• Rural Digital Europe
• Other research productsclose
• 2014-2023close
• Open Access close
• NEANIAS Underwater Research Community close

The East Demerara Water Conservancy (EDWC) and east coast drainage and irrigation systems provide water storage and flood control mechanisms for Guyana's most populous region, including the capital city of Georgetown. In 2005, extreme rainfall caused devastang flooding along these coastal lowlands, with many areas remaining inundated for up to three weeks. The flood highlighted the vulnerability of the EDWC dam to overtopping and potential breaching. The Conservancy Adaptation Project (CAP) was conceived in the wake of the 2005 flood to help the Government of Guyana adapt to the threats posed by future climate change. The aim was to reduce the likelihood of catastrophic flooding along Guyana's low-lying coastal areas, also threatened by sea level rise. The project identified key investments totaling over US$ 123 million. These are being used by the Government to update the national master-plan strategy for drainage and irrigation and to plan future investment programs for reducing flood risk.

Estimating river discharge from in situ and/or remote sensing data is a key issue for evaluation of water balance at local and global scales and for water management. Variational data assimilation (DA) is a powerful approach used in operational weather and ocean forecasting, which can also be used in this context. A distinctive feature of the river discharge estimation problem is the likely presence of significant uncertainty in principal parameters of a hydraulic model, such as bathymetry and friction, which have to be included into the control vector alongside the discharge. However, the conventional variational DA method being used for solving such extended problems often fails. This happens because the control vector iterates (i.e., approximations arising in the course of minimization) result into hydraulic states not supported by the model. In this paper, we suggest a novel version of the variational DA method specially designed for solving estimation-under-uncertainty problems, which is based on the ideas of iterative regularization. The method is implemented with SIC2, which is a full Saint-Venant based 1D-network model. The SIC2 software is widely used by research, consultant and industrial communities for modeling river, irrigation canal, and drainage network behavior. The adjoint model required for variational DA is obtained by means of automatic differentiation. This is likely to be the first stable consistent adjoint of the 1D-network model of a commercial status in existence. The DA problems considered in this paper are offtake/tributary estimation under uncertainty in the cross-device parameters and inflow discharge estimation under uncertainty in the bathymetry defining parameters and the friction coefficient. Numerical tests have been designed to understand identifiability of discharge given uncertainty in bathymetry and friction. The developed methodology, and software seems useful in the context of the future Surface Water and Ocean Topography satellite mission.

Space-borne instruments can measure river water surface elevation, slope and width. Remote sensing of river discharge in ungauged basins is far more challenging, however. This work investigates the estimation of river discharge from simulated observations of the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission using a variant of the classical variational data assimilation method "4D-Var". The variational assimilation scheme simultaneously estimates discharge, river bathymetry and bed roughness in the context of a 1.5D full Saint Venant hydraulic model. Algorithms and procedures are developed to apply the method to fully ungauged basins. The method was tested on the Po and Sacramento Rivers. The SWOT hydrology simulator was used to produce synthetic SWOT observations at each overpass time by simulating the interaction of SWOT radar measurements with the river water surface and nearby land surface topography at a scale of approximately 1 m, thus accounting for layover, thermal noise and other effects. SWOT data products were synthesized by vectorizing the simulated radar returns, leading to height and width estimates at 200 m increments along the river centerlines. The ingestion of simulated SWOT data generally led to local improvements on prior bathymetry and roughness estimates which allowed the prediction of river discharge at the overpass times with relative root-mean-squared errors of 12.1% and 11.2% for the Po and Sacramento rivers respectively. Nevertheless, equifinality issues that arise from the simultaneous estimation of bed elevation and roughness may prevent their use for different applications, other than discharge estimation through the presented framework.

Seamounts are isolated elevations in the seafloor with circular or elliptical plans, comparatively steep slopes, and relatively small summit areas (Menard, 1964). The vertical gravity gradient (VGG), which is the curvature of the ocean surface topography derived from satellite altimeter measurements, has been used to map the global distribution of seamounts (Kim and Wessel, 2011). We used the latest grid of VGG to update and refine the global seamount catalog; we identified 10,796 new seamounts, expanding the catalog by 1/3. 739 well-surveyed seamounts, having heights ranging from 421 m to 2500 m, were then used to estimate the typical radially-symmetric seamount morphology. First, an Empirical Orthogonal Function (EOF) analysis was used to demonstrate that these small seamounts have a basal radius that is linearly related to their height – their shapes are scale invariant. Two methods were then used to compute this characteristic base to height ratio: an average Gaussian fit to the stack of all profiles and an individual Gaussian fit for each seamount in the sample. The first method combined the radial normalized height data from all 739 seamounts to form median and median-absolute deviation. These data were fitted by a 3-parameter Gaussian model that explained 99 percent of the variance. The second method used the Gaussian function to individually model each seamount in the sample and further establish the Gaussian model. Using this characteristic Gaussian shape, we show that VGG can be used to estimate the height of small seamounts to an accuracy of about 270 m.

