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Abstract
Understanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment
(GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC)
error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance
information in these studies.
(GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC)
error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance
information in these studies.
Original language | English |
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Article number | e2023WR036764 |
Journal | Water Resources Research |
Volume | 60 |
Issue number | 9 |
Number of pages | 31 |
ISSN | 0043-1397 |
DOIs | |
Publication status | Published - Sept 2024 |
Bibliographical note
Yang, F., Forootan, E., Liu, S., & Schumacher, M. (2024). A Monte Carlopropagation of the full variance‐covariance of GRACE‐like level‐2 data with
applications in hydrological data assimilation and sea‐level budget studies.
Water Resources Research, 60, e2023WR036764. https://doi.org/10.1029/
2023WR036764
Keywords
- Bayesian
- Covariance Matrix
- GRACE
- GRACE-FO
- Global
- Hydrology
- Marcov Chain
- Numerical
- Terrestrial water storage (TWS)
- Uncertainty analysis
- global and large-scale hydrology
- uncertainty
- error propagation
- GRACE and GRACE-FO
- Monte Carlo
- terrestrial water storage
Fingerprint
Dive into the research topics of 'A Monte Carlo Propagation of the Full Variance‐Covariance of GRACE‐Like Level‐2 Data With Applications in Hydrological Data Assimilation and Sea‐Level Budget Studies'. Together they form a unique fingerprint.Projects
- 1 Active
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DANSk-LSM: Developing efficient multi-sensor Data Assimilation frameworks for integrating Earth ObservatioN Satellite data into Land Surface Models (DANSk-LSM)
Forootan, E. (PI), Schumacher, M. (CoI), Yang, F. (Project Participant) & Retegui Schiettekatte, L. A. (Project Participant)
01/09/2022 → 31/08/2026
Project: Research
Research output
- 1 Citations
- 1 Journal article
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PyGLDA: a fine-scale Python-based Global Land Data Assimilation system for integrating satellite gravity data into hydrological models
Yang, F., Schumacher, M., Retegui-Schiettekatte, L., van Dijk, A. I. & Forootan, E., 19 Jul 2024, (Submitted) In: Geoscientific Model Development Discussions. 2024, p. 1-34 34 p.Research output: Contribution to journal › Journal article › Research
Datasets
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An efficient and accurate Python tool for uncertainty quantification of GRACE based TWS
Yang, F. (Creator) & Forootan, E. (Creator), Figshare, 9 Oct 2023
DOI: 10.6084/m9.figshare.24272485.v2, https://figshare.com/articles/software/An_efficient_and_accurate_python_tool_for_uncertainty_quantification_of_GRACE_based_TWS/24272485/2
Dataset