a very useful data assimilation (DA) tool to reduce the model errors at each time step by updating the model state variables (Evensen, 2003). DA methods have been successfully applied in hydrologic modeling to assimilate observations from various sources into the model, including soil moisture (e.g., Aubert et al, 2003;Crow & Ryu, 2009;Houser et al, 1998;Pauwels et al, 2001;Reichle et al, 2008Reichle et al, , 2004, snow water equivalent (e.g., Barrett, 2003;Slater & Clark, 2006;Sun et al, 2004), streamflow (e.g., Clark et al, 2008;Lee et al, 2011), groundwater levels (e.g., Hendricks Franssen et al, 2017, microwave radiances (e.g., Dechant & Moradkhani, 2011), and TWS (e.g., van Dijk et al, 2014;Ellett et al, 2006;Forman & Reichle, 2013;Forman et al, 2012;Girotto et al, 2016Girotto et al, , 2017Houborg et al, 2012;Khaki, Ait-El-Fquih, et al, 2017;Khaki, Schumacher, et al, 2017;Kumar et al, 2016;Li & Rodell, 2015;Li et al, 2012;Smith, 2013;Tian et al, 2017;Zaitchik et al, 2008). Therefore, assimilating the GRACE TWS retrievals into a hydrological model yields more reliable water storage estimates in which the drawbacks of both approaches are mitigated.…”

Section: Introductionmentioning

confidence: 99%

Performance of Different Ensemble Kalman Filter Structures to Assimilate GRACE Terrestrial Water Storage Estimates Into a High‐Resolution Hydrological Model: A Synthetic Study

Shokri

Walker

Dijk

et al. 2018

Water Resources Research

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Among all remote sensing missions, the Gravity Recovery and Climate Experiment (GRACE) was unique as it measured the change in total water content across all terrestrial water storages (TWS) including subsurface, deep soil moisture, and groundwater. However, its coarse resolution is a major challenge for practical applications. Ensemble Kalman filters (EnKFs) are useful tools to combine observations with models to reduce prediction errors. But due to the coarse resolution of the GRACE products, the EnKF does not work well in its usual form. Accordingly, different EnKF structures have been proposed and employed but a comparison between them has not yet been attempted. Here we assessed these structures using a synthetic problem. Alternative structures were formed using different increment calculation and updating strategies, observation operators, and the types of observation fed to the filter. It was found that all available structures led to an improvement in model performance when measured against a synthetic reference. However, the degree of improvement was strongly dependent on the assimilation strategy. Assimilating absolute TWS values (the summation of the TWS anomalies and an unbiased baseline) gave the best model performance when combined with an increment calculation strategy in which the increments are calculated and applied to all days of the month. However, without an unbiased baseline, assimilating TWS changes still leads to an acceptable improvement in model performance. Among the observation operators, those that predict the observations as an average of multiple days had the best performance.

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Section: Introductionmentioning

confidence: 99%

Performance of Different Ensemble Kalman Filter Structures to Assimilate GRACE Terrestrial Water Storage Estimates Into a High‐Resolution Hydrological Model: A Synthetic Study

Shokri

Walker

Dijk

et al. 2018

Water Resources Research

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“…Assimilation of GRACE-based TWS in the Catchment Land Surface Model (CLSM) [82] using an ensemble Kalman smoother method allowed better modeling of groundwater storage variations in the Mississippi River Basin and in 18 river basins in western and central Europe [83,84]. The impact of GRACE error correlation structure on the assimilation of GRACE data was very recently studied by [85,86]. Their results indicate that incorporating GRACE data's full covariance matrices results in more realistic groundwater estimations.…”

Section: Calibration And/or Assimilation Into Hydological Modelsmentioning

confidence: 99%

Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review

2018

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The Gravity Recovery and Climate Experiment (GRACE) satellite mission, which was in operation from March 2002 to June 2017, was the first remote sensing mission to provide temporal variations of Terrestrial Water Storage (TWS), which is the sum of the water masses that were contained in the soil column (i.e., snow, surface water, soil moisture, and groundwater), at a spatial resolution of a few hundred kilometers. As in situ level measurements are generally not sufficiently available for monitoring groundwater changes at the regional-scale, this unique dataset, combined with external information, is widely used to quantify the interannual variations of groundwater storage in the world's major aquifers. GRACE-based groundwater changes revealed significant aquifer depletion over large regions, such as the Middle East, the northwest India aquifer, the North China Plain aquifer, the Murray-Darling Basin in Australia, the High Plains, and the California Central Valley aquifers in the United States of America (USA), but were also used to estimate groundwater-related parameters such as the specific yield, which relates groundwater level to storage, or to define the indices of groundwater depletion and stress. In this review, the approaches used for estimating groundwater storage variations are presented along with the main applications of GRACE data for groundwater monitoring. Issues that were related to the use of GRACE-based TWS are also addressed.

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“…It has been proven that the ensemble Kalman filter (EnKF) can improve the accuracy of hydrologic models by merging observations with model predictions. Such observations include soil moisture (e.g., Aubert et al, ; Crow & Ryu, ; Houser et al, ; Pauwels et al, ; Reichle et al, , ; Walker & Houser, ), snow water equivalent (e.g., Barrett, ; Slater & Clark, ; Sun et al, ), streamflow (e.g., Lee et al, ; Clark et al, ), groundwater levels (e.g., Hendricks Franssen et al, ), turbulent heat fluxes (e.g., Bateni & Entekhabi, ; Pipunic et al, ; Xu et al, ) , microwave radiances (e.g., Dechant & Moradkhani, ), and terrestrial water storage (TWS; e.g., Ellett et al, ; Forman & Reichle, ; Forman et al, ; Girotto et al, ; ; Houborg et al, ; Khaki, Ait‐El‐Fquih, et al, ; Khaki, Hoteit, et al,; Khaki, Schumacher, et al, ; Kumar et al, ; Li et al, ; Li & Rodell, ; Smith, ; Tian et al, ; van Dijk et al, ; Zaitchik et al, ). The idea behind the EnKF is to combine observations and model estimates of state variables considering their relative error covariances.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation

Shokri

Walker

Dijk

et al. 2019

Water Resources Research

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The ensemble Kalman filter (EnKF) has been proved as a useful algorithm to merge coarse‐resolution Gravity Recovery and Climate Experiment (GRACE) data with hydrologic model results. However, in order for the EnKF to perform optimally, a correct forecast error covariance is needed. The EnKF estimates this error covariance through an ensemble of model simulations with perturbed forcing data. Consequently, a correct specification of perturbation magnitude is essential for the EnKF to work optimally. To this end, an adaptive EnKF (AEnKF), a variant of the EnKF with an additional component that dynamically detects and corrects error misspecifications during the filtering process, has been applied. Due to the low spatial and temporal resolutions of GRACE data, the efficiency of this method could be different than for other hydrologic applications. Therefore, instead of spatially or temporally averaging the internal diagnostic (normalized innovations) to detect the misspecifications, spatiotemporal averaging was used. First, sensitivity of the estimation accuracy to the degree of error in forcing perturbations was investigated. Second, efficiency of the AEnKF for GRACE assimilation was explored using two synthetic and one real data experiment. Results show that there is considerable benefit in using this method to estimate the forcing error magnitude and that the AEnKF can efficiently estimate this magnitude.

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Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization

Cited by 40 publications

References 95 publications

Performance of Different Ensemble Kalman Filter Structures to Assimilate GRACE Terrestrial Water Storage Estimates Into a High‐Resolution Hydrological Model: A Synthetic Study

Performance of Different Ensemble Kalman Filter Structures to Assimilate GRACE Terrestrial Water Storage Estimates Into a High‐Resolution Hydrological Model: A Synthetic Study

Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review

On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation

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