Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method

Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5° to 0.25°, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5° to 0.05° by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at −20.86 mm/yr and −21.79 mm/yr (−14.53 mm/yr and −15.46 mm/yr), respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products.

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Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

10.5194/hess-2020-665 ◽

2021 ◽

Author(s):

Natthachet Tangdamrongsub ◽

Michael F. Jasinski ◽

Peter Shellito

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Water Storage ◽

Land Surface Model ◽

Surface Model ◽

Model Parameters ◽

Terrestrial Water Storage ◽

Grace Data ◽

Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

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Deep Learning Approaches to Spatial Downscaling of GRACE Terrestrial Water Storage Products Using EALCO Model Over Canada

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2021.1954498 ◽

2021 ◽

pp. 1-19

Author(s):

Hongjie He ◽

Ke Yang ◽

Shusen Wang ◽

Hasti Andon Petrosians ◽

Ming Liu ◽

...

Keyword(s):

Deep Learning ◽

Water Storage ◽

Learning Approaches ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

Storage Products ◽

Spatial Downscaling

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Emerging instability in global terrestrial water storage since 2010

10.5194/egusphere-egu2020-17922 ◽

2020 ◽

Author(s):

Shuang Yi ◽

Nico Sneeuw

Keyword(s):

Sea Level ◽

Water Storage ◽

Precipitation Trend ◽

Terrestrial Water Storage ◽

Level Change ◽

Before And After ◽

Terrestrial Water ◽

Global Sea Level ◽

The Stability ◽

Global Precipitation

<p>Global terrestrial water storage (TWS) is an indicator of the integrated impact of climate variability on the environment. Accurate assessments of global TWS also facilitate the understanding of disturbances in global sea level rise. Gravity satellite GRACE has proved to be an effective tool in monitoring global TWS changes. With the latest observations of GRACE and GRACE Follow-on, we estimated the global TWS from April 2002 to October 2019, and found contrasting variations in global TWS before and after 2010. Before 2010, the global TWS was almost stable with variations that contribute only a few millimeters of sea level change; while the stability is ceased after the 2010/11 La Nina and three drastic fluctuations of up to 10 millimeters sea level contribution have occurred since then. We find these TWS changes have a good linear relationship with the global precipitation trend, rather than the accumulation of net precipitation, indicating that the precipitation trend is the main driving force of the recent global TWS instability. We further investigate the sensitivities of TWS to precipitation in basins.</p>

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Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China

Remote Sensing ◽

10.3390/rs13173358 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3358

Author(s):

Yifan Shen ◽

Wei Zheng ◽

Wenjie Yin ◽

Aigong Xu ◽

Huizhong Zhu ◽

...

Keyword(s):

Machine Learning ◽

Southwest China ◽

Pearson Correlation ◽

Water Storage ◽

Terrestrial Water Storage ◽

Study Region ◽

Southwest Yunnan ◽

Terrestrial Water ◽

Land Data Assimilation System ◽

Gravity Recovery

Dense Global Position System (GPS) arrays can be used to invert the terrestrial water-storage anomaly (TWSA) with higher accuracy. However, the uneven distribution of GPS stations greatly limits the application of GPS to derive the TWSA. Aiming to solve this problem, we grid the GPS array using regression to raise the reliability of TWSA inversion. First, the study uses the random forest (RF) model to simulate crustal deformation in unobserved grids. Meanwhile, the new Machine-Learning Loading-Inverted Method (MLLIM) is constructed based on the traditional GPS derived method to raise the truthfulness of TWSA inversion. Second, this research selects southwest China as the study region, the MLLIM and traditional GPS inversion methods are used to derive the TWSA, and the inverted results are contrasted with datasets of the Gravity Recovery and Climate Experiment (GRACE) Mascon and the Global Land Data Assimilation System (GLDAS) model. The comparison shows that values of Pearson Correlation Coefficient (PCC) between the MLLIM and GRACE and GRACE Follow-On (GRACE-FO) are equal to 0.91 and 0.88, respectively; and the values of R-squared (R2) are equal to 0.76 and 0.65, respectively; the values of PCC and R2 between MLLIM and GLDAS solutions are equal to 0.79 and 0.65. Compared with the traditional GPS inversion, the MLLIM improves PCC and R2 by 8.85% and 7.99% on average, which indicates that the MLLIM can improve the accuracy of TWSA inversion more than the traditional GPS method. Third, this study applies the MLLIM to invert the TWSA in each province of southwest China and combines the precipitation to analyze the change of TWSA in each province. The results are as follows: (1) The spatial distribution of TWSA and precipitation is coincident, which is highlighted in southwest Yunnan and southeast Guangxi; (2) this study compares TWSA of MLLIM with GRACE and GLDAS solutions in each province, which indicates that the maximum value of PCC is as high as 0.86 and 0.94, respectively, which indicates the MLLIM can be used to invert the TWSA in the regions with sparse GPS stations. The TWSA based on the MLLIM can be used to fill the vacancy between GRACE and GRACE-FO.

