Amélioration et désagrégation des données GRACE et GRACE-FO pour l’estimation des variations de stock d’eau terrestre et d’eau souterraine à fine échelle

Abstract : Groundwater is an essential natural resource for domestic, industrial and agricultural uses worldwide. Unfortunately, climate change, excess withdrawal, population growth and other human impacts can affect its dynamics and availability. These excessive demands can lead to lower groundwater levels and depletion of aquifers, and potentially to increased water scarcity. Despite the abundance of lakes and rivers in many parts of Canada, the potential depletion of groundwater remains a major concern, particularly in the southern Prairie. Groundwater is traditionally monitored through in-situ piezometric wells, which are scarcely distributed in Canada and many parts of the world. Consequently, its quantities, distribution and availability are not well known, both spatially and temporally. Fortunately, the launch of the twin satellite systems of Gravity Recovery And Climate Experiment (GRACE) in 2002 and its successor, GRACE Follow-On in 2018 (GRACE-FO) opened up new ways to study groundwater changes. These platforms measure the variations of the Earth's gravity field, which in turn can be related to terrestrial water storage (TWS). The main objective of this thesis is to improve the estimation and spatial resolution of TWS and related groundwater storage changes (GWS), using GRACE and GRACE-FO data. This challenge was addressed through four specific objectives, where original approaches were developed in each case. The first objective was to understand and better take into account the uncertainties associated with the hydrological models (the Global Land Data Assimilation System (GLDAS), and the Water Global Assessment Prognosis hydrological model (WGHM)), generally used in the processing of GRACE or GRACE-FO data. The thesis proposes a new approach based on the Gauss-Markov model to estimate the optimal hydrological parameters from GLDAS, considering six different surface schemes. The Förstner estimator and the best quadratic unbiased estimator of the variance components were used with a least-squares method to estimate the optimal hydrological parameters and their errors. The comparison of the optimal TWS derived from GLDAS to the TWS derived from WGHM showed a very significant correlation of r = 0.91. The correlation obtained with GRACE was r = 0.71, which increased to r = 0.81 when the groundwater component was removed from GRACE. Compared to WGHM and GRACE, the optimal TWS calculated from GLDAS had much smaller errors (RMSE = 7 to 8.5 mm) than those obtained when individual surface schemes are considered (RMSE = 10 to 21 mm); demonstrating the performance of the proposed approach. The second specific objective was to understand regional variations in TWS and their uncertainties. The approach was applied over the Canadian landmass. To achieve the goal, the thesis proposes a new modeling of glacial isostatic adjustment uplift (GIA) in Canada. The comparison of the results of the proposed model and three other existing models with data from 149 very high precision GPS stations demonstrated its superiority in the region considered. The regional approach proposed was then used to extract TWS by correcting the effects of the GIA and leakage. The analyzes showed patterns of significant seasonal variations in TWS, with values ranging between -160 mm and 80 mm. Overall TWS showed a positive slope of temporal variations over the Canadian landmass (+ 6.6 mm/year) with GRACE and GRACE-FO combined. The slope reached up to 45 mm/year in the Hudson Bay region. The third objective was to extract GWS component using a comprehensive rigorous approach to reconstruct, refine and map the variations of GWS and its associated uncertainties. The approach used the methods proposed in the two previous objectives. Moreover, a new filtering approach called Gaussian-Han-Fan (GHF) was developed and integrated into the process in order to have a more robust procedure for extracting information from GRACE and GRACE-FO data. The performance and merits of the proposed filter compared to previous filters were analyzed. Then, the groundwater signal was reconstructed by taking into account all the other components, including surface water variations (estimated using satellite altimetry data). The results showed that the average variations of GWS are between -200 mm and +230 mm in the Canadian Prairies. The maximum and minimum GWS trends were found around the Hudson Bay region (approximately 55 mm/year) and southern Prairies (approximately -20 mm/year), respectively. The error on GWS was around 10% (about 19 mm). The estimated GWS changes were validated using the data from 116 in-situ wells. This validation showed a significant level of correlation (r > |0.70|, P |0.90|, P |0,70|, P |0,90|, P < 10-4, RMSE < 30 mm). Enfin, le dernier objectif consistait à améliorer la résolution spatiale des résultats extraits des données GRACE de 1° à 0.25°. Ainsi, une nouvelle approche basée sur l'ajustement des conditions a d’abord été proposée pour estimer les paramètres hydrologiques optimaux et leurs erreurs. Elle est légèrement différente de la méthode proposée dans le premier objectif. Ensuite, les corrections requises pour extraire les anomalies de TWS et ses incertitudes de manière rigoureuse ont été effectuées suivant la méthodologie présentée à l’objectif 3. Par la suite une nouvelle méthode basée sur la combinaison spectrale-spatiale a été développée pour dériver les anomalies de TWS à échelle réduite (0.25°), en combinant de manière optimale les modèles GRACE et les paramètres hydrologiques. Enfin, les anomalies d’eau souterraines ont été dérivées en utilisant les anomalies de TWS estimées. Les validations ont été faites à partir des données de 75 puits en aquifère non confiné en Alberta. Elles démontrent le potentiel de l’approche proposée avec une corrélation très significative de = 0.80 et un RMSE de 11 mm. Ainsi, la recherche proposée dans la thèse a permis de faire des avancées importantes dans l’extraction d’information sur le stockage total d’eau et les eaux souterraines à partir des données des satellites gravimétriques GRACE et GRACE-FO. Elle propose et valide plusieurs nouvelles approches originales en s’appuyant sur des données in-situ. Elle ouvre également plusieurs nouvelles avenues de recherche, qui permettront de faciliter une utilisation plus opérationnelle de ces types de données à l’échelle régionale, voire locale

Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Floridan Aquifer, Georgia, USA

Accurate assessments of groundwater resources in major aquifers across the globe are crucial for sustainable management of freshwater reservoirs. Observations from the Gravity Recovery and Climate Experiment (GRACE) satellite have become invaluable as a means to identify regions groundwater change. While there is a large body of research that focuses on downscaling coarse (1&deg;) GRACE products, few studies have attempted to spatially downscale GRACE to produce fine resolution (5 km) maps that are more useful to resource managers. This study trained a boosted regression tree model to statistically downscale GRACE total water storage anomaly to monthly 5 km groundwater level anomaly maps in the karstic upper Floridan aquifer (UFA) using multiple hydrologic datasets. Evaluation of spatial predictions with existing groundwater wells indicated satisfactory performance (R = 0.79, NSE = 0.61). Results demonstrate that groundwater levels were stable between 2002&ndash;2016 but varied seasonally. The data also highlights areas where groundwater pumping is exacerbating UFA water-level declines. While results demonstrate the applicability of machine learning based methods for spatial downscaling of GRACE data, future studies should account for preferential flowpaths (i.e., conduits, lineaments) in karstic systems