Can daily GRACE gravity field models be used to evaluate short-term hydro-meteorological signals over the continents?

2020 ◽  
Author(s):  
Annette Eicker ◽  
Laura Jensen ◽  
Viviana Wöhnke ◽  
Andreas Kvas ◽  
Henryk Dobslaw ◽  
...  
Keyword(s):  
Time Series ◽  
Gravity Field ◽  
Time Scales ◽  
Water Flux ◽  
Water Storage ◽  
Numerical Differentiation ◽  
Data Set ◽  
Grace Data ◽  
Terrestrial Water ◽  
Gravity Field Models

<p>Over the recent years, the computation of temporally high-resolution (daily) GRACE gravity field solutions has advanced as an alternative to the processing of monthly models. In this presentation we will show that recent processing improvements incorporated in the latest version of daily gravity field models (ITSG-Grace2018) now allow for the investigation of water flux signals on the continents down to time scales of a few days.</p><p>Time variations in terrestrial water storage derived from GRACE can be related to atmospheric net-fluxes of precipitation (P), evapotranspiration (E) and lateral runoff (R) via the terrestrial water balance equation, which makes GRACE a new and completely independent data set for constraining hydro-meteorological observations and the output of atmospheric reanalyses.</p><p>In our study, band-pass filtered water fluxes are derived from the daily GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days. We can show that on these time scales GRACE is able to identify quality differences between different global reanalyses, e.g. the improvements in the latest reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) over its direct predecessor ERA-Interim.</p><p>We can further demonstrate that only the very recent progress in GRACE data processing has enabled the use of daily GRACE time series for such an evaluation of high-frequency atmospheric fluxes. The accuracy of the previous daily GRACE time series ITSG-Grace2016 would not have been sufficient to carry out such an assessment.</p>

Evaluating short-term hydro-meteorological fluxes in global atmospheric reanalyses using daily GRACE data

2020 ◽  
Author(s):  
Viviana Wöhnke ◽  
Annette Eicker ◽  
Laura Jensen ◽  
Andreas Kvas ◽  
Torsten Mayer-Gürr ◽  
...  
Keyword(s):  
Water Storage ◽  
Numerical Differentiation ◽  
Evaluation Tool ◽  
Satellite Gravity ◽  
Data Set ◽  
Weather Forecasts ◽  
Grace Data ◽  
Terrestrial Water ◽  
Band Pass ◽  
Net Flux

<p>Changes in terrestrial water storage as observed by the satellite gravity mission GRACE represent a new and completely independent data set for constraining the net flux deficit of precipitation (P), evapotranspiration (E), and lateral runoff (R) in atmospheric reanalyses.</p><p>In this study we use daily GRACE gravity field changes to investigate high-frequency hydro-meteorological fluxes over the continents. Band-pass filtered water fluxes are derived from GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days.</p><p>We can show that on these time scales GRACE is able to identify quality differences between different reanalyses, e.g. the improvements in the latest reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) over its direct predecessor ERA-Interim. We will therefore use GRACE as an evaluation tool to compare hydro-meteorological fluxes in various global atmospheric reanalyses, such as ERA5(-Land), ERA-Interim, Merra2, JRA-55, or NCEP.</p>

scholarly journals Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis

2022 ◽  
Vol 14 (2) ◽  
pp. 282
Author(s):  
Bin Liu ◽  
Wenkun Yu ◽  
Wujiao Dai ◽  
Xuemin Xing ◽  
Cuilin Kuang
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Hydrological Model ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Coordinate Time ◽  
Yunnan Region ◽  
Grace Data ◽  
Terrestrial Water ◽  
Gps Observations

GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing.

scholarly journals Global modelling of continental water storage changes – sensitivity to different climate data sets

2007 ◽  
Vol 11 ◽  
pp. 63-68 ◽  
Author(s):  
K. Fiedler ◽  
P. Döll
Keyword(s):  
Water Storage ◽  
Global Scale ◽  
Data Sets ◽  
Precipitation Data ◽  
Climate Data ◽  
Satellite Mission ◽  
Full Data ◽  
Data Set ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Since 2002, the GRACE satellite mission provides estimates of the Earth's dynamic gravity field with unprecedented accuracy. Differences between monthly gravity fields contain a clear hydrological signal due to continental water storage changes. In order to evaluate GRACE results, the state-of-the-art WaterGAP Global Hydrological Model (WGHM) is applied to calculate terrestrial water storage changes on a global scale. WGHM is driven by different climate data sets to analyse especially the influence of different precipitation data on calculated water storage. The data sets used are the CRU TS 2.1 climate data set, the GPCC Full Data Product for precipitation and data from the ECMWF integrated forecast system. A simple approach for precipitation correction is introduced. WGHM results are then compared with GRACE data. The use of different precipitation data sets leads to considerable differences in computed water storage change for a large number of river basins. Comparing model results with GRACE observations shows a good spatial correlation and also a good agreement in phase. However, seasonal variations of water storage as derived from GRACE tend to be significantly larger than those computed by WGHM, regardless of which climate data set is used.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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.

ITG-GRACE: Global Static and Temporal Gravity Field Models from GRACE Data

2010 ◽  
pp. 159-168 ◽  
Author(s):  
Torsten Mayer-Gürr ◽  
Annette Eicker ◽  
Enrico Kurtenbach ◽  
Karl-Heinz Ilk
Keyword(s):  
Gravity Field ◽  
Grace Data ◽  
Temporal Gravity ◽  
Gravity Field Models

scholarly journals Variations in China's terrestrial water storage over the past decade using GRACE data

2015 ◽  
Vol 6 (3) ◽  
pp. 187-193 ◽  
Author(s):  
Qian Zhao ◽  
Weiwei Wu ◽  
Yunlong Wu
Keyword(s):  
Water Storage ◽  
Terrestrial Water Storage ◽  
The Past ◽  
Grace Data ◽  
Terrestrial Water

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.

scholarly journals Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China

2017 ◽  
Vol 9 (10) ◽  
pp. 1011 ◽  
Author(s):  
Zhangli Sun ◽  
Xiufang Zhu ◽  
Yaozhong Pan ◽  
Jinshui Zhang
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Water Storage ◽  
The Yangtze River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water ◽  
The Yangtze River Basin
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