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Towards unprecedented spatiotemporal observations in hydrological systems using Uncrewed Vehicles
L'amélioration de la gestion des ressources en eau a été un grand défi et l'utilisation appropriée de l'eau est d'une grande importance pour les écosystèmes et la population humaine dans le monde entier. Le changement climatique affecte le cycle de l'eau et donc l'utilisation de l'eau et les activités humaines dans le monde entier. Il est donc de plus en plus nécessaire de mieux surveiller les ressources en eau et de comprendre les processus hydrogéologiques qui se déroulent dans les systèmes hydrologiques et hydrogéologiques. La technologie de la télédétection a été intensivement utilisée pour surveiller les masses d'eau, mais jusqu'à présent principalement par le biais de satellites et d'avions pilotés. Cependant, des limites subsistent en termes de résolution des données et de manque de données in situ dans les zones isolées et difficilement accessibles. Il est donc nécessaire de disposer de technologies plus avancées permettant d'observer l'eau à des échelles spatiales et temporelles plus fines. Dans ce contexte, les drones, avec leur technologie autonome innovante et leurs résolutions spatio-temporelles élevées, ouvrent une nouvelle ère dans l'étude des systèmes hydrogéologiques. Les véhicules aériens sans pilote (UAV) ont été utilisés dans les études hydrogéologiques en raison de leur souplesse opérationnelle, notamment leur capacité à voler à basse altitude, dans des environnements difficiles et à tout moment, à faible coût. Cette souplesse permet de surmonter les faibles résolutions spatiales et temporelles des données satellitaires et les coûts élevés de l'acquisition de données par des avions pilotés. Outre les drones, les véhicules de surface sans pilote (USV) ont également été utilisés pour l'étude des océans en raison de leur autonomie et de leur navigation à longue distance. Leurs fréquences d'échantillonnage élevées et leur capacité à échantillonner directement la surface de l'océan et pas seulement quelques mètres en dessous, les rendent plus favorables que les plateformes de mesure in situ traditionnelles telles que les bouéesou d'autres bateaux habités. L'objectif de cette thèse de doctorat est d'améliorer la compréhension des processus hydrogéologiques en utilisant cette technologie innovante des véhicules sans pilote. En combinant diverses plateformes et données de télédétection, nous avons produit de nouveaux ensembles de données pour la communauté scientifique avec des résolutions spatio-temporelles sans précédent. Nos études, qui utilisent ces données transportées par des véhicules sans pilote avec des résolutions spatio-temporelles sans précédent, prouvent l'efficacité et le potentiel de la technologie des véhicules sans pilote. Nous menons quatre études différentes liées à différents processus hydrogéologiques dans différents environnements. La première étude est une contribution technique aux techniques de cartographie de la neige à l'aide de la technologie LiDAR embarquée sur drone. La deuxième étude, qui est la toute première étude menée sur un terrain forestier escarpé à l'aide d'un LiDAR embarqué sur un drone, démontre l'effet de la structure de la canopée et du rayonnement solaire sur la formation de motifs de neige dans les pentes forestières escarpées. Nous avons également obtenu des contributions techniques précieuses pour des campagnes similaires à venir. La troisième étude porte sur la dynamique de la température de surface de la mer sur la côte californienne et prouve que Saildrone, un bateau de surface sans équipage, est capable de valider les produits satellitaires grâce à sa fréquence d'échantillonnage d'une minute. Il s'agit de la première étude comparant les produits satellitaires MODIS niveau 2 et MUR (Multi-scale Ultrahigh Resolution) niveau 4 sur la côte californienne et de la première étude évaluant la précision des niveaux de qualité MODIS niveau 2 sur la côte californienne. La dernière étude vise à cartographier la matière organique de la couche arable à haute résolution en exploitant les propriétés spectrales du sol ainsi que les analyses traditionnelles en laboratoire. ABSTRACT The improvement of water resources management has been a big challenge and the appropriate use of water is of high importance for the ecosystems and the human population globally. Climate change affects the water cycle and therefore water use and human activities around the world. There is therefore an increased need for better monitoring of the water resources and the understanding of the hydrogeological processes that take place in hydrological and hydrogeological systems. Remote sensing technology has been intensively used to monitor water bodies, but so far mainly via satellites and crewed aircraft. However, there are still limitations in terms of data resolution and lack of in situ data in isolated and difficultly accessible areas. Therefore, there is a need for more advanced technologies that can observe the water at finer spatial and temporal scales. In this context, uncrewed vehicles with their innovative autonomous technology and their high spatiotemporal resolutions open a new era in the study of hydrogeological systems. Uncrewed aerial vehicles (UAVs) have been used in hydrogeological studies due to their operational flexibilities such as their ability to fly at low altitudes, in challenging environments, and whenever the user needs them at low costs. These flexibilities overcome the low spatial and temporal resolutions of the satellite data and the high costs of crewed aircraft data acquisitions. Besides the UAVs, uncrewed surface vehicles (USVs) have also been used in ocean studies due to their autonomous and long-range navigation. Their high sampling frequencies and their ability to directly sample the ocean surface and not just a few meters underneath, make them more favorable than traditional in situ measurement platforms such as buoys or other crewed boats. The aim of this Ph.D. thesis is to improve the understanding of hydrogeological processes using this innovative technology of uncrewed vehicles. By combining various remote sensing platforms and data, we produced new datasets for the scientific community with unprecedented spatiotemporal resolutions. Our studies, using these uncrewed vehicle-borne data with their unprecedented spatiotemporal resolutions, prove the efficiency and potential of the uncrewed vehicle technology. We pursue four different studies related to different hydrogeological processes in different environments. The first study is a technical contribution to snow mapping techniques using UAVborne LiDAR technology. The second study, which is the first-ever study conducted in steep forested terrain using UAV-borne LiDAR demonstrates the effect of canopy structure and solar radiation in the formation of snow patterns within the steep forested slopes. We also extract valuable technical contributions for similar future campaigns. The third study focuses on sea surface temperature dynamics on the California Coast and proves that Saildrone, an uncrewed surface boat, is capable of validating satellite products with its one-minute sampling frequency. It is the first study to compare MODIS level-2 with Multi-scale Ultra-high Resolution (MUR) level-4 satellite products over the California Coast and the first one to assess the accuracy of MODIS level-2 quality levels over the California Coast. The last study aims at mapping topsoil organic matter at high resolution by exploiting the soil spectral properties along with traditional laboratory analysis.
Using remote sensing to regionalize local precipitation recharge rates obtained from the Chloride Method
Water supply in semiarid Botswana is, to a large extent, based on groundwater. In the planning of a groundwater abstraction scheme, criteria for the sustainability of the abstraction with respect to both quantity and quality have to be satisfied. The most important parameter in the context of quantitative sustainability is the long-term average groundwater recharge together with its spatial distribution. A method is developed to calculate a recharge map that can be used in a groundwater model. The relative distribution of recharge is obtained from remotely sensed data and then calibrated with local values of recharge derived from the Chloride Method. The method was tested for two sites in Botswana, the Chobe Region and Ngamiland. (C) 2004 Elsevier B.V. All rights reserved.
Calibration of a groundwater model using pattern information from remote sensing data
Due to the chronic lack of verification data, hydrologic models are notoriously over-parameterized. If a large number of parameters are estimated, while few verification data are available, the calibrated model may have little predictive value. However, recent development in remote sensing (RS) techniques allows generation of spatially distributed data that can be used to construct and verify hydrological models. These additional data reduce the ambiguity of the calibration process and thus increase the predictive value of the model. An example for such remotely sensed data is the spatial distribution of phreatic evaporation. In this modeling approach, we use the spatial distribution of phreatic evaporation obtained by remote sensing images as verification data Compared to the usual limited amount of head data, the spatial distribution of evaporation data provides a complete areal coverage. However, the absolute values of the evaporation data are uncertain and therefore three ways of using the spatial distribution pattern of evaporation were tested and compared. The first way is to directly use the evaporation pattern defined in a relative manner by dividing the evaporation rate in a pixel by the total evaporation of a selected rectangular area of interest. Alternatively, the discrete fourier transform (DFT) or the discrete wavelet transform (DWT) are applied to the relative evaporation pattern in the space domain defined before. Seven different combinations of using hydraulic head data and/or evaporation pattern data as conditioning information have been tested. The code PEST, based on the least-squares method, was used as an automatic calibration tool. From the calibration results, we can conclude that the evaporation pattern can replace the head data in the model calibration process, independently of the way the evaporation pattern is introduced into the calibration procedure. (C) 2009 Elsevier B.V All rights reserved.
Uncertainty assessment and implications for data acquisition in support of integrated hydrologic models
The data set used for calibration of regional numerical models which simulate groundwater flow and vadose zone processes is often dominated by head observations. It is to be expected therefore, that parameters describing vadose zone processes are poorly constrained. A number of studies on small spatial scales explored how additional data types used in calibration constrain vadose zone parameters or reduce predictive uncertainty. However, available studies focused on subsets of observation types and did not jointly account for different measurement accuracies or different hydrologic conditions. In this study, parameter identifiability and predictive uncertainty are quantified in simulation of a 1-D vadose zone soil system driven by infiltration, evaporation and transpiration. The worth of different types of observation data (employed individually, in combination, and with different measurement accuracies) is evaluated by using a linear methodology and a nonlinear Pareto-based methodology under different hydrological conditions. Our main conclusions are (1) Linear analysis provides valuable information on comparative parameter and predictive uncertainty reduction accrued through acquisition of different data types. Its use can be supplemented by nonlinear methods. (2) Measurements of water table elevation can support future water table predictions, even if such measurements inform the individual parameters of vadose zone models to only a small degree. (3) The benefits of including ET and soil moisture observations in the calibration data set are heavily dependent on depth to groundwater. (4) Measurements of groundwater levels, measurements of vadose ET or soil moisture poorly constrain regional groundwater system forcing functions.
Inclusion of remote sensing information to improve groundwater flow modelling in the Chobe region (Botswana)
groundwater flow model has been built for the Chobe region in Botswana. Due to the scarcity of conventional data, alternative sources of information have been explored. METEOSAT and NOAA-AVHR images have been used to estimate the spatial distribution of the water balance. This spatial distribution has been correlated to local chloride measurements from which the recharge rate could be estimated. A digital elevation model has been used as a constraint for the maximum local piezometric head values. Finally, geomagnetic data were used as an indication for the presence of faults. These data have been used together with a limited number of traditional data (hydraulic head and transmissivity measurements) in an inverse calibration procedure. In the inverse conditioning, equally likely realizations, consistent with all the measurements (transmissivity, hydraulic head, digital elevation model, satellite images, chloride measurements and geomagnetic data) have been generated. The objective function contains an extra constraint on a statistical basis that guarantees that the calibrated recharge rate pattern does not deviate too much from the estimated water balance using the satellite image. The study demonstrates the importance of the digital elevation model and the satellite information in improving the groundwater model of the site.