A spatial covariance model for GRACE and GRACE-FO terrestrial water storage data

2020 ◽  
Author(s):  
Maik Thomas ◽  
Eva Boergens ◽  
Henryk Dobslaw ◽  
Robert Dill ◽  
Christoph Dahle ◽  
...  
Keyword(s):  
Bessel Function ◽  
Numerical Models ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Covariance Model ◽  
Spatial Covariance ◽  
Spatial Error ◽  
Grace Data ◽  
Uncertainty Estimates ◽  
Terrestrial Water

<p>Gridded terrestrial water storage (TWS) observed by GRACE or GRACE-FO typically show a spatial error structure that is anisotropic (direction depending), non-homogeneous (latitude depending), and non-stationary (time depending).</p><p>We will introduce a new covariance model characterizing this error behavior analytically with a direction depending Bessel function of the first kind. The anisotropy of this function is governed by a shape parameter allowing for longer correlation lengths in longitudinal than in latitudinal direction. The wave-effect of the Bessel function allows us to account for the residuals of the GRACE striping errors. Both size as well as shape parameters of the Bessel function vary smoothly with latitude. These variations are implemented via even Legendre polynomials. The non-stationarity of the covariance is modeled with time-varying point variances. The validity of this covariance model on the sphere was thoroughly tested with a Monte-Carlo approach.</p><p>First, we apply this covariance model to 5 years of simulated GRACE data (Flechtner et al., 2016) where true errors are readily available from the differences of the synthetic input and the finally recovered gravity fields. For the 50 largest discharge basins, we obtain more realistic time series uncertainties than from propagating the formal errors associated with the Stokes coefficients. For smaller basins, however, the covariance model tends to provide overly pessimistic uncertainty estimates.</p><p>Second, the model is adapted to real GRACE and GRACE-FO data to obtain realistic error covariance information for arbitrarily shaped basins from globally gridded error information. We will show the current plans to update GFZ’s GravIS portal (http://gravis.gfz-potsdam.de/home) so that area- and time-dependent error information which is critically important for the assimilation of GRACE-based TWS data into numerical models will become readily available to the user community.</p>

scholarly journals Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

2015 ◽  
Vol 19 (4) ◽  
pp. 2079-2100 ◽  
Author(s):  
N. Tangdamrongsub ◽  
S. C. Steele-Dunne ◽  
B. C. Gunter ◽  
P. G. Ditmar ◽  
A. H. Weerts
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rhine River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

scholarly journals Modelling spatial covariances for terrestrial water storage variations verified with synthetic GRACE-FO data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Eva Boergens ◽  
Henryk Dobslaw ◽  
Robert Dill ◽  
Maik Thomas ◽  
Christoph Dahle ◽  
...  
Keyword(s):  
Water Storage ◽  
Longitudinal Direction ◽  
Correlation Model ◽  
Simulation Environment ◽  
Terrestrial Water Storage ◽  
Covariance Model ◽  
Spatial Correlation Structure ◽  
Terrestrial Water ◽  
Covariance Models ◽  
Standard Deviations

AbstractGridded terrestrial water storage (TWS) variations observed by GRACE or GRACE-FO typically show a spatial correlation structure that is both anisotropic (direction-dependent) and non-homogeneous (latitude-dependent). We introduce a new correlation model to represent this structure. This correlation model allows GRACE and GRACE-FO data users to get realistic correlations of the TWS grids without the need to derive them from the formal spherical harmonic uncertainties. Further, we found that the modelled correlations fit the spatial structure of uncertainties to a greater extent in a simulation environment. The model is based on a direction-dependent Bessel function of the first kind which allows to model the longer correlation lengths in the longitudinal direction via a shape parameter, and also to account for residual GRACE striping errors that might remain after spatial filtering. The global scale and shape parameters vary with latitude by means of even Legendre polynomials. The correlation between two points transformed to covariance by scaling with the standard deviations of each point. The covariance model is valid on the sphere which is empirically verified with a Monte-Carlo approach. The covariance model is subsequently applied to 5 years of simulated GRACE-FO data which allow for immediate validation with true uncertainties from the differences between the input mass signal and the recovered gravity fields. Four different realisations of the point standard deviations were tested: two based on the formal errors provided with the simulated Stokes coefficients, and two based on empirical standard deviations, where the first is spatially variant and temporally invariant, and the second spatially invariant and temporally variant. These four different covariance models are applied to compute TWS time series uncertainties for both the fifty largest discharge basins and regular grid cells over the continents. These four models are compared with the true uncertainties available in the simulations. The two empirically-based covariance models provide more realistic TWS uncertainties than the ones based on the formal errors. Especially, the empirically-based covariance models are better in reflecting the spatial pattern of the uncertainties of the simulated GRACE-FO data including their latitude dependence. However, these modelled uncertainties are in general too large. But with only one global scaling factor, a statistical test confirms the equivalence between the empirically-based covariance model with temporally variable point standard deviations and the true uncertainties. Thus at the end, this covariance model represents the closest fit in the simulation environment. The simulated GRACE-FO data are assumed to be very realistic which is why we recommend the new covariance model to be further investigated for the characterisation of real GRACE and GRACE-FO terrestrial water storage data.

