Long-term temporal prediction of terrestrial water storage changes over global basins using GRACE and limited GRACE-FO data

Ahi, Gonca Okay; Çekim, Hatice Öncel

doi:10.1007/s40328-021-00338-4

Cited by 9 publications

(12 citation statements)

References 96 publications

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“…TWS was projected for four RCP scenarios in this study. Only a few studies have been conducted in recent years for the future projection and prediction of TWS in different regions of the globe [69][70][71]. Jia et al [69] used the MME of GCMs for the projection of TWS in China until 2050 for two RCP scenarios, RCP 2.6 and RCP 6.0.…”

Section: Discussionmentioning

confidence: 99%

Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

2021

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This study projects water availability and sustainability in Nigeria due to climate change. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data (TWS), Global Precipitation Climatology Center (GPCC) precipitation data and Climate Research Unit (CRU) temperature data. Four general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 were downscaled using the best of four downscaling methods. Two machine learning (ML) models, RF and SVM, were developed to simulate GRACE TWS data for the period 2002–2016 and were then used for the projection of spatiotemporal changes in TWS. The projected TWS data were used to assess the spatiotemporal changes in water availability and sustainability based on the reliability–resiliency–vulnerability (RRV) concept. This study revealed that linear scaling was the best for downscaling over Nigeria. RF had better performance than SVM in modeling TWS for the study area. This study also revealed there would be decreases in water storage during the wet season (June–September) and increases in the dry season (January–May). Decreases in projected water availability were in the range of 0–12 mm for the periods 2010–2039, 2040–2069, and 2070–2099 under RCP2.6 and in the range of 0–17 mm under RCP8.5 during the wet season. Spatially, annual changes in water storage are expected to increase in the northern part and decrease in the south, particularly in the country’s southeast. Groundwater sustainability was higher during the period 2070–2099 under all RCPs compared to the other periods and this can be attributed to the expected increases in rainfall during this period.

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Section: Discussionmentioning

confidence: 99%

Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

2021

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“…Moreover, the grid will be defined as an unobserved grid if it does not contain GNSS stations, and the vertical deformation will be simulated in step III. (2) Step II: The GNSS coordinate solution will be calculated by using observation, precision ephemeris, navigation, and table files based on GAMIT software [42]. The daily coordinates are calculated by the GLOBK software based on baseline data files (h-files), and series outliers and step terms that are three times larger than the standard deviation are removed.…”

Section: Construction Of Dwlimmentioning

confidence: 99%

“…Terrestrial water storage (TWS) comprises all of the water stored on the crustal surface and underground, including snow, glaciers, soil water, groundwater, runoff, and biological water components, which is an essential part of the water cycle system [1,2]. However, the TWS is extraordinarily limited, only accounting for 3.47% of the total global water resources [3].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

et al. 2022

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Densely distributed Global Navigation Satellite System (GNSS) stations can invert the terrestrial water storage anomaly (TWSA) with high precision. However, the uneven distribution of GNSS stations greatly limits the application of TWSA inversion. The purpose of this study was to compensate for the spatial coverage of GNSS stations by simulating the vertical deformation in unobserved grids. First, a new deep learning weight loading inversion model (DWLIM) was constructed by combining the long short-term memory (LSTM) algorithm, inverse distance weight, and the crustal load model. DWLIM is beneficial for improving the inversion accuracy of TWSA based on the GNSS vertical displacement. Second, the DWLIM-based and traditional GNSS-derived TWSA methods were utilized to derive TWSA over mainland China. Furthermore, the TWSA results were compared with the TWSA solutions of the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) model. The results indicate that the maximum Pearson’s correlation coefficient (PCC), Nash–Sutcliffe efficiency (NSE) coefficient, and root mean square error (RMSE) equal 0.81, 0.61, and 2.18 cm, respectively. The accuracy of DWLIM was higher than that of the traditional GNSS inversion method according to PCC, NSE, and RMSE, which were increased by 67.11, 128.15, and 22.75%. The inversion strategy of DWLIM can effectively improve the accuracy of TWSA inversion in regions with unevenly distributed GNSS stations. Third, this study investigated the variation characteristics of TWSA based on DWLIM in 10 river basins over mainland China. The analysis shows that the TWSA amplitudes of Songhua and Liaohe River basins are significantly higher than those of the other basins. Moreover, TWSA sequences in each river basin contain annual seasonal signals, and the wave peaks of TWSA estimates emerge between June and July. Overall, DWLIM provides a useful measure to derive TWSA in regions where GNSS stations are uneven or sparse.

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“…To include physical information into TWS reconstruction, mathematical algorithms were either modified with meteorological parameters [14,15] or trained with SLR data [16], with only GRACE data [17], with GRACE and Swarm data [18], or with a combination of satellite data with meteorological/hydrological parameters [19,20], and were demonstrated to correlate well with GRACE [21,22]. The main advantage of mathematical approaches is that they are able to predict climate signals such as droughts [23] or floods [24] to reconstruct signals of the El Niño oscillation [22] or human-induced TWS changes [25] at any spatial resolution. However, observations also have their constraints (limited spatial or temporal resolution) by which they are unable to correctly reflect the aforementioned changes in water storage.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive Reconstruction of Total Water Storage within GRACE and GRACE Follow-On Gap Period

et al. 2022

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For 15 years, the Gravity Recovery and Climate Experiment (GRACE) mission have monitored total water storage (TWS) changes. The GRACE mission ended in October 2017, and 11 months later, the GRACE Follow-On (GRACE-FO) mission was launched in May 2018. Bridging the gap between both missions is essential to obtain continuous mass changes. To fill the gap, we propose a new approach based on a remove–restore technique combined with an autoregressive (AR) prediction. We first make use of the Global Land Data Assimilation System (GLDAS) hydrological model to remove climatology from GRACE/GRACE-FO data. Since the GLDAS mis-models real TWS changes for many regions around the world, we further use least-squares estimation (LSE) to remove remaining residual trends and annual and semi-annual oscillations. The missing 11 months of TWS values are then predicted forward and backward with an AR model. For the forward approach, we use the GRACE TWS values before the gap; for the backward approach, we use the GRACE-FO TWS values after the gap. The efficiency of forward–backward AR prediction is examined for the artificial gap of 11 months that we create in the GRACE TWS changes for the July 2008 to May 2009 period. We obtain average differences between predicted and observed GRACE values of at maximum 5 cm for 80% of areas, with the extreme values observed for the Amazon, Alaska, and South and Northern Asia. We demonstrate that forward–backward AR prediction is better than the standalone GLDAS hydrological model for more than 75% of continental areas. For the natural gap (July 2017–May 2018), the misclosures in backward–forward prediction estimated between forward- and backward-predicted values are equal to 10 cm. This represents an amount of 10–20% of the total TWS signal for 60% of areas. The regional analysis shows that the presented method is able to capture the occurrence of droughts or floods, but does not reflect their magnitudes. Results indicate that the presented remove–restore technique combined with AR prediction can be utilized to reliably predict TWS changes for regional analysis, but the removed climatology must be properly matched to the selected region.

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Long-term temporal prediction of terrestrial water storage changes over global basins using GRACE and limited GRACE-FO data

Cited by 9 publications

References 96 publications

Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

Improving the Inversion Accuracy of Terrestrial Water Storage Anomaly by Combining GNSS and LSTM Algorithm and Its Application in Mainland China

Autoregressive Reconstruction of Total Water Storage within GRACE and GRACE Follow-On Gap Period

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