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

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

A simple spatio–temporal data fusion method based on linear regression coefficient compensation

High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.</p

Erratum to: The State of the World’s Beaches (Scientific Reports, (2018), 8, 1, (6641), 10.1038/s41598-018-24630-6)

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.</p

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.Accepted Author ManuscriptRivers, Ports, Waterways and Dredging Engineerin

Time-series surface water gap filling based on spatiotemporal neighbourhood similarity

Optical satellite-derived surface water monitoring is challenging because of the spatial gaps in images caused by clouds, cloud shadows, voids, etc. Here, an efficient method for filling gaps in time-series surface water images is proposed, based on the spatiotemporal characteristics of water. This method utilises the accurately classified historical ternary (gap, water, non-water) or binary (water, non-water) water image time-series and the clear part of the ternary gap water image. Pixels with values of 0 and 1 in the same period water occurrence image are first used to correct the gap water image. The spatial neighbourhood similarity is then calculated as a quality control band for mosaicking the accurately classified historical water images. The final result is generated by replacing the gap pixels with a mosaic image. The proposed method was implemented on the Google Earth Engine, and 93 Landsat 8 top-of-atmosphere (TOA) images were used to verify its validity. Quantitative evaluations were adequate, with a mean accuracy, recall, and precision of 0.98, 0.90, and 0.85, respectively. The proposed method could improve the utilisation of optical remote sensing data and would be applicable to the production of large-area homogeneous surface water time-series and water resource monitoring

Naive Bayes classification-based surface water gap-filling from partially contaminated optical remote sensing image

Optical remote sensing images are a common data sources for surface water monitoring, while they are easily contaminated by clouds, cloud shadows, terrain shadows, etc., resulting in spatial gaps in surface water images. This paper proposes a surface water gap-filling method based on Naive Bayes classification. It uses the historical cloud-free binary (water, non-water) surface water images as prior data and the uncontaminated pixels in the partially contaminated ternary (water, non-water, contaminated pixels) surface water image as evidence to identify the category of gap pixels to achieve the purpose of gap-filling. This method considers the relationship between disconnected water bodies and does not depend on terrain data. When the image is heavily covered by clouds, this method can also reconstruct the complete water extent accurately. Five study areas with different scenarios including rivers, lakes or reservoirs, are selected to evaluate the method. Results show that the average gap-filling accuracy in all five study areas is over 90 %. After gap-filling, the time series of surface water area presents a good correlation with the time series of water level (e.g., the coefficient of determinationR2 = 0.95 in the Dartmouth reservoir). The proposed method is proved effective in filling gaps caused by clouds, cloud shadows and terrain shadows in surface water image, and it would be suitable for high-frequency surface water monitoring and near real-time surface water mapping