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

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

This study proposes a new mathematical approach to downscale monthly terrestrial water storage anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) and estimates groundwater storage anomalies (GWSA) at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, simultaneously. The method combines monthly 3° GRACE gravity models and daily 0.25° hydrological model outputs and their uncertainties in the spectral domain by minimizing the mean-square error (MSE) of their estimator to enhance the quality of both low and high frequency signals in the estimated TWSA and GWSA. The Global Land Data Assimilation System (GLDAS) was the hydrological model considered in this study. The estimator was tested over Alberta, Saskatchewan, and Manitoba (Canada), especially over the Province of Alberta, using data from 65 in-situ piezometric wells for 2003. Daily minimum and maximum GWS varied from 14 mm to 32 mm across the study area. A comparison of the estimated GWSA with the corresponding in-situ wells showed significant and consistent correlations in most cases, with r = 0.43–0.92 (mean r = 0.73). Correlations were >0.70 for approximately 70% of the wells, with root mean square errors <24 mm. These results provide evidence for using the proposed spectral combination estimator in downscaling GRACE data on a daily basis at a spatial scale of 0.25° × 0.25°.

https://www.mdpi.com/2072-4292/14/23/5991/pdf?version=1669719980

A New Spatiotemporal Estimator to Downscale GRACE Gravity Models for Terrestrial and Groundwater Storage Variations Estimation

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

Abstract Particle gluing operating parameters in particleboard (PB) production have an important influence on the mechanical properties of PBs. This study developed a multi-objective optimization model based on support vector regression (SVR) optimized by the non-dominated sorted genetic algorithm-II (NSGA2) to realize the multi-objective accurate prediction of PB mechanical properties (modulus of elasticity (MOE), modulus of rupture (MOR), and internal bonding (IB) strength) by adjusting particle gluing operating parameters. The NSGA2-SVR multi-objective prediction model was trained by 496 groups of experimental data of particle gluing operating parameters and PB mechanical properties. The prediction results of the NSGA2-SVR multi-objective prediction model were evaluated by 124 groups of experimental data and compared with the prediction results of the back propagation neural network (BPNN) model, general regression neural network (GRNN) model, and SVR model. The mean absolute percentage errors (MAPEs) of the NSGA2-SVR model were 49.11%, 33.64%, and 24.20% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The Theil’s inequality coefficients (TICs) of the NSGA2-SVR model were 40.93%, 27.39%, and 18.58% lower than that of the BPNN model, GRNN model, and SVR model, respectively. The results showed that the multi-objective prediction model based on NSGA2-SVR has a superior fitting and higher prediction accuracy for the prediction performance of particle gluing operating parameters, and the NSGA2-SVR model can be applied to the multi-objective synchronous prediction of particle gluing operating parameters in the PB production line. 

/pdf/multi-objective-optimization-of-particle-gluing-operating-2iagmxon.pdf

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

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

Zhiwei Chen

Papers

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