Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

Sabzehee, F.; Amiri-Simkooei, A. R.; Pour, Siavash Iran; Vishwakarma, Bramha Dutt; Kerachian, Reza

doi:10.1016/j.jenvman.2022.117180

Cited by 21 publications

(5 citation statements)

References 111 publications

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“…In recent years, deep learning has progressed rapidly and shown remarkable potential in modelling the Earth system [29][30][31] . Many studies investigated the potential of applying deep learning or classical machine learning approaches to downscale GRACE measurements in a supervised learning context 32,33 . The main challenge is the need for high-resolution ground truth of TWSAs, which are inaccessible.…”

Section: Global Downscaled Twsa Product With Uncertaintiesmentioning

confidence: 99%

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Gou,

Soja

2024

Nat Water

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Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for understanding our climate system. This study proposes a self-supervised data assimilation model with a new loss function to provide global TWSAs with a spatial resolution of 0.5°. The model combines hydrological simulations as well as measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite missions. The efficiency of the high-resolution information is proved by closing the water balance equation in small basins while preserving large-scale accuracy inherited from the GRACE(-FO) measurements. The product contributes to monitoring natural hazards locally and shows potential for better understanding the impacts of natural and anthropogenic activities on the water cycle. We anticipate our approach to be generally applicable to other TWSA data sources and the resulting products to be valuable for the geoscience community and society.

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Section: Global Downscaled Twsa Product With Uncertaintiesmentioning

confidence: 99%

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Gou,

Soja

2024

Nat Water

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“…Furthermore, data-driven approaches such as ML and more advanced DL techniques are being developed to (i) replace some model elements by satellite data [82], e.g. ET, SSM, and groundwater [83]; (ii) to downscale satellite data to be used for extracting information or constraining available models through, e.g., DA frameworks [84], [85], [86]; (iii) and a pure data-driven DL approach using satellite gravimetry, satellite laser ranging, hydrometeorological model outputs as learning datasets iteratively downscale TWS and groundwater storage (GWS) globally for water resources and climateinduced floods/droughts and cyclone-induced storm surges. The DL techniques for downscaling, e.g., [87], [88], [89], [90], [91] try to relate the smoothed GRACE/GRACE-FO signals to desired high-resolution fields, using training data, which can be those of available hydrological model outputs, forcing fields such as satellite-derived or reanalysis precipitation, evapotranspiration, and river discharge fields, along with auxiliary information such as high-resolution digital elevation fields to present the physical constraints.…”

Section: The Use Of Remote Sensing Big Data In Geodynamics At Regiona...mentioning

confidence: 99%

A review on how Big Data can help to monitor the environment and to mitigate risks due to climate change

Montillet,

Kermarrec,

Forootan

et al. 2024

Preprint

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Climate change triggers a wide range of hydrometeorological, glaciological and geophysical processes that span across vast spatiotemporal scales. With the advances in technology and analytics, a multitude of remote sensing, geodetic and in situ instruments have been developed to effectively monitor and help comprehend the Earth's system including its climate variability and the recent anomalies associated with global warming. A huge volume of data is generated by recording these observations, resulting in the need for novel methods to handle and interpret such Big Datasets. Managing this enormous amount of data extends beyond current computer storage considerations; it also encompasses the complexities of processing, modeling, and analysing. Big Datasets present unique characteristics that set them apart from smaller datasets, thereby posing challenges to traditional approaches. Moreover, computational time plays a crucial role, especially in the context of geohazard warning and response systems which necessitate low latency requirements. In this review, we delve into the monitoring and analysis of various climate change-related phenomena, including, but not limited to, droughts, floods, cyclones-induced storm surges, urban heat islands, ice mass balance, sea-level rise, and the modelling of the influence of solar variability on the Earth’s climate. By examining these phenomena, we explore some of the current and future trends in Big Data, aiming to encourage and speed-up the development of such techniques and promoting their benefits to timely monitor and towards achieving climate sustainability, thereby addressing its threat to humanity.

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“…Note that the coarse-scale inputs can either be obtained through observations or through simulations of low-fidelity models operating at coarser scales. In particular, machine learning models have been widely used for automatically projecting the coarserscale or lower-resolution environmental data into fine-scale or higher-resolution ones, which is especially popular in the domains of climate science, hydrology, and ecology [9,51,66,80,105,122,136,138,163,178]. For example, Wang et al [150] adopted super-resolution methods to generate higher-resolution predictions (e.g.…”

Section: Downscalingmentioning

confidence: 99%

Knowledge-Guided Machine Learning

Karpatne¹,

Kannan²,

Kumar³

2022

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This paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based models. It also provides an introduction to the current state of research in the emerging field of scientific knowledge-guided machine learning (KGML) that aims to use both scientific knowledge and data in ML frameworks to achieve better generalizability, scientific consistency, and explainability of results. We discuss different facets of KGML research in terms of the type of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML. We also discuss some of the common categories of use cases in environmental sciences where KGML methods are being developed, using illustrative examples in each category.

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Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods

Cited by 21 publications

References 111 publications

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

A review on how Big Data can help to monitor the environment and to mitigate risks due to climate change

Knowledge-Guided Machine Learning

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