No abstract

Temporal gaps within the Gravity Recovery and Climate Experiment (GRACE) (gap: 20 months), between GRACE and GRACE Follow-On (GRACE-FO) missions (gap: 11 months), and within GRACE-FO record (gap: 2 months) make it difficult to analyze and interpret spatiotemporal variability in GRACE- and GRACE-FO-derived terrestrial water storage (TWSGRACE) time series. In this study, an overview of data and approaches used to fill these gaps and reconstruct the TWSGRACE record at the global scale is provided. In addition, the study provides an innovative approach that integrates three machine learning techniques (deep-learning neural networks [DNN], generalized linear model [GLM], and gradient boosting machine [GBM]) and eight climatic and hydrological input variables to fill these gaps and reconstruct the TWSGRACE data record at both global grid and basin scales. For each basin and grid cell, the model performance was assessed using Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (CC), and normalized root-mean-square error (NRMSE), a leader model was selected based on the model performance, and variables that significantly control leader model outputs were defined. Results indicate that (1) the leader model reconstructed the TWSGRACE with high accuracy over both grid and local scales, particularly in wet and low anthropogenically active regions (grid scale: NSE = 0.65 ± 0.20, CC = 0.81 ± 0.13, and NSE = 0.56 ± 0.16; basin scale: NSE = 0.78 ± 0.14, CC = 0.89 ± 0.07, and NRMSE = 0.43 ± 0.14); (2) no single model was flawless in reconstructing the TWSGRACE over all grids or basins, so a combination of models is necessary; (3) basin-scale models outperform grid-scale models; (4) the DNN model outperforms both GLM and GBM at the basin scale, whereas the GBM outperforms at the grid scale; (5) among other inputs, the Global Land Data Assimilation System (GLDAS)-derived TWS controls the model performance on both basin and grid scales; and (6) the reconstructed TWSGRACE data captured extreme climatic events over the investigated basins and grid cells. The developed approach is robust, effective, and could be used to accurately reconstruct TWSGRACE for any hydrologic system across the globe.

With the increasing vulnerability of groundwater resources, especially in coastal regions, there is a growing need to monitor changes in groundwater storage (GWS). Estimations of GWS have been conducted extensively at regional to global scales using GRACE and GRACE-FO observations. The major goal of this study was to evaluate the applicability of uninterrupted monthly GRACE-derived terrestrial water storage (TWSGRACE) records in facilitating detection of long- and short-term hydroclimatic events affecting the GWS in a coastal area. The TWSGRACE data gap was filled with reconstructed values from multi-linear regression (MLR) and artificial neural network (ANN) models and used to estimate changes in GWS in the Texas coastal region (Gulf Coast and Carrizo–Wilcox Aquifers) between 2002 and 2019. The reconstructed TWSGRACE, along with soil moisture storage (SMS) from land surface models (LSMs), and surface water storage (SWS) were used to estimate the GRACE-derived GWS (GWSGRACE), validated against the GWS estimated from groundwater level observations (GWSwell) and extreme hydroclimatic event records. The results of this study show: (1) Good agreement between the predicted TWSGRACE data gaps from the MLR and ANN models with high accuracy of predictions; (2) good agreement between the GWSGRACE and GWSwell records (CC = 0.56, p-value < 0.01) for the 2011–2019 period for which continuous GWLwell data exists, thus validating the approach and increasing confidence in using the reconstructed TWSGRACE data to monitor coastal GWS; (3) a significant decline in the coastal GWSGRACE, at a rate of 0.35 ± 0.078 km3·yr−1 (p-value < 0.01), for the 2002–2019 period; and (4) the reliable applicability of GWSGRACE records in detecting multi-year drought and wet periods with good accuracy: Two drought periods were identified between 2005–2006 and 2010–2015, with significant respective depletion rates of −8.9 ± 0.95 km3·yr−1 and −2.67 ± 0.44 km3·yr−1 and one wet period between 2007 and 2010 with a significant increasing rate of 2.6 ± 0.63 km3·yr−1. Thus, this study provides a reliable approach to examine the long- and short-term trends in GWS in response to changing climate conditions with significant implications for water management practices and improved decision-making capabilities.

Nitrogen dioxide (NO 2 ) is one of the six "criteria" air pollutants regulated by the United States Environmental Agency (EPA) (US EPA, 2017). It is a toxic gas and a marker of pollutants created by fuel combustion. Nitrogen oxides (NOₓ = nitric oxide (NO) + NO 2 ) alter the hydroxyl radical (OH) concentration, control the formation of ozone (O 3 ) (Bradshaw et al., 2000), and contribute to the atmospheric aerosol formation (Crutzen, 1979;Lelieveld et al., 2004), thereby impacting air quality and the earth's radiation budget in the troposphere. NO x is emitted by anthropogenic activities and natural processes (

&lt;p&gt;Terrestrial water storage (TWS) data derived from past Gravity Recovery and Climate Experiment (GRACE; April 2002&amp;#8211;June 2017) and current GRACE-Follow On (GRACE-FO; June 2018&amp;#8211;present) missions provide insights into mass transport within, and between, different Earth&amp;#8217;s systems (e.g., atmosphere, oceans, groundwater, and ice sheets). However, there are currently temporal gaps within GRACE-derived TWS record (20 months) and between GRACE and GRACE-FO missions (11 months), within GRACE-FO-derived TWS record (2 months), and similar gaps could be experienced between GRACE-FO and GRACE-II missions. In this study, we compare the performance of different data-driven techniques in filling TWS gaps for 62 global watersheds. Additionally, these techniques are being applied to reconstruct TWS globally on a grid scale (1&amp;#176; &amp;#215; 1&amp;#176;). We used artificial neural networks (ANNs), support vector machines (SVMs), and multiple linear regression (MLR) models to predict TWS data (04/2002 &amp;#8211; 03/2020) based on the knowledge of relevant climatic datasets such as rainfall, temperature, evapotranspiration, vegetation indices, climate indices. The performance of the developed models was evaluated using several standard measures such as the root mean square error (RMSE), correlation coefficient (R), and Nash-Sutcliff efficiency coefficient (NSE). Our preliminary results indicate: (1) ANN models show higher performance over the examined watersheds compared to the other models (RMSE: 5.20; R: 0.93; NSE: 0.88), (2) the performances of ANN, MLR, and SVM models depend mainly on the nature of factors that control TWS in each of the examined hydrologic systems, and (3) higher model performance is achieved when the model input data were further spectrally decomposed. Results of our research could be used to validate GRACE-FO datasets. Our research will promote additional and improved use of GRACE products by the scientific community, end-users, and decision makers by providing a continuous uninterrupted TWS record from GRACE and GRACE-FO missions.&lt;/p&gt;