Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the irrigated Indus basin.

Groundwater storage and depletion fluctuations in response to groundwater availability for irrigation require understanding on a local scale to ensure a reliable groundwater supply. However, the coarser spatial resolution and intermittent data gaps to estimate the regional groundwater storage anomalies (GWSA) prevent the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GARCE-FO) mission from being applied at the local scale. To enhance the resolution of GWSA measurements using machine learning approaches, numerous recent efforts have been made. With a focus on the development of a new algorithm, this study enhanced the GWSA resolution estimates to 0.05° by extensively investigating the continuous spatiotemporal variations of GWSA based on the regional downscaling approach using a regression algorithm known as the geographically weighted regression model (GWR). First, the modified seasonal decomposition LOESS method (STL) was used to estimate the continuous terrestrial water storage anomaly (TWSA). Secondly, to separate GWSA from TWSA, a water balance equation was used. Third, the continuous GWSA was downscaled to 0.05° based on the GWR model. Finally, spatio-temporal properties of downscaled GWSA were investigated in the North China Plain (NCP), China's fastest-urbanizing area, from 2003 to 2022. The results of the downscaled GWSA were spatially compatible with GRACE-derived GWSA. The downscaled GWSA results are validated (R = 0.83) using in-situ groundwater level data. The total loss of GWSA in cities of the NCP fluctuated between 2003 and 2022, with the largest loss seen in Handan (-15.21 ± 7.25 mm/yr), Xingtai (-14.98 ± 7.25 mm/yr), and Shijiazhuang (-14.58 ± 7.25 mm/yr). The irrigated winter-wheat farming strategy is linked to greater groundwater depletion in several cities of NCP (e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The study's high-resolution findings can help with understanding local groundwater depletion that takes agricultural water utilization and provide quantitative data for water management. 

The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain.

The study proposes a new mathematical method, referred to as spectral combination, to downscale Gravity Recovery And Climate Experiment (GRACE) observations. The goal is to improve the spatial resolution of GRACE from 1̊ to 0.25̊, based upon available hydrological variables. First, a new approach based upon condition adjustment is proposed to estimate uncertainties related to hydrological variables. Second, a spectral-spatial estimator is developed to derive downscaled Total Water Storage Anomalies (TWSA) by optimally combining GRACE models and hydrological variables. Last, groundwater storage anomalies (GWSA) are derived from the downscaled TWSA. The proposed spectral combination approach was tested over the Canadian Prairies by considering GRACE data and required Global Land Data Assimilation System (GLDAS) variables for February 2003 to December 2016. The results reveal greater details in TWSA after spatial downscaling. Quantitatively, retrieved downscaled GWSA were validated using 75 unconfined in situ piezometric wells that were distributed across the Province of Alberta. A correlation of 0.80, with an RMSE of 11 mm, was obtained after downscaling with all wells over the validation area. These results are better than those obtained before downscaling (correlation of 0.42, with an RMSE of 21.4 mm), demonstrating that the proposed approach is successful. 

New spectro-spatial downscaling approach for terrestrial and groundwater storage variations estimated by GRACE models

A sub-surface hydrogeological investigation conducted in the Gangetic Plain of India would involve studying the groundwater resources, aquifer characteristics, and overall hydrogeological conditions in the region. The Gangetic Plain is a vast alluvial plain formed by the sediments deposited by the Ganges and its tributaries, making it a significant groundwater (GW) bearing region, which is surrounded by a thick stratum of Unconsolidated Alluvial deposits and comprises rich aquifer systems. This study describes a procedure for estimation of the subsurface GW levels for pre-monsoon, 2021 which has been determined by JPL, RL05, temporal datasets of Gravity Recovery and Climate Experiment (GRACE), and Global Land Data Assimilation System (GLDAS). The water level observed ranged from 2.3 m to 13 m in the Bhabhar and Terai Belt, 14 to 23 m in the Central Alluvial Plain and Khadar Belt, and 24 to 31 m in the Marginal Alluvial Plain. Two types of variation are seen: Local and Geological variation. The GW storage justifies the remote sensing perspective and further validates our research study that regional total water storage mapping can be expeditiously and accurately cognizable by the GRACE/GLDAS mission. This method is used to estimate changes in regional GW, it is a very accurate and time-limited method rather than the traditional method of GW extraction. By using this technology, we can be able to know the discharge status of GW in the state and make policies for its over-exploitation. Drinking water can be conserved to a great extent and monitoring of GW can also be done in the time frame. And attention can be given to those districts where water is being exploited more. In view of the increasing impact of climate change on water resources, this modeling will prove vital for understanding future scenarios and planning sustainable management strategies that will aid in the development of adaptation and mitigation measures. 