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A time series assessment of terrestrial water storage and its relationship with hydro-meteorological factors in Gilgit-Baltistan region using GRACE observation and GLDAS-Noah model

SN Applied Sciences ◽

10.1007/s42452-021-04525-4 ◽

2021 ◽

Vol 3 (5) ◽

Author(s):

Dostdar Hussain ◽

Aftab Ahmed Khan ◽

Syed Najam Ul Hassan ◽

Syed Ali Asad Naqvi ◽

Akhtar Jamil

Keyword(s):

Snow Water Equivalent ◽

Gravity Data ◽

Water Storage ◽

Indus Basin ◽

Flood Hazards ◽

Terrestrial Water Storage ◽

Satellite Gravity ◽

Terrestrial Water ◽

Gilgit Baltistan

AbstractMountains regions like Gilgit-Baltistan (GB) province of Pakistan are solely dependent on seasonal snow and glacier melt. In Indus basin which forms in GB, there is a need to manage water in a sustainable way for the livelihood and economic activities of the downstream population. It is important to monitor water resources that include glaciers, snow-covered area, lakes, etc., besides traditional hydrological (point-based measurements by using the gauging station) and remote sensing-based studies (traditional satellite-based observations provide terrestrial water storage (TWS) change within few centimeters from the earth’s surface); the TWS anomalies (TWSA) for the GB region are not investigated. In this study, the TWSA in GB region is considered for the period of 13 years (from January 2003 to December 2016). Gravity Recovery and Climate Experiment (GRACE) level 2 monthly data from three processing centers, namely Centre for Space Research (CSR), German Research Center for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL), System Global Land Data Assimilation System (GLDAS)-driven Noah model, and in situ precipitation data from weather stations, were used for the study investigation. GRACE can help to forecast the possible trends of increasing or decreasing TWS with high accuracy as compared to the past studies, which do not use satellite gravity data. Our results indicate that TWS shows a decreasing trend estimated by GRACE (CSR, GFZ, and JPL) and GLDAS-Noah model, but the trend is not significant statistically. The annual amplitude of GLDAS-Noah is greater than GRACE signal. Mean monthly analysis of TWSA indicates that TWS reaches its maximum in April, while it reaches its minimum in October. Furthermore, Spearman’s rank correlation is determined between GRACE estimated TWS with precipitation, soil moisture (SM) and snow water equivalent (SWE). We also assess the factors, SM and SWE which are the most efficient parameters producing GRACE TWS signal in the study area. In future, our results with the support of more in situ data can be helpful for conservation of natural resources and to manage flood hazards, droughts, and water distribution for the mountain regions.

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Opportunities for Gravity and GNSS Surveying to Monitor Changes in the Terrestrial Water Storage in the Congo River Basin

Remote Sensing in Earth Systems Sciences ◽

10.1007/s41976-021-00049-y ◽

2021 ◽

Author(s):

Donghwan Kim

Keyword(s):

River Basin ◽

Water Storage ◽

Terrestrial Water Storage ◽

Congo River ◽

Congo River Basin ◽

Terrestrial Water

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Evaluation of hydrological and cryospheric angular momentum estimates based on GRACE, GRACE-FO and SLR data for their contributions to polar motion excitation

Earth Planets and Space ◽

10.1186/s40623-021-01393-5 ◽

2021 ◽

Vol 73 (1) ◽

Author(s):

Justyna Śliwińska ◽

Jolanta Nastula ◽

Małgorzata Wińska

Keyword(s):

Angular Momentum ◽

Spherical Harmonic ◽

Polar Motion ◽

Spectral Band ◽

Water Storage ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

High Consistency ◽

Polar Motion Excitation

AbstractIn geodesy, a key application of data from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and Satellite Laser Ranging (SLR) is an interpretation of changes in polar motion excitation due to variations in the Earth’s surficial fluids, especially in the continental water, snow, and ice. Such impacts are usually examined by computing hydrological and cryospheric polar motion excitation (hydrological and cryospheric angular momentum, HAM/CAM). Three types of GRACE and GRACE-FO data can be used to determine HAM/CAM, namely degree-2 order-1 spherical harmonic coefficients of geopotential, gridded terrestrial water storage anomalies computed from spherical harmonic coefficients, and terrestrial water storage anomalies obtained from mascon solutions. This study compares HAM/CAM computed from these three kinds of gravimetric data. A comparison of GRACE-based excitation series with HAM/CAM obtained from SLR is also provided. A validation of different HAM/CAM estimates is conducted here using the so-called geodetic residual time series (GAO), which describes the hydrological and cryospheric signal in the observed polar motion excitation. Our analysis of GRACE mission data indicates that the use of mascon solutions provides higher consistency between HAM/CAM and GAO than the use of other datasets, especially in the seasonal spectral band. These conclusions are confirmed by the results obtained for data from first 2 years of GRACE-FO. Overall, after 2 years from the start of GRACE-FO, the high consistency between HAM/CAM and GAO that was achieved during the best GRACE period has not yet been repeated. However, it should be remembered that with the systematic appearance of subsequent GRACE-FO observations, this quality can be expected to increase. SLR data can be used for determination of HAM/CAM to fill the one-year-long data gap between the end of GRACE and the start of the GRACE-FO mission. In addition, SLR series could be particularly useful in determination of HAM/CAM in the non-seasonal spectral band. Despite its low seasonal amplitudes, SLR-based HAM/CAM provides high phase consistency with GAO for annual and semiannual oscillation.

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