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Klimaatadaptatie in het rivierengebied : een geo-ecologisch perspectief
Door klimaatverandering verandert het afvoerregime van onze grote rivieren. Hoogwaters worden hoger en frequenter, laagwaters lager en langduriger. Hoe we daarop reageren hangt af van hoe we klimaatverandering zien: als opgave, of als kans om onvolkomenheden aan te pakken. In dit artikel presenteren we aanzetten voor een meer geo-ecologisch gefundeerde inrichting, of -naar McHarg- voor design with nature.
A framework for predicting rainfall-induced landslides using machine learning methods
Landslides are catastrophic geo-hazards that threaten urbanization. Growth in population besides construction of critical infrastructures such as roads and pipelines in landslide-prone areas elevates the risk associated with landslides. Therefore, a system that is able to predict landslides and issues warning in a timely manner is very appealing. Various factors influence the stability of natural and engineered slopes and cause landslides, including topography, geology of slopes, precipitation, temperature changes, snowmelt, seismic activities, vol-canic activities, and human actions. It is widely accepted that precipitation is one of the most influential factors for triggering landslides. In this paper, we present the preliminary results of a practical research study that has been carried out in Deltares, The Netherlands. To that end, we have set up a framework that combines geo-engineering, remote sensing, hydrology with machine learning to predict the onset of landslides under the effect of precipitation. In this data-driven approach, Machine Learning (ML) methods are used to predict landslides by exploiting multiple Earth observation datasets, including rainfall data (e.g. TRMM 3B42) and Digital Elevation Models (e.g. SRTM1) and the NASA Global Landslide Catalogue. A detailed inventory of 10,988 landslides at a global level is built out of which 4,542 cases are used to train a supervised machine learning algorithm. The trained ML model is then fed by rainfall data, topography features such as slope and elevation relief, soil and bedrock data, and vegetation index of target regions to assess the stability of the studied area.
Proceedings of the 17th European Conference on Soil Mechanics and Foundation Engineering - ECSMGE 2019 : geotechnical engineering, foundation of the future (Reykjavik, Iceland, 1-6 September 2019)
A Bayesian approach to ecosystem service trade‑off analysis utilizing expert knowledge
The concept of ecosystem services is gaining attention in the context of sustainable resource management. However, it is inherently difficult to account for tangible and intangible services in a combined model. The aim of this study is to extend the definition of ecosystem service trade-offs by using Bayesian Networks to capture the relationship between tangible and intangible ecosystem services. Tested is the potential of creating such a network based on existing literature and enhancement via expert elicitation. This study discusses the significance of expert elicitation to enhance the value of a Bayesian Network in data-restricted case studies, underlines the importance of inclusion of experts’ certainty, and demonstrates how multiple sources of knowledge can be combined into one model accounting for both tangible and intangible ecosystem services. Bayesian Networks appear to be a promising tool in this context, nevertheless, this approach is still in need of further refinement in structure and applicable guidelines for expert involvement and elicitation for a more unified methodology.
Rheological analysis of mud from Port of Hamburg, Germany
An innovative way to define navigable fluid mud layers is to make use of their rheological properties, in particular their yield stress. In order to help the development of in situ measurement techniques, it is essential that the key rheological parameters are estimated beforehand. We investigated the changes in the rheological properties of mud from along the river stream in the Port of Hamburg, Germany, using a recently developed laboratory protocol. A variety of rheological tests was performed including: stress sweep tests, flow curves, thixotropic tests, oscillatory amplitude, and frequency sweep tests. The yield stresses of sediments from different locations were significantly dissimilar from each other due to differences in densities and organic matter content. Two yield stresses (termed static and fluidic) were observed for every sample and linearly correlated to each other. The thixotropic studies showed that all mud samples, except from one location, displayed a combination of thixotropic and anti-thixotropic behaviors. The results of frequency sweep tests showed the solid-like character of the sediments within the linear viscoelastic limit. The yield stresses, thixotropy, and moduli of the mud samples increased by going deeper into the sediment bed due to the increase in density of the sediments. This study confirmed the applicability of the recently developed protocol as a fast and reliable tool to measure the yield stresses of sediments from different locations and depths in the Port of Hamburg. The fluid mud layer, in all the locations it was observed, exhibited relatively small yield stress values and weak thixotropic behavior. This confirms that despite the fact that rheology of fluid mud is complex, this layer can be navigable.