scholarly journals A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 401 ◽  
Author(s):  
Vagner Ferreira ◽  
Samuel Andam-Akorful ◽  
Ramia Dannouf ◽  
Emmanuel Adu-Afari
Keyword(s):  
West Africa ◽  
Water Storage ◽  
Remotely Sensed ◽  
Climate Indices ◽  
Terrestrial Water Storage ◽  
Drought Assessment ◽  
Grace Data ◽  
Short Period ◽  
Terrestrial Water ◽  
Land Data Assimilation System

Remotely sensed terrestrial water storage changes (TWSC) from the past Gravity Recovery and Climate Experiment (GRACE) mission cover a relatively short period (≈15 years). This short span presents challenges for long-term studies (e.g., drought assessment) in data-poor regions like West Africa (WA). Thus, we developed a Nonlinear Autoregressive model with eXogenous input (NARX) neural network to backcast GRACE-derived TWSC series to 1979 over WA. We trained the network to simulate TWSC based on its relationship with rainfall, evaporation, surface temperature, net-precipitation, soil moisture, and climate indices. The reconstructed TWSC series, upon validation, indicate high skill performance with a root-mean-square error (RMSE) of 11.83 mm/month and coefficient correlation of 0.89. The validation was performed considering only 15% of the available TWSC data not used to train the network. More so, we used the total water content changes (TWCC) synthesized from Noah driven global land data assimilation system in a simulation under the same condition as the GRACE data. The results based on this simulation show the feasibility of the NARX networks in hindcasting TWCC with RMSE of 8.06 mm/month and correlation coefficient of 0.88. The NARX network proved robust to adequately reconstruct GRACE-derived TWSC estimates back to 1979.

The uncertainty of storage-based drought indices from GRACE

2020 ◽  
Author(s):  
Peyman Saemian ◽  
Mohammad Javad Tourian ◽  
Nico Sneeuw
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Extended Period ◽  
Drought Indices ◽  
Terrestrial Water Storage ◽  
Earth’S Gravity Field ◽  
Grace Data ◽  
Temporal And Spatial Variability ◽  
Terrestrial Water ◽  
Gravity Recovery

<p>Climate change and the growing demand for freshwater have raised the frequency and intensity of extreme events like drought. Satellite observations have improved our understanding of the temporal and spatial variability of droughts. Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) have been observing variations in Earth's gravity field yielding valuable information about changes in terrestrial water storage anomaly (TWSA). The terrestrial water storage vertically integrates all forms of water on and beneath land surface including snow, surface water, soil moisture, and groundwater storage.</p><p>Drought indices help to monitor drought by characterizing it in terms of their severity, location, duration and timing. Several drought indices have been developed based on GRACE water storage anomaly from a GRACE-based climatology, most of which suffer from the short record of GRACE, about 15 years, for their climatology. The limited duration of the GRACE observations necessitates the use of external datasets of TWSA with a more extended period for climatology. Drought characterization comes with its own uncertainties due to the inherent uncertainty in the GRACE data, the various post-processing approaches of GRACE data, and different options for external datasets on the other hand.</p><p>This study offers a method to quantify uncertainties for the storage-based drought index. Moreover, we assess the sensitivity of major global river basins to the duration of the observations. The outcome of the study is invaluable in the sense that it allows for a more informative storage based drought, including uncertainty, thus enabling a more realistic risk assessment.</p>

scholarly journals Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

2021 ◽  
Author(s):  
Natthachet Tangdamrongsub ◽  
Michael F. Jasinski ◽  
Peter Shellito
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

scholarly journals Validation of terrestrial water storage variations as simulated by different global numerical models with GRACE satellite observations

2017 ◽  
Vol 21 (2) ◽  
pp. 821-837 ◽  
Author(s):  
Liangjing Zhang ◽  
Henryk Dobslaw ◽  
Tobias Stacke ◽  
Andreas Güntner ◽  
Robert Dill ◽  
...  
Keyword(s):  
Large Scale ◽  
Water Cycle ◽  
Numerical Models ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Satellite Mission ◽  
Validation Data ◽  
Data Set ◽  
Terrestrial Water ◽  
Grace Satellite

Abstract. Estimates of terrestrial water storage (TWS) variations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are used to assess the accuracy of four global numerical model realizations that simulate the continental branch of the global water cycle. Based on four different validation metrics, we demonstrate that for the 31 largest discharge basins worldwide all model runs agree with the observations to a very limited degree only, together with large spreads among the models themselves. Since we apply a common atmospheric forcing data set to all hydrological models considered, we conclude that those discrepancies are not entirely related to uncertainties in meteorologic input, but instead to the model structure and parametrization, and in particular to the representation of individual storage components with different spatial characteristics in each of the models. TWS as monitored by the GRACE mission is therefore a valuable validation data set for global numerical simulations of the terrestrial water storage since it is sensitive to very different model physics in individual basins, which offers helpful insight to modellers for the future improvement of large-scale numerical models of the global terrestrial water cycle.

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