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GRACE Data Assimilation for Understanding the Earth System

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3786

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Dr. Natthachet Tangdamrongsub

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Guest Editor

Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
Interests: remote sensing; land surface modelling; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Pavel G. Ditmar

E-Mail Website1 Website2
Guest Editor

Department of Geoscience and Remote Sensing, Delft University of Technology, 2600 GA Delft, The Netherlands
Interests: satellite gravimetry; mass transport; data processing

Prof. Dr. Ehsan Forootan

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Guest Editor

Geodesy and Earth Observation Group, Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark
Interests: satellite gravity; satellite altimetry; satellite remote-sensing data assimilation; calibration; inversion
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Special Issue Information

Dear Colleagues,

Since the launch of the Gravity Recovery and Climate Experiment (GRACE) satellite mission in 2002, satellite gravimetry has become the primary tool to use in studying mass transport in the Earth’s system at the global and regional scales. Data from GRACE and its successor, GRACE Follow-On (GFO) mission, have substantially contributed to the quantification of various mass transport processes at the Earth’s surface and in the Earth’s interior. An asset of the GRACE/GFO missions is their ability to provide accurate estimates of mass anomalies, with errors being almost uncorrelated in the time domain. However, the spatial and temporal resolution of GRACE-/GFO-based estimates is limited. On the other hand, geophysical models of various processes in the Earth’s system may offer a much higher spatial and temporal resolution, but typically suffer from systematic errors in the obtained estimates. These errors may be caused, among other issues, by the imperfect calibration of the model parameters and an accumulation of noise in the course of the integration of fluxes into the time domain. Thus, the assimilation of GRACE/GFO data into a geophysical model can be considered a powerful tool for maximizing the synergy of these two sources of information.

Authors may submit both full-length papers and shorter notes. The manuscripts may focus on any GRACE/GFO applications area, such as hydrology, oceanography, atmosphere, cryosphere and solid Earth studies. Manuscripts addressing theoretical aspects of GRACE/GFO data assimilation are welcome as well. Any types of GRACE-/GFO-based input data may be exploited, such as mass anomalies, spherical harmonic coefficients or inter-satellite ranging data. Simultaneous assimilation of GRACE/GFO and other data in a joint or multivariate data assimilation framework is encouraged. The goals of the data assimilation can also be diverse, e.g., an improvement of model parameters (model calibration), improved model forcing, an improvement of model states, as well as a combination of them. Both hindcast and forecast applications are of interest. A clear demonstration of the added value of a GRACE/GFO data assimilation will be particularly appreciated.

Dr. Natthachet Tangdamrongsub
Dr. Pavel G. Ditmar
Prof. Dr. Ehsan Forootan
Guest Editors

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Published Papers (3 papers)

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Research

21 pages, 8074 KiB

Open AccessArticle

Groundwater Storage Variations across Climate Zones from Southern Poland to Arctic Sweden: Comparing GRACE-GLDAS Models with Well Data

by Zofia Rzepecka, Monika Birylo, Jerker Jarsjö, Feifei Cao and Jan Pietroń

Remote Sens. 2024, 16(12), 2104; https://doi.org/10.3390/rs16122104 - 11 Jun 2024

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Abstract

The aim of this paper is to assess the correlation of groundwater level changes (or groundwater level anomalies (GWLA)) obtained from direct measurements in wells with groundwater storage anomalies (GWSA) calculated using Gravity Recovery and Climate Experiment (GRACE) products and Global Land Data Assimilation Systems (GLDAS) models across different climate zones, from temperate Poland to Arctic Sweden. We recognize that such validation studies are needed to increase the understanding of the spatio-temporal limits of remote sensing model applicability, not least in data-scarce sub-Arctic and Arctic environments where processes are complex due to the impacts of snow and (perma) frost. Results for temperate climates in Poland and southern Sweden show that, whereas one of the models (JPL_NOAH_GWSA) failed due to water balance term overestimation, the other model (CSR_CLM_GWSA) produced excellent results of monthly groundwater dynamics when compared with the observations in 387 groundwater wells in the region during 2003–2022 (cross-correlation coefficient of 0.8). However, for the sub-Arctic and Arctic northern Sweden, the model suitable for other regions failed to reproduce typical northern groundwater regimes (of the region’s 85 wells), where winter levels decrease due to the blocking effect of ground frost on groundwater recharge. This suggests, more generally, that conventional methods for deriving GWSA and its seasonality ceases to be reliable in the presence of considerably infiltration-blocking ground frost and permafrost (whereas snow storage modules perform well), which hence need further attention in future research. Regarding long-term groundwater level trends, remote sensing results for southern Sweden show increasing levels, in contrast with observed unchanged to decreasing (~10 mm/a) levels, which may not necessarily be due to errors in the remote sensing model but may rather emphasize impacts of anthropogenic pressures, which are higher near the observation wells that are often located in eskers used for water supply. For sub-Arctic and Arctic Sweden, the (relatively uncertain) trend of the remote sensing results nevertheless agrees reasonably well with the groundwater well observations that show increasing groundwater levels of up to ~14 mm/a, which, e.g., is consistent with reported trends of large Siberian river basins. Full article

(This article belongs to the Special Issue GRACE Data Assimilation for Understanding the Earth System)

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26 pages, 5148 KiB

Open AccessArticle

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

by Hussein A. Mohasseb, Wenbin Shen and Jiashuang Jiao

Remote Sens. 2024, 16(8), 1424; https://doi.org/10.3390/rs16081424 - 17 Apr 2024

Cited by 2 | Viewed by 857

Abstract

Over the past two decades, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor, GRACE-follow on (GRACE-FO), have played a vital role in climate research. However, the absence of certain observations during and between these missions has presented a persistent challenge. Despite numerous studies attempting to address this issue with mathematical and statistical methods, no definitive optimal approach has been established. This study introduces a practical solution using Linear Regression Analysis (LRA) to overcome data gaps in both GRACE data types—mascon and spherical harmonic coefficients (SHCs). The proposed methodology is tailored to monsoon patterns and demonstrates efficacy in filling data gaps. To validate the approach, a global analysis was conducted across eight basins, monitoring changes in total water storage (TWS) using the technique. The results were compared with various geodetic products, including data from the Swarm mission, Institute of Geodesy and Geoinformation (IGG), Quantum Frontiers (QF), and Singular Spectrum Analysis (SSA) coefficients. Artificial data gaps were introduced within GRACE observations for further validation. This research highlights the effectiveness of the monsoon method in comparison to other gap-filling approaches, showing a strong similarity between gap-filling results and GRACE’s SHCs, with an absolute relative error approaching zero. In the mascon approach, the coefficient of determination (R²) exceeded 91% for all months. This study offers a readily usable gap-filling product—SHCs and smoothed gridded observations—with accurate error estimates. These resources are now accessible for a wide range of applications, providing a valuable tool for the scientific community. Full article

(This article belongs to the Special Issue GRACE Data Assimilation for Understanding the Earth System)

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21 pages, 6989 KiB

Open AccessArticle

Assessing Groundwater Sustainability in the Arabian Peninsula and Its Impact on Gravity Fields through Gravity Recovery and Climate Experiment Measurements

by Hussein A. Mohasseb, Wenbin Shen, Hussein A. Abd-Elmotaal and Jiashuang Jiao

Remote Sens. 2024, 16(8), 1381; https://doi.org/10.3390/rs16081381 - 13 Apr 2024

Cited by 2 | Viewed by 1301

Abstract

This study addresses the imperative to comprehend gravity shifts resulting from groundwater storage (GWS) variations in the Arabian Peninsula. Despite the critical importance of water resource sustainability and its relationship with gravity, limited research emphasizes the need for expanded exploration. The investigation explores the impact of GWS extraction on the gravity field, utilizing Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data in addition to validation using the WaterGAP Global Hydrology Model (WGHM). Spanning April 2002 to June 2023, this study predicts GWS trends over the next decade using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The comprehensive time-series analysis reveals a significant GRACE-derived groundwater storage (GWS) trend of approximately −4.90 ± 0.32 mm/year during the study period. This trend has a notable impact on the gravity anomaly (GA) values, as observed through the decomposition analysis. The projected GWS indicates a depletion rate of 14.51 km³/year over the next decade. The correlation between GWS and GA is substantial at 0.80, while the GA and rainfall correlation is negligible due to low precipitation rates. Employing multiple linear regression explains 80.61% of the variance in gravity anomaly due to GWS, precipitation, and evapotranspiration. This study investigates climate change factors—precipitation, temperature, and evapotranspiration—providing a holistic understanding of the forces shaping GWS variations. Precipitation and evapotranspiration exhibit nearly equal values, limiting GWS replenishment opportunities. This research holds significance in studying extensive GWS withdrawal in the Arabian Peninsula, particularly concerning crust mass stability. Full article

(This article belongs to the Special Issue GRACE Data Assimilation for Understanding the Earth System)

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