Monitoring of the Groundwater Level using GRACE with GLDAS Satellite Data in Ganga Plain, India to Understand the Challenges of Groundwater, Depletion, Problems, and Strategies for Mitigation

GRACE observations and land subsidence data derived from InSAR both assess groundwater storage changes. However, GRACE data at local scales are restricted by the coarser spatial resolution of satellite systems, and inversion of Groundwater Storage Anomalies (GWSA) by InSAR requires extensive and unavailable lithological data. Here, we propose a New Subsidence Feature Weighted Combination (NSFWC) scheme to enhance the spatial resolution of GRACE-derived GWSA from 0.5° to 0.05°. This method can not only retain the spatial distribution of groundwater changes but also reflect local details related to surface subsidence. A case study was executed to evaluate the performance of the NSFWC scheme in the Beijing Plain, which has seriously overexploited groundwater. Results showed that the simulated GWSA were consistent with in situ measurements in most regions, with a correlation coefficient of 0.85 and an RMSE of 4.41 mm/year. Additionally, there were 22 overexploited wells in the Beijing Plain, although groundwater levels generally recovered after the South to North Water Diversion Project. Simultaneously, four cones of depression were detected by the InSAR technology, where the maximum cumulative subsidence and subsidence rate achieved −198.52 mm and −53.09 mm/year, respectively. This paper provides data support and technical guarantees for small-scale groundwater resources management.

/pdf/improving-the-resolution-of-grace-insar-groundwater-storage-15kjpo52.pdf

Improving the Resolution of GRACE/InSAR Groundwater Storage Estimations Using a New Subsidence Feature Weighted Combination Scheme

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.

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

Obtaining high-resolution products that can accurately estimate the spatiotemporal changes in regional water resources is essential for the rational utilization of water resources. The temporal gravity field model derived from the Gravity Recovery and Climate Experiment (GRACE) satellite can effectively monitor the regional terrestrial water storage (TWS) changes. Nevertheless, the main of GRACE products is their coarse spatial resolution, which limits their applicability to small-scale regions. Herein, we propose a geographically weighted regression downscaled model (GWRDM) to improve the spatial resolution of TWS from 0.5° to 0.1° and validate it against the downscaled results provided by the spatial global regression downscaled model (SGRDM) and temporal grid regression downscaled model (TGRDM). The results show that the downscaled products based on GWRDM outperform those based on SGRDM and TGRDM. The GWRDM not only effectively improves the spatial resolution of GRACE products but also maintains the intensity of the original signal. Similarly, the GWRDM is further used to downscale groundwater storage (GWS) and validated against the in situ observations. The downscaled GWS based on GWRDM agrees well with in situ observations. On the monthly scale, the average correlation coefficient (CC) for both is 0.53 and above 0.70 for some in situ observations. On the annual scale, 81.25% and 37.50% of the in situ observations have CC values larger than 0.60 and 0.90, respectively, which further verifies the reliability of GWRDM. Consequently, the GWRDM provides a practical algorithm for obtaining high-resolution water storage estimates, which is expected to provide a reference for small-scale regional water resources management.

