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

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).Su, Z.; Zeng, Y.; Romano, N.; Manfreda, S.; Francés, F.; Ben Dor, E.; Szabó, B.... (2020). An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources. Water. 12(5):1-36. https://doi.org/10.3390/w12051495S13612

Integration of Satellite Data, Physically-based Model, and Deep Neural Networks for Historical Terrestrial Water Storage Reconstruction

Terrestrial water storage (TWS) is an essential part of the global water cycle. Long-term monitoring of observed and modeled TWS is fundamental to analyze droughts, floods, and other meteorological extreme events caused by the effects of climate change on the hydrological cycle. Over the past several decades, hydrologists have been applying physically-based global hydrological model (GHM) and land surface model (LSM) to simulate TWS and the water components (e.g., groundwater storage) composing TWS. However, the reliability of these physically-based models is often affected by uncertainties in climatic forcing data, model parameters, model structure, and mechanisms for physical process representations. Launched in March 2002, the Gravity Recovery and Climate Experiment (GRACE) satellite mission exclusively applies remote sensing techniques to measure the variations in TWS on a global scale. The mission length of GRACE, however, is too short to meet the requirements for analyzing long-term TWS. Therefore, lots of effort has been devoted to the reconstruction of GRACE-like TWS data during the pre-GRACE era. Data-driven methods, such as multilinear regression and machine learning, exhibit a great potential to improve TWS assessments by integrating GRACE observations and physically-based simulations. The advances in artificial intelligence enable adaptive learning of correlations between variables in complex spatiotemporal systems. As for GRACE reconstruction, the applicability of various deep learning techniques has not been well studied previously. Thus, in this study, three deep learning-based models are developed based on the LSM-simulated TWS, to reconstruct the historical TWS in the Canadian landmass from 1979 to 2002. The performance of the models is evaluated against the GRACE-observed TWS anomalies from 2002 to 2004, and 2014 to 2016. The trained models achieve a mean correlation coefficient of 0.96, with a mean RMSE of 53 mm. The results show that the LSM-based deep learning models significantly improve the match between original LSM simulations and GRACE observations

41st annual hydrology days (2021) - online proceedings

The 41st Annual AGU Hydrology Days event at Colorado State University was hosted online March 30-31, 2021.Includes the schedule and presentation abstracts only. The 41st Annual American Geophysical Union Hydrology Days meeting provides a unique opportunity for students, faculty, staff and practitioners to engage in wide range of water-related interdisciplinary research topics. Unfortunately, the global pandemic has left students with limited opportunities to share their research and satisfy graduation requirements. This year the spotlight focused on students to highlight the interconnections of water and linked systems. The 2021 Student Showcase provides an opportunity for students to exchange ideas, present their research findings and refine their science communication skills

Explaining National Trends in Terrestrial Water Storage

Access to fresh water is critical for human well-being, economic activity and, in some cases, political stability. Data from the Gravity Recovery and Climate Experiment (GRACE) has been used to monitor variability and trends in total water storage. This makes it possible to associate changes in water storage with both climate variability and large scale water management. Recent research has shown that these trends can be associated, globally, with rainfall, irrigation, and climate model predictions. This research indicates a need for further investigation into specific human predictors of trends in terrestrial water storage. This paper presents the first global scale analysis of GRACE trends focused on national scale socio-economic predictors of terrestrial water storage. We show that rainfall, irrigation, agricultural characteristics, and energy practices all contribute to GRACE trends, and the importance of each differs by country and region. Additionally, this work suggests that other factors such as GDP, population density, urbanization, and forest cover do not explain GRACE trends at a national level. Identifying these key predictors aids in understanding trends in water availability and for informing water management policy in a changing climate

Hydro-Climatic Changes and Corresponding Impacts on Agricultural Water Demand in the Ganges Delta of Bangladesh

The Ganges Delta in Bangladesh, a transboundary rural river basin, is an example of water-related calamities due to natural and human-induced stresses. It is an agriculture-dominated area with the presence of Sundarbans mangrove forest. Recently this area is facing unfavorable conditions due to limitations in quantity, quality, and timing of available freshwater. As a result, floods, droughts, water scarcity, stream depletion, salinity intrusion, excessive sedimentation are becoming common phenomena. These calamities are making this area unsuitable for agriculture and vulnerable to the Sundarbans’ ecosystem. This study aims to provide technical insight into issues related to water scarcity and projected agricultural water demand for 2020-2100 considering the climate change uncertainties. We addressed three critical areas to attain this purpose. As a first task, this study attempted to analyze and understand the observed hydrological changes over the past six decades to fathom the critical reasons for freshwater scarcity. Secondly, interdependency, availability, and accessibility of surface water and groundwater were analyzed to investigate the adequacy of current water demand and supply in agriculture, industrial and domestic sectors. Irrigation demand is much higher than others and occupies 93% of the total water demand. Similarly, irrigation is 96% of total water withdrawal. This high demand in the agriculture sector led to our next objective to estimate agricultural demand for this century. It helps to understand an overall agricultural water consumption scenario for the future. This study provides necessary background information, which is vital for hydro-economically feasible agricultural water management plans

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

The Sanjiang Plain (SJP), located at the confluence reaches of the Heilong, Songhua, and Wusuli Rivers in Northeast China. his study aimed to quantify the effects of varying climate and land-use/land-cover (LULC) dynamics on green water (GW) over the SJP during two distinctive periods (i.e., pre-2000 and post-2000), when synergetic effects of increased precipitation and temperature and rapid development of agriculture occurred. This assessment used the distributed eco-hydrological model ESSI-3. Multivariable and multi-objective calibration approaches (i.e., discharge, evapotranspiration, and terrestrial water storage anomaly) were used to ensure the high accuracies of the model outputs. New hydrological insights for the region: This research concluded that GW flow and GW storage in the SJP evidently increased after 2000 compared with before. Across the SJP, GW flow and GW storage responded differently to climate changes and LULC dynamics during pre-2000 and post-2000 period

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences