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Simulating synthetic tropical cyclone tracks for statistically reliable wind and pressure estimations
The design of coastal protection measures and the quantification of coastal risks at locations affected by tropical cyclone (TC) are often based solely on the analysis of historical cyclone tracks. Due to data scarcity and the random nature of TCs, the assumption that future TCs could hit a neighboring area with equal likelihood than past events can potentially lead to over- and/or underestimations of extremes and associated risks. The simulation of numerous synthetic TC tracks based on (historical) data can overcome this limitation. In this paper, a new method for the generation of synthetic TC tracks is proposed. The method has been implemented in the highly flexible open-source Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE). TCWiSE uses an Empirical Track Model based on Markov-chains and can simulate thousands of synthetic TC tracks and wind fields in any oceanic basin based on any (historical) data source. Moreover, the tool can be used to determine the wind extremes and the output can be used for the reliable assessment of coastal hazards. Validation results for the Gulf of Mexico show that TC patterns and extreme wind speeds are well reproduced by TCWiSE.
Micro and nanoplastics in the aquatic environment with special reference to synthetic fibers
Plastic particles in the micrometer-size range have been detected worldwide in virtually all aquatic compartments, such as surface waters, water column, sea floor, coastlines, polar ice, rivers, small waterways and lakes, as well as in a wide range of species. This raises scientific and public concern on their possible impact on aquatic populations, food webs, and food production for humans. The debate is fuelled by the persistence of the plastics and a projected future increase in microplastic pollution levels.
Synthetic nano- and microfibers
Increasing consumption of fabric material causes the accumulation of single fibers into the natural environment. Significant numbers of fibers are discharged via wastewater from washing clothes, deposition from atmosphere or by other ways of transport. Fibers are now the most prevalent type of anthropogenic particles found by microplastic pollution surveys around the world. Substantial concentration of fibers have been detected in surface water, deep-sea and fresh water ecosystems. As a consequence fibers are present in food, drinking water, human lungs and digestive tracts of aquatic animals. Currently, there is great concern for the release of plastic nano- and micro fibers and microparticles (microplastics) to the natural environment for which nobody knows, so far, the ultimate consequences for health and ecological homeostasis. During November 4th and 5th, 2019, a group of scientists from different parts of the world met at Wetsus, the European Centre of Excellence for Sustainable Water Technology in Leeuwarden, The Netherlands, to discuss all known aspects of synthetic nano- and microfibers. This included the morphology, physicochemical properties, production and origin of nano/microfibers entering the atmosphere, water and food chain; the potential consequences of inhalation and ingestion for human health, and exposure and ingress via life cycle for aquatic biota; analytical and measurement methods; techniques to clean air and water, and protection means against inhalation or other ways to enter the human body.
Variations in canopy cover and its relationship with canopy water and temperature in the Miombo Woodland based on satellite data
Understanding the canopy cover relationship with canopy water content and canopy temperature in the Miombo ecosystem is important for studying the consequences of climate change. To better understand these relationships, we studied the satellite data-based land surface temperature (LST) as proxy for canopy temperature, leaf area index (LAI), and the normalized difference vegetation index (NDVI) as proxies for canopy cover. Meanwhile, the normalized difference infrared index (NDII) was used as a proxy for canopy water content. We used several statistical approaches including the correlated component regression linear model (CCR.LM) to understand the relationships. Our results showed that the most determinant factor of variations in the canopy cover was the interaction between canopy water content (i.e., NDII) and canopy temperature (i.e., LST) with coefficients of determination (R2) ranging between 0.67 and 0.96. However, the coefficients of estimates showed the canopy water content (i.e., NDII) to have had the largest percentage of the interactive effect on the variations in canopy cover regardless of the proxy used i.e., LAI or NDVI. From 2009–2018, the NDII (proxy for canopy water content) showed no significant (at alpha level 0.05) trend. However, there was a significant upward trend in LST (proxy for canopy temperature) with a magnitude of 0.17 C/year. Yet, the upward trend in LST did not result in significant (at alpha level 0.05) downward changes in canopy cover (i.e., proxied by LAI and NDVI). This result augments the observed least determinant factor characterization of temperature (i.e., LST) on the variations in canopy cover as compared to the vegetation water content (i.e., NDII).
Urgent measure of geospatial parameters for flood modeling in Indonesia
Indonesia is prone to flood. The flood condition is different from time to time due to rain intensity and rivers capacity are leading to disaster. Low land areas such as coastal areas and peatland areas in many regions of Indonesia are experiencing the same disaster. Adaptation and mitigation should be taken against this flood disaster. In order to find the best adaptation and mitigation, first, we must understand the characteristic of the flood by creating flood models. Essential parameters of flood modeling would include geospatial parameters (e.g., Digital Elevation Model, Land use, and rivers geometry). Unfortunately for Indonesia's case, these geospatial parameters of the flood are still relatively weak. We can see that several flood models of Indonesia are in low accuracy spatially and temporarily. So, the measure of geospatial parameters is urgent. This paper will highlight this urgency.
Monitoring and statistical modelling of the solids accumulation rate in gully pots
Gully pots are utilized for conveying runoff to drainage systems, as well as for reducing the system’s solids loading by retaining suspended solids. However, the accumulation of solids in gully pots reduces their removal efficiency, leading to an increase in solids transport towards the drainage system. This article aims to identify the main drivers of the solids accumulation in gully pots and, thus the relevant processes for wash-off models. The solids accumulation rates in 407 gully pots were monitored within a period of approximately 14 months and were analysed by means of a linear mixed model and a regression tree. The parameters vegetation factor, rainfall volume, and filling degree are the main drivers of the accumulation process. These parameters are linked to the solids build-up in a catchment, solids transport, and solids retention in gully pots, which means that none of these 3 processes is dominant.
De effecten van stroombaanmaaien proefondervindelijk onderzocht in de Eefse Beek
Hoewel er rekenmethoden bestaan om bij stroombaanmaaien de breedte van de stroombaan te berekenen, ontbreekt goede velddata voor validatie. In dit onderzoek zijn onder gecontroleerde omstandigheden stroomproeven uitgevoerd in de Eefse Beek (Waterschap Rijn en IJssel) waarbij een steeds bredere stroombaan is gemaaid. Het blijkt dat er een maximale breedte bestaat waarbij maaien effect heeft, dat goed maaien niet hoeft te leiden tot problemen met de waterkwaliteit en dat voorspellen van de benodigde breedte een uitdaging blijft. Modelberekeningen met de hier bepaalde relaties tussen ruwheid en opstuwing suggereren bovendien dat beperking van slibophoping voor peilbeheersