Filling the gap between GRACE-and GRACE-FO-derived terrestrial water storage anomalies with Bayesian convolutional neural networks

Mo, Shaoxing; Zhong, Yulong; Shi, Xiaoqing; Wang, Feng; Yin, Xiong; Wu, Jichun

doi:10.1002/essoar.10505916.1

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…(2020), Landerer (2021a, 2021b, 2021c, 2023a, 2023b, 2023c), Loomis (2021), Mo et al. (2021), Muñoz‐Sabater (2019), Save (2020), Teixeira da Encarnação et al. (2019), and Wiese et al.…”

Section: Data Availability Statementmentioning

confidence: 99%

A Two‐Step Linear Model to Fill the Data Gap Between GRACE and GRACE‐FO Terrestrial Water Storage Anomalies

Yang,

You,

Tian

et al. 2023

Water Resources Research

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The Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On (GRACE‐FO) missions have revolutionized global terrestrial water storage anomalies (TWSA) measurements. However, the 11‐month data gap between the two GRACE missions disrupts the measurement continuity and limits its further applications. Previous attempts to fill this data gap require further improvement in terms of method robustness and product quality. Here, we propose a novel two‐step linear model using precipitation, temperature data, and hydrological model‐simulated TWSA as predictors to fill the 11‐month data gap between the two GRACE missions and generate six global gridded GRACE‐like TWSA products from April 2002 to July 2021. These products are evaluated at grid scale globally and also basin scale for the world's largest 72 river basins. Results indicate that our GRACE‐like data show great consistency with the GRACE/GRACE‐FO observations. While most basins exhibit consistent performance across the six GRACE‐like TWSA products, certain areas with lower signal‐to‐noise ratios show significant variability. Furthermore, we assess the performance of our GRACE‐like data during the data gap using one previous reconstruction, a hydrological model simulation, and the Swarm satellite measurement. The results confirm that our GRACE‐like data exhibit equivalent performance within and outside the data gap. This study introduces a more simple and robust method for predicting the missing data between the two GRACE missions and provides readily applicable continuous GRACE‐like TWSA products for hydrologic applications.

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Section: Data Availability Statementmentioning

confidence: 99%

A Two‐Step Linear Model to Fill the Data Gap Between GRACE and GRACE‐FO Terrestrial Water Storage Anomalies

Yang,

You,

Tian

et al. 2023

Water Resources Research

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“…However, there is about one-year observation gap between GRACE and GRACE-FO (from July 2017 to May 2018). Therefore, Mo et al (2021) recently attempted to employee Bayesian convolutional neural networks for lling the gap between GRACE and GRACE-FO satellite data. Recently, Ali et al (2021) endeavored to increase the resolution of GRACE products using downscaling techniques.…”

Section: Grace Gravitational Satellite Mission Is a Joint Mission Of ...mentioning

confidence: 99%

Groundwater storage change estimation using GRACE satellite data in the Helmand River Basin of Afghanistan

Nazari

Zaryab

Ahmadi

2022

Preprint

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Understanding dynamic changes of groundwater storage and quantifying the trends of groundwater fluctuations in the Helmand River Basin (HRB) aquifers, where the groundwater is the main source for drinking and irrigation applications is significantly important for effective management of groundwater resources. This case study quantifies the changes in groundwater storage over 18 years (i.e., from 2003 to 2021) in the HRB by employing the Gravity Recovery and Climate Experiment (GRACE) observations and Global Land Data Assimilation System (GLDAS) data due to sparse groundwater monitoring networks and lack of ground-based information. The data of observation wells was used to validate the results of the GRACE and GLDAS outputs. The results indicate that changes in groundwater storage on average from 2003 to 2021 are equal to (-112.4 ± 237.14 mm or -2.048 ± 4.32 km3/year). In average, during 2003 to 2021 groundwater table decline was -6.10 m in the HRB. The study indicated relatively strong correlation (0.75) between the GRACE derived data and direct in situ measurements. This study highlights the effectiveness of the GRACE-derived data for the reliable estimation of groundwater storage changes in the HRB and may contribute to sustainable groundwater resources management in the region.

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“…Especialmente relevante es en este campo la misión espacial GRACE (Experimento de Clima y Recuperación Gravitatoria; Tapley y Reigber 2001), que mide, entre otras, la tendencia en nivel piezométrico de las reservas de agua subterránea globales. Las redes neuronales convolucionales se han usado para completar datos de anomalías en la estimación de almacenamiento de agua terrestre derivadas del Experimento de Clima y Recuperación Gravitatoria (Mo et al 2021), obteniendo una caracterización más completa de las masas de agua subterránea. Además, para la gestión de masas de agua subterránea es clave conocer el comportamiento hidrológico a nivel regional y local, cuestión que es posible abordar utilizando un gran volumen de datos de la zona (Kratzert et al 2019).…”

Section: Variables Biofísicas En El Estudio De La Desertificaciónunclassified

Potential of artificial intelligence to advance the study of desertification

Guirado

Martínez‐Valderrama

2021

ECOS

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La desertificación es un problema global que afecta a más de 1.500 millones de personas que viven en los lugares más pobres y vulnerables del planeta. En los últimos años numerosos estudios han contribuido a aportar información para evaluar el problema. Algunos de ellos se basan en analizar variables biofísicas y socio-económicas mediante técnicas de inteligencia artificial. Por ejemplo, se han usado para completar datos de anomalías en la estimación de almacenamiento de agua, la identificación precisa de cobertura del suelo, estimación de la radiación solar diaria a nivel global y mejora en predicciones climáticas, entre otras. Si bien su uso todavía no está muy extendido, el futuro en los estudios sobre desertificación parece prometedor. En este trabajo revisamos el potencial de las técnicas de inteligencia artificial (aprendizaje automático y aprendizaje profundo) en el estudio de la desertificación y su reciente crecimiento en los últimos años. Durante el periodo 2015-2020 el número de publicaciones que implementan el aprendizaje profundo se incrementó un 63%, mientras que para el aprendizaje automático su crecimiento fue más modesto, del 3%. En particular, cuando buscamos estudios relacionados con la desertificación, las cifras de crecimiento son más llamativas: un incremento medio del 103% en estudios con aprendizaje profundo, y del 43% en aprendizaje automático. Sin embargo, se requieren más estudios y esfuerzos que agrupen todas las disciplinas implicadas en el estudio de la desertificación para obtener una visión global y transversal de este fenómeno y así diseñar acciones efectivas para mitigar sus efectos adversos o anticiparse a ellos.

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Filling the gap between GRACE-and GRACE-FO-derived terrestrial water storage anomalies with Bayesian convolutional neural networks

Cited by 4 publications

References 46 publications

A Two‐Step Linear Model to Fill the Data Gap Between GRACE and GRACE‐FO Terrestrial Water Storage Anomalies

A Two‐Step Linear Model to Fill the Data Gap Between GRACE and GRACE‐FO Terrestrial Water Storage Anomalies

Groundwater storage change estimation using GRACE satellite data in the Helmand River Basin of Afghanistan

Potential of artificial intelligence to advance the study of desertification

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