High resolution remotely sensed bathymetric data is rapidly increasing in volume, but analyzing this data requires a mastery of a complex toolchain of disparate software, including computing derived measurements of the environment. Bathymetric gradients play a fundamental role in energy transport through the seascape. Benthic Terrain Modeler (BTM) uses bathymetric data to enable simple characterization of benthic biotic communities and geologic types, and produces a collection of key geomorphological variables known to affect marine ecosystems and processes. BTM has received continual improvements since its 2008 release; here we describe the tools and morphometrics BTM can produce, the research context which this enables, and we conclude with an example application using data from a protected reef in St. Croix, US Virgin Islands. Part of Special Issue of "Marine Geomorphometry" - http://www.mdpi.com/journal/geosciences/special_issues/marine_geomorphometry Refereed 14A Hard Coral Cover and Composition Fish Abundance and Distribution TRL 2 Technology concept and/or application formulated Best Practice

Coastal water mapping from remote-sensing hyperspectral data suffers from poor retrieval performance when the estimation parameters have little effect on subsurface reflectance, especially due to the ill-posed nature of the inversion problem. For example, depth cannot accurately be retrieved for deep water, where the bottom influence is negligible. Similarly, for very shallow water it is difficult to estimate the water quality because the subsurface reflectance is affected more by the bottom than by optically active water components. Most methods based on radiative transfer model inversion do not consider the distribution of targeted parameters within the inversion process, thereby implicitly assuming that any parameter value in the estimation range has the same probability. In order to improve the estimation accuracy for the above limiting cases, we propose to regularize the objective functions of two estimation methods (maximum likelihood or ML, and hyperspectral optimization process exemplar, or HOPE) by introducing local prior knowledge on the parameters of interest. To do so, loss functions are introduced into ML and HOPE objective functions in order to reduce the range of parameter estimation. These loss functions can be characterized either by using prior or expert knowledge, or by inferring this knowledge from the data (thus avoiding the use of additional information). This approach was tested both on simulated and real hyperspectral remote-sensing data. We show that the regularized objective functions are more peaked than their non-regularized counterparts when the parameter of interest has little effect on subsurface reflectance. As a result, the estimation accuracy of regularized methods is higher for these depth ranges. In particular, when evaluated on real data, these methods were able to estimate depths up to 20 m, while corresponding non-regularized methods were accurate only up to 13 m on average for the same data. This approach thus provides a solution to deal with such difficult estimation conditions. Furthermore, because no specific framework is needed, it can be extended to any estimation method that is based on iterative optimization.

This archive contains data, results and code related to a manuscript published with the journal 'Remote Sensing in Ecology and Conservation'. Please cite and refer to this publication when using this zenodo archive: How can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? DOI: 10.1002/rse2.246