Modelling the torque with artificial neural networks on a tunnel boring machine
The performance of earth pressure balanced tunnel boring machines (EPB-TBM) is dependent of a variety of parameters. Moreover, these parameters interact in a rather challenging way, making it difficult to adequately model their behaviour. Artificial neural networks have the aptitude to model complex problems and have been used in a variety of construction engineering problems. They can learn from existing data and then be used to predict the results, which makes them adequate for modelling problems where large amount of data is generated. In this work, a multilayer feedforward artificial neural network has been used to predict the torque at the cutter head of an EPB-TBM. A time series neural network has been used, where torque was predicted as a function of the measured torque and the volume of the injected foam on previous time steps. Results indicate that feedforward artificial neural network can be used to predict the torque at the cutter head in a EPB-TBM.
Lessons of past disasters and preparedness actions to cope with future hydrological extreme events in the Netherlands
The Netherlands, being a low-lying delta of the rivers Rhine, Meuse and Scheldt, have grappled for centuries in coping with water-related disasters: floods originating from both storm surges and high river discharges. Projected climate change scenarios learned the country to prepare for even more frequent and more intense extreme events. We realized the need for new solutions: automatically heightening the levees to protect against flooding was no longer a sustainable solution. We had to change the system we worked with for centuries and broaden its goals. The Netherlands revisited their safety standards for protection against flooding, now incorporating a risk-based approach. We introduced nature-based solutions like “Room for the River” to enable higher river discharges and the “Sand Engine” for beach nourishment to complement traditional engineering for protective disaster resilient infrastructure. The Netherlands embraced system thinking to future proof the country, and we incorporated cultural and ecological values into adaptive decision making. The Netherlands has proven it can shift the fundamentals of its strategy to prepare for a changing climate. Essentially, we have addressed the synergies between the agendas of water-related disaster risk reduction and climate adaptation in a coherent way, both of which are essential in reaching the integrated goals of the nation’s long-term vision for sustainable development.
HELP global report on water and disasters 2019
The focus of HELP is to promote concrete actions by governments and stakeholders and to help achieve transformative changes to drastically improve preparedness and readiness for water-related disasters as well as provision of safe water and sanitation at emergency. Learning from experiences, lessons, and good practices of disasters that have already happened will enable the transformation fast and effectively. That is the very reason why HELP decided to start compiling and sharing lessons and experiences of major disasters on regular basis. This document is a major part of HELP’s flagship initiatives. The Report will be published on annual basis.
Parallellisatie van MODFLOW 6
Sinds eind 2017 werkt Deltares in opdracht van de United States Geological Survey (USGS) aan de parallellisatie van de open-source rekencode MODFLOW 6. Dit artikel geeft kort de gekozen concepten van de parallellisatie weer en de eerste numerieke experimenten op de Nationale Supercomputer Cartesius. De experimenten tonen aan dat aanzienlijke versnellingen in rekentijd kunnen worden behaald en dat de MODFLOW 6 rekenkern in de basis geschikt is voor zeer grote, hoge-resolutie, grondwatermodellen.
Global potential for the growth of fresh groundwater resources with large beach nourishments
Whether a coastal area is suitable for beach nourishments and can induce a growth in fresh groundwater resources depends on the appropriateness of the intended site for beach nourishments, and the attainable growth in fresh groundwater resources. In this study we presume that all eroding sandy beaches are suitable for large beach nourishments, and focus on the impact of these nourishments on fresh groundwater in various coastal settings. The growth in fresh groundwater resources -as a consequence of the construction of a beach nourishment- was quantified with 2-D variable-density groundwater models, for a global range in geological parameters and hydrological processes. Our simulation results suggest that large beach nourishments will likely lead to a (temporary) increase of fresh groundwater resources in most settings. However, for a substantial growth in fresh groundwater, the coastal site should receive sufficient groundwater recharge, consist of sediment with a low to medium hydraulic conductivity, and be subject to a limited number of land-surface inundations. Our global analysis shows that 17% of shorelines may consist of erosive sandy beaches, and of these sites 50% have a high potential suitability. This shows a considerable potential worldwide to combine coastal protection with an increase in fresh groundwater resources.