The architecture and prototype implementation of the Model Environment system

International audienceAn approach that simplifies software development of the model based decision support systems for environmental management has been introduced. The approach is based on definition and management of metadata and data related to computational model without losing data semantics and proposed methods of integration of the new modules into the information system and their management. An architecture of the integrated modelling system is presented. The proposed architecture has been implemented as a prototype of integrated modelling system using. NET/Gtk{#} and is currently being used to re-design European Decision Support System for Nuclear Emergency Management RODOS (http://www.rodos.fzk.de) using Java/Swing

Next Generation Hydro Software

A few years ago Deltares started a large multidisciplinary project named Next Generation Hydro Software. The main focus of the project is to improve, harmonize and integrate existing hydro software that has been developed throughout the years. Important technological innovations include development of the new computational core D-Flow Flexible Mesh, as well as the user-friendly, open modelling environment Delta Shell. The project involves more than 40 scientists and software engineers. The new integrated system will allow both water managers and modellers to do their work better and faster. The unique characteristic of the project is that it focuses on the possibility of setting up integrated models of the whole aquatic chain from the source to the sea, resulting in complex model configurations. The challenges further increase because of the involvement of experts from many different fields within the aforementioned aquatic chain. Furthermore, the project addresses the complete workflow of a modeller, including model setup, calibration and validation. For this purpose the system includes new scientific visualization, analysis and interactive modeling tools that enable users to improve their understanding of the modelled processes. Applications of the system show the successful integration of 0D (lumped hydrological models and real-time control rules), 1D (river flow and water quality models) and 2D/3D model components (river, estuary and coastal areas). In this paper some of the preliminary results of the project are demonstrated, as well as its current status and a preview of possible future developments

RivWidthCloud: An Automated Google Earth Engine Algorithm for River Width Extraction from Remotely Sensed Imagery

The wetted width of a river is one of the most important hydraulic parameters that can be readily measured using remote sensing. Remotely sensed river widths are used to estimate key attributes of river systems, including changes in their surface area, channel storage, and discharge. Although several published algorithms automate river network and width extraction from remote sensing images, they are limited by only being able to run on local computers and do not automatically manage cloudy images as input. Here we present RivWidthCloud, a river width software package developed on the Google Earth Engine cloud computing platform. RivWidthCloud automatically extracts river centerline and widths from optical satellite images with the ability to flag observations that are obstructed by features like clouds, cloud shadows, and snow based on existing quality band classification. Because RivWidthCloud is built on a popular cloud computing platform, it allows users to easily apply the algorithm to the platform's vast archive of remote sensing images, thereby reducing the users' overhead for computing hardware and data storage. By comparing RivWidthCloud-derived widths from Landsat images to in situ widths from the U.S. and Canada, we show that RivWidthCloud can estimate widths with high accuracy (root mean square error: 99 m; mean absolute error: 43 m; mean bias:-21 m). By making RivWidthCloud publicly available, we anticipate that it will be used to address both river science questions and operational applications of water resource management

Planetary-scale surface water detection from space

This thesis studies automated methods of surface water detection from satellite imagery. Multiple existing methods are explored, discussed, and some new algorithms are introduced to allowvery accurate detection of surface water and surfacewater changes. Themethods range in applicability from the local level to global, and from detecting high-frequency changes to low-frequency changes. Their trade-offs regarding the accuracy and applicability of the surface water detection methods are also discussed.Several applications are presented to test the introduced methods. One of the studies focuses on a long-term global surface water change detection over the past 30 years at 30m resolution. The other application looks at the generation of a permanent surface water mask for Murray-Darling River Basin in Australia. Additionally, an in-depth validation for a small reservoir in California, USA is presented, to demonstrate performance of the new methods.The algorithms discussed in the thesis were applied and tested to process both passive optical multispectral and active Synthetic Aperture Radar (SAR) satellite data. Combining data fromall freely available satellite sensors requires harmonizations of the satellite data, but also, significant computing resources. In this thesis, Google Earth Engine parallel processing platformwas used to performmost of the experiments.We will see, thatwhen studying surface water dynamics, the best results can be achieved by combining discriminative and generative methods of surface water detection. This way, the surface water can also be detected from satellite images where surface water is only partially visible.In the thesis, top-of-atmosphere reflectance images are used to detect surface water. The atmospheric correction is not required when dynamic local thresholding methods are used to detect surface water.Water Resource

Deep-channel dynamics: A challenge for erosion management in large rivers

In this paper, we present flow and erosion problems in selected reaches of two large and dynamic river systems in South Asia, namely the Koshi River in Nepal (and India) and the Lower Brahmaputra (Jamuna) in Bangladesh. We attempted to analyse large- and meso-scale (short- and medium-term) morphological changes with a focus on the dynamics of deep-channels, revealing their importance for the river and riverbank erosion management. This focus on deep-channels is a key change of perspective as most morphological studies and analyses of large rivers are usually focused on sandbar and braiding dynamics. We used ground data, satellite imagery, and explorative morphological modelling to quantify and analyse the flow and morphological processes. We demonstrate how multispectral satellite imagery can be processed using Google Earth Engine to assess the spatiotemporal dynamics of morphological processes and changes. We also analysed bathymetric surveys to assess short-term changes of meso-scale morphology that are not fully captured by the satellite data analysis. The morphological modelling provided first results on reproducing essential processes, such as growth and migration of meso-scale features, particularly deep-channels, under varying flow conditions. Some features of these reaches of two rivers differ, but particularly the importance of deep-channel dynamics was revealed for both. We infer that the seasonal and annual discharge variabilities are key factors for the dynamic behaviour of bank, char (island), sandbars and deep-channels, particularly regarding short- and mid-term changes. We also infer that morphologically extreme situations do not always occur during high flows, but rather through the concentration of the flow along the deep-channels during medium and lower flows

OpenEarth Painting

Interactive model including particles and dye advection