During the past few years there was an increasing demands of needs for mapping the bottom of water, either because it was needed for Navigation Safety, Nautical charts, or for Pollution controlling, mineral and fish industries. Over the years the methods of bathymetry and mapping showed a huge improvement especially in the last 40 years where a rapid growth occurs in this part of science. Particular growth occurs in the bathymetry area using satellite data which continuously presented and exported new models in order to create maps in a shorter time period and with fewer expenses. Additionally there is an increasing improvement in the accuracy of the results of the maps over time and it is accomplished with smaller errors. For export of maps and finding data followed a fairly complicated process which needs to take into account many parameters are either located in the constituents of water, either the nature of the water bottom, or in the atmosphere and beyond. The method of remote sensing is divided into two main categories imagine methods and the Non imagine methods. Both are widely known, in conclusion, the methodology of remote sensing comparatively with the eco-sounding method is more efficient in a matter of time, financial budget, data accuracy than any other method exists, and usually is recommended for use. Πάντα υπήρχε η ανάγκη της χαρτογράφησης του πυθμένα του νερού, είτε ο λόγος αυτός αφορούσε την ασφαλή ναυσιπλοία είτε αφορούσε τον έλεγχο της στάθμης του νερού ή αφορούσε περιβαλλοντικούς λόγους. Κατά την πάροδο των χρόνων οι μέθοδοι της βυθομέτρησης και της χαρτογράφησης εξελίσονταν και βελτιώνονταν, ειδικά τα τελευταία 40 χρόνια όπου παρουσιάζεται μία ραγδαία ανάπτυξη στον τομέα αυτό. Ιδιαίτερη ανάπτυξη παρουσιάζεται στον τομέα της βυθομετρίας με την χρήση δορυφορικών δεδομένων όπου συνεχώς παρουσιάζονται νέοι τρόποι εξαγωγής χαρτών σε συντομότερο χρόνο και με λιγότερες δαπάνες. Επιπρόσθετα διακρίνεται μία συνεχής βελτίωση στην ακρίβεια των αποτελεσμάτων καθώς με τον καιρό επιτυγχάνεται η εξαγωγή αποτελεσμάτων με μεγαλύτερες ακρίβειες συνεπώς με μικρότερα σφάλματα. Για την εξαγωγή των χαρτών και την εύρεση των δεδομένων ακολουθείται μία αρκετά περίπλοκη διαδικασία όπου χρειάζεται να λαμβάνονται υπόψη πολλοί παραμέτροι είτε αυτοί βρίσκονται στα συστατικά του νερού, είτε στο είδος του πυθμένα του νερού, είτε στην ατμόσφαιρα και όχι μόνο. Η μέθοδος της τηλεπισκόπησης διακρίνεται σε δύο βασικές κατηγορίες η μέθοδος απικόνησης και η μέθοδος μη απικόνησης. Είναι και οι δύο ευρέως γνωστές. Εν κατακλείδι, η μέθοδολογία της τηλεπισκόπησης είναι πιο αποδοτική σε θέμα χρόνου, οικονομικού προυπολογισμού, ακρίβεια δεδομένων σε σχέση με οποιαδήποτε άλλη μέθοδο υπάρχει, και συνήθως είναι αυτή που συστήνεται για χρήση. Completed

/ Au cours de ce stage, j'ai développé de nouvelles méthodes d'estimation (d'inversion) de la bathymétrie et de la hauteur d'arbres à partir de formes d'onde LiDAR. Ces nouvelles méthodes statistiques, utilisant un apprentissage, tombent dans le domaine dit de l'analyse fonctionnelle. Ces méthodes réduisant le signal en un nombre réduit de paramètres qui décrivent ce signal et une régression statistique (modèle d'inversion) est ensuite effectuée entre ces paramètres et la variable d'intérêt (bathymétrie ou hauteur de canopée). Quatre méthodes de réduction de la dimension ont été testée : l'Analyse en Composantes Principales, la décomposition en ondelettes, l'approximation par splines cubique et l'approximation par BSplines. Trois méthodes de régression ont été testées : la régression multiple, CART et la méthode de forêt aléatoire (random forest). Les résultats de ces méthodes montrent que, pour des formes d'onde simulées, parfaitement contrôlées, on peut estimer la bathymétrie avec des précisions allant jusqu'à 3 cm pour des gammes de profondeur comprises entre 0 et 3 mètres. Les résultats de ces méthodes sur des formes d'onde simulées pour une eau claire et turbide sont équivalents. Les résultats des modèles d'inversion des formes d'onde bathymétriques réelles pour le Golfe du Morbihan avec deux types de capteurs (APD et PMT) sont également très proches. L'écart de précision entre les formes d'onde réelles et simulées s'explique par le fait que les paramètres du milieu sont fixés pour le cas des formes d'onde LiDAR simulées. En ce qui concerne l'estimation des hauteurs d'arbres, les résultats des modèles d'inversion des formes d'onde LiDAR atlimétriques (réelles) obtenues donnent une précision allant jusqu'à 2.35 mètres. Les modèles d'inversion utilisant les forêts aléatoires donne de meilleurs résultats que pour la régression linéaire. CART reste le modèle statistique le moins précis. Si on compare les différentes méthodes, on voit que les B-Splines donnent les meilleurs résultats. L'Analyse en Composantes Principales et l'analyse en ondelettes restent également un bon choix pour l'inversion des formes d'onde LiDAR. L'avantage de ces méthodes est qu'elles proposent des modèles statistiques avec peu de variables explicatives.