Assessing climate change impacts on flooding risks in the Belgian coastal zone

Within the scope of the Belgian project CLIMAR an attempt is made to develop an evaluation framework for adaptation scenario’s as a response to the climate change induced impacts in the North Sea area. Primary effects are direct consequences of climate change such as sea level rise, erosion, changes in temperature and precipitation and increased storminess. Secondary impacts are direct and indirect results of the primary effects on different sectors. A first phase of the project consists of identifying and scoping the secondary impacts on ecological and social-economic activities. In this paper results will be presented regarding the secondary impacts of flooding only.Climate change induced primary effects such as sea level rise and increased storminess lead to higher risks of flooding of low-lying coastal areas. One of the most significant social secondary effects is the number of people at risk due to flooding. An important economical effect of climate change is the amount of damage costs. Besides direct damages there will also be indirect economic results such as temporary suspension of production and loss of jobs. Other ecological effects of increased flooding risks are the loss of beach and dune area, as well as associated specific habitats such as wetlands. Indirectly this leads to loss of biodiversity.The magnitude of the most significant secondary effects is quantified by carrying out risk calculations. For each of the sets of the changing physical parameters a related storm scenario is statistically determined. In a first approach, the flooding risks during an extreme storm under present climate conditions and sea level rise are estimated. By means of a set of numerical models the areas susceptible to flooding in the Belgian coastal plain are identified. The resulting flooding risk maps are then used to estimate the scope of the secondary impacts

Geochemical modeling of magmatic gas scrubbing

The EQ3/6 software package, version 7.2 was successfully used to model scrubbing of magmatic gas by pure water at 0.1 MPa, in the liquid and liquid-plus-gas regions. Some post-calculations were necessary to account for gas separation effects. In these post-calculations, redox potential was considered to be fixed by precipitation of crystalline a-sulfur, a ubiquitous and precocious process. As geochemical modeling is constrained by conservation of enthalpy upon water-gas mixing, the enthalpies of the gas species of interest were reviewed, adopting as reference state the liquid phase at the triple point. Our results confirm that significant emissions of highly acidic gas species (SO2(g), HCl(g), and HF(g)) are prevented by scrubbing, until dry conditions are established, at least locally. Nevertheless important outgassing of HCl(g) can take place from acid, HCl-rich brines. Moreover, these findings support the rule of thumb which is generally used to distinguish SO2-, HCl-, and HF-bearing magmatic gases from SO2-, HCl-, and HF-free hydrothermal gases

A GIS for flood risk management in Flanders

In the past decades, Flanders, a region of north Belgium that extends from the coastline inland (in northwest Europe), has suffered several serious riverine floods that caused substantial property damage. As Flanders is one of the most densely populated regions in the world, a solid water management policy is needed in order to mitigate the effects of this type of calamity. In the past, Flemish water managers chose to drain off river water as quickly as possible by heightening the dikes along the rivers. However, this method leads to a higher flood probability further downstream. Moreover, water defence infrastructure can always suffer from technical failures (e.g., breaching) creating even more damage than would have occurred if no defences were in place. In a search for a better solution to this recurring problem, the Flemish administration proposed a new approach in the 1990 s. This approach focuses on minimizing the consequences of flooding instead of attempting to prevent floods. To implement this approach, large amounts of data were gathered for the Flemish Region. Using a Geographic Information System (GIS), a risk-based methodology was created to quantitatively assess flood risk based on hydrologic models, land use information and socio-economic data. Recently, this methodology was implemented in a specifically designed GIS-based flood risk assessment tool called LATIS. By estimating the potential damage and number of casualties during a flood event, LATIS offers the possibility to perform risk analysis quickly and effectively. This chapter presents a concise overview of LATIS’ methodology and its implementation for flood risk management in Flanders

CLIMAR: evaluation of climate change impacts and adaptation responses for marine activities. Subdocument coastal flooding

Within the project CLIMAR an attempt is made to develop an evaluation framework for adaptation responses toclimate change induced impacts within the North Sea environment. The identification and quantification of all thepossible secondary impacts of climate change is handled on a sectoral level. The different case-studiesinvestigated in the framework of the research are fishery, tourism, sand and gravel extraction, the port ofZeebrugge and flood risks. This document gives an overview of all the different types of land occupation andinfrastructure within the coastal zone that might be subject to flood risks as a consequence of primary climatechange impacts

DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

Crop yield is an essential measure for breeders, researchers and farmers and is comprised of and may be calculated by the number of ears/m2, grains per ear and thousand grain weight. Manual wheat ear counting, required in breeding programmes to evaluate crop yield potential, is labour intensive and expensive; thus, the development of a real-time wheat head counting system would be a significant advancement. In this paper, we propose a computationally efficient system called DeepCount to automatically identify and count the number of wheat spikes in digital images taken under the natural fields conditions. The proposed method tackles wheat spike quantification by segmenting an image into superpixels using Simple Linear Iterative Clustering (SLIC), deriving canopy relevant features, and then constructing a rational feature model fed into the deep Convolutional Neural Network (CNN) classification for semantic segmentation of wheat spikes. As the method is based on a deep learning model, it replaces hand-engineered features required for traditional machine learning methods with more efficient algorithms. The method is tested on digital images taken directly in the field at different stages of ear emergence/maturity (using visually different wheat varieties), with different canopy complexities (achieved through varying nitrogen inputs), and different heights above the canopy under varying environmental conditions. In addition, the proposed technique is compared with a wheat ear counting method based on a previously developed edge detection technique and morphological analysis. The proposed approach is validated with image-based ear counting and ground-based measurements. The results demonstrate that the DeepCount technique has a high level of robustness regardless of variables such as growth stage and weather conditions, hence demonstrating the feasibility of the approach in real scenarios. The system is a leap towards a portable and smartphone assisted wheat ear counting systems, results in reducing the labour involved and is suitable for high-throughput analysis. It may also be adapted to work on RGB images acquired from UAVs