https://doi.org/10.1109/jstars.2023.3272916

Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model

Plasma sheath poses a serious challenge to inverse synthetic aperture radar (ISAR) imaging of hypersonic targets. This paper investigated the distribution characteristics of the electron density and velocity field in the plasma sheath surrounding the hypersonic target in various flight scenes. The incident depth and reflective surface of electromagnetic (EM) waves with X-band, Ku-band, and Ka-band can be determined based on the plasma frequency. We established the echo model coupled with the velocity field of the plasma sheath on the reflective surface and obtained one-dimensional range profiles and ISAR images of the hypersonic target in various flight scenes. The simulation results indicated that the non-uniform velocity field on the reflective surface induced displacement and diffusion in the one-dimensional range profile, resulting in ISAR image distortion. A changing flight scene and radar frequency can have an impact on imaging results. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) were utilized to assess the impact of plasma sheath on ISAR images. This study revealed the defocus mechanism of the ISAR image caused by the velocity field of the plasma sheath and provided a theoretical reference for the selection of radar frequency for hypersonic targets in various flight scenes.

/pdf/influence-of-plasma-sheath-s-velocity-field-on-isar-imaging-e7juwjz4.pdf

Influence of Plasma Sheath's Velocity Field on ISAR Imaging of Hypersonic Target

The bistatic global navigation satellite system’s (GNSS) signal reflection technology has become an effective means of space-based sea surface wind field retrieval and height retrieval. By adopting a wider signal bandwidth, a higher pulse-limited footprint resolution can be achieved. However, for the GNSS-Reflectometry (GNSS-R) system, its signal bandwidth is affected by the signal bandwidth of the GNSS satellite, which limits the further improvement of the pulse-limited footprint resolution. This article proposes a method based on the novel signal bandwidth joint principle to improve the resolution of GNSS-R pulse-limited footprints. Firstly, currently in-orbit GNSS-R satellites use the traditional single frequency band (TSFB) method, which is limited by the GNSS satellite’s signals and has a theoretical upper limit on its signal bandwidth. In response to this issue, this article proposes the novel joint bandwidth (NJBW) method (Galileo E5a and E5b signals) based on the auto-correlation function (ACF) signal ambiguity theory. The NJBW method reduces the main lobe width of the ACF of the GNSS-R signal by jointly processing the signals of E5a and E5b frequency bands, thus improving the pulse-limit footprint resolution of GNSS-R. Secondly, in order to verify the improvement effect of the novel joint bandwidth method on the pulse-limited footprint resolution of GNSS-R, this paper designs and fabricates an NJBW antenna verification prototype for the joint Galileo E5a and E5b frequency band and tests it in a microwave anechoic chamber. The test results indicate that the radio frequency (RF) bandwidth of the NJBW antenna validation prototype can cover both the frequency bands of E5a and E5b, making it suitable for use as the NJBW method for the GNSS-R receiving antenna. The bandwidth test values of the NJBW antenna validation prototype are consistent with the design values, which verifies the correctness of the NJBW antenna design model and further proves the feasibility of the NJBW method. Thirdly, based on the joint Galileo E5a and E5b frequency band signals, the NJBW method was applied to analyze the improvement effect of the pulse-limited footprint resolution. Compared to the TSFB method, the application of the NJBW method can increase the resolution of the GNSS-R pulse-limiting footprint by 1.73 times, which effectively improves the performance of the GNSS-R system. The NJBW method proposed in this article provides the theoretical method foundation and key technical support for sea surface wind field retrieval and height retrieval and the antenna design for the future high-precision and high pulse-limited footprint resolution GNSS-R sea surface wind field retrieval and height retrieval verification satellite. 

Improving the Pulse-Limited Footprint Resolution of GNSS-R Based on the Novel Joint Bandwidth Method

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Papers

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

Improving Spatial Resolution of GRACE-Derived Water Storage Changes Based on Geographically Weighted Regression Downscaled Model

Influence of Plasma Sheath's Velocity Field on ISAR Imaging of Hypersonic Target

Improving the Pulse-Limited Footprint Resolution of GNSS-R Based on the Novel Joint Bandwidth Method