Towards a 3D hydrodynamic characterization from the joint analysis and blending of multiplatform observations for potential marine applications in the southeastern Bay of Biscay

277 p.La necesidad de un mayor conocimiento y una gestión sostenible de las áreas costeras ha suscitado la instalación de observatorios que monitorizan su estado. A pesar de que la información aportada por estos observatorios es esencial la compleja hidrodinámica de estas áreas dificulta una completa caracterización de las mismas. Además, la cobertura espacial de las observaciones es, en general, relativamente escasa especialmente en la columna de agua. Por tanto, el objetivo de esta tesis es combinar los datos disponibles de diferentes plataformas de observación en el sureste del Golfo de Bizkaia proporcionados por el sistema de oceanografía operacional de la costa vasca (EuskOOS) y también por fuentes externas para caracterizar en 3D la hidrodinámica de la zona. Para ello se han analizado conjuntamente las diferentes observaciones disponibles y se han utilizado métodos de reconstrucción de datos que permiten expandir dichas observaciones en 3D. Las observaciones conjuntas permiten detectar los principales procesos hidrodinámicos como los remolinos o la corriente de talud. Por otro lado, se observa que el usode los métodos de reconstrucción evaluados es factible en el área, especialmente el de la interpolación óptima de orden reducido (ROOI). Las observaciones y las corrientes reconstruidas por el ROOI han permitido caracterizar un remolino en 3D en el área de estudio por primera vez. Además, los campos de corrientes reconstruidos han posibilitado simular la advección superficial y subsuperficial de huevos y larvas de anchoa en la zona, mostrando el potencial del ROOI para aplicaciones marinas

Advanced Data Mining and Machine Learning Algorithms for Integrated Computer-Based Analyses of Big Environmental Databases

Einsicht in die räumliche Verteilung geotechnischer und hydrologischer Untergrundeigenschaften sowie von Reservoir- und Umweltparametern sind grundlegend für geowissenschaftliche Forschungen. Entwicklungen in den Bereichen geophysikalische Erkundung sowie Fernerkundung resultieren in der Verfügbarkeit verschiedenster Verfahren für die nichtinvasive, räumlich kontinuierliche Datenerfassung im Rahmen hochauflösender Messverfahren. In dieser Arbeit habe ich verschiedene Verfahren für die Analyse erdwissenschaftlicher Datenbasen entwickelt auf der Basis von Wissenserschließungsverfahren. Eine wichtige Datenbasis stellt geophysikalische Tomographie dar, die als einziges geowissenschaftliches Erkundungsverfahren 2D und 3D Abbilder des Untergrunds liefern kann. Mittels unterschiedlicher Verfahren aus den Bereichen intelligente Datenanalyse und maschinelles Lernen (z.B. Merkmalsextraktion, künstliche neuronale Netzwerke, etc.) habe ich ein Verfahren zur Datenanalyse mittels künstlicher neuronaler Netzwerke entwickelt, das die räumlich kontinuierliche 2D oder 3D Vorhersage von lediglich an wenigen Punkten gemessenen Untergrundeigenschaften im Rahmen von Wahrscheinlichkeitsaussagen ermöglicht. Das Vorhersageverfahren basiert auf geophysikalischer Tomographie und berücksichtigt die Mehrdeutigkeit der tomographischen Bildgebung. Außerdem wird auch die Messunsicherheit bei der Erfassung der Untergrundeigenschaften an wenigen Punkten in der Vorhersage berücksichtigt. Des Weiteren habe ich untersucht, ob aus den Trainingsergebnissen künstlicher neuronaler Netzwerke bei der Vorhersage auch Aussagen über die Realitätsnähe mathematisch gleichwertiger Lösungen der geophysikalischen tomographischen Bildgebung abgeleitet werden können. Vorhersageverfahren wie das von mir vorgeschlagene, können maßgeblich zur verbesserten Lösung hydrologischer und geotechnischer Fragestellungen beitragen. Ein weiteres wichtiges Problem ist die Kartierung der Erdoberfläche, die von grundlegender Bedeutung für die Bearbeitung verschiedener ökonomischer und ökologischer Fragestellungen ist, wie z.B., die Identifizierung von Lagerstätten, den Schutz von Böden, oder Ökosystemmanagement. Kartierungsdaten resultieren entweder aus technischen (objektiven) Messungen oder visuellen (subjektiven) Untersuchungen durch erfahrene Experten. Im Rahmen dieser Arbeit zeige ich erste Entwicklungen hin zu einer automatisierten und schnellen Integration technischer und visueller (subjektiver) Daten auf der Basis unterschiedlicher intelligenter Datenanalyseverfahren (z.B., Graphenanalyse, automatische Konturerfassung, Clusteranalyse, etc.). Mit solchem Verfahren sollen hart oder weich klassifizierte Karten erstellt werden, die das Untersuchungsgebiet optimal segmentieren um höchstmögliche Konformität mit allen verfügbaren Daten zu erzielen

DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

peer reviewedA method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.MULTI-SYNC project (contract SR/00/359), Consortium des Équipements de Calcul Intensif (CÉCI), funded by the F.R.S.-FNRS under grant no. 2.5020.11, COST action ES1402 – “Evaluation of Ocean Syntheses

Spatial Analysis for Landscape Changes

Recent increasing trends of the occurrence of natural and anthropic processes have a strong impact on landscape modification, and there is a growing need for the implementation of effective instruments, tools, and approaches to understand and manage landscape changes. A great improvement in the availability of high-resolution DEMs, GIS tools, and algorithms of automatic extraction of landform features and change detections has favored an increase in the analysis of landscape changes, which became an essential instrument for the quantitative evaluation of landscape changes in many research fields. One of the most effective ways of investigating natural landscape changes is the geomorphological one, which benefits from recent advances in the development of digital elevation model (DEM) comparison software and algorithms, image change detection, and landscape evolution models. This Special Issue collects six papers concerning the application of traditional and innovative multidisciplinary methods in several application fields, such as geomorphology, urban and territorial systems, vegetation restoration, and soil science. The papers include multidisciplinary studies that highlight the usefulness of quantitative analyses of satellite images and UAV-based DEMs, the application of Landscape Evolution Models (LEMs) and automatic landform classification algorithms to solve multidisciplinary issues of landscape changes. A review article is also presented, dealing with the bibliometric analysis of the research topic

Estimating Ocean Surface Currents With Machine Learning

Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers. However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, but the low temporal sampling can potentially lead to aliasing. Applying these balances directly may lead to an incorrect un-physical estimate of the surface flow. In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities. We train our ML models with SSH, SST, and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions. In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman. Using a local stencil of neighboring grid points as additional input features, we can train the deep learning models to effectively “learn” spatial gradients and the physics of surface currents. By passing the stenciled variables through convolutional filters we can help the model learn spatial gradients much faster. Various training strategies are explored using systematic feature hold out and multiple combinations of point and stenciled input data fed through convolutional filters (2D/3D), to understand the effect of each input feature on the NN's ability to accurately represent surface flow. A model sensitivity analysis reveals that besides SSH, geographic information in some form is an essential ingredient required for making accurate predictions of surface currents with deep learning models

Smart Classifiers and Bayesian Inference for Evaluating River Sensitivity to Natural and Human Disturbances: A Data Science Approach

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing. An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and will become more critical under a nonstationary climate, as sediment yields are expected to increase in regions of the world that will experience increased frequency, persistence, and intensity of storm events. Practical tools are needed to predict sediment erosion, transport and deposition and to characterize sediment sources within a reasonable measure of uncertainty. Water resource scientists and engineers use multidimensional data sets of varying types and quality to answer management-related questions, and the temporal and spatial resolution of these data are growing exponentially with the advent of automated samplers and in situ sensors (i.e., “big data”). Data-driven statistics and classifiers have great utility for representing system complexity and can often be more readily implemented in an adaptive management context than process-based models. Parametric statistics are often of limited efficacy when applied to data of varying quality, mixed types (continuous, ordinal, nominal), censored or sparse data, or when model residuals do not conform to Gaussian distributions. Data-driven machine-learning algorithms and Bayesian statistics have advantages over Frequentist approaches for data reduction and visualization; they allow for non-normal distribution of residuals and greater robustness to outliers. This research applied machine-learning classifiers and Bayesian statistical techniques to multidimensional data sets to characterize sediment source and flux at basin, catchment, and reach scales. These data-driven tools enabled better understanding of: (1) basin-scale spatial variability in concentration-discharge patterns of instream suspended sediment and nutrients; (2) catchment-scale sourcing of suspended sediments; and (3) reach-scale sediment process domains. The developed tools have broad management application and provide insights into landscape drivers of channel dynamics and riverine solute and sediment export

Comparison of sea-ice freeboard distributions from aircraft data and cryosat-2

The only remote sensing technique capable of obtain- ing sea-ice thickness on basin-scale are satellite altime- ter missions, such as the 2010 launched CryoSat-2. It is equipped with a Ku-Band radar altimeter, which mea- sures the height of the ice surface above the sea level. This method requires highly accurate range measure- ments. During the CryoSat Validation Experiment (Cry- oVEx) 2011 in the Lincoln Sea, Cryosat-2 underpasses were accomplished with two aircraft, which carried an airborne laser-scanner, a radar altimeter and an electro- magnetic induction device for direct sea-ice thickness re- trieval. Both aircraft flew in close formation at the same time of a CryoSat-2 overpass. This is a study about the comparison of the sea-ice freeboard and thickness dis- tribution of airborne validation and CryoSat-2 measure- ments within the multi-year sea-ice region of the Lincoln Sea in spring, with respect to the penetration of the Ku- Band signal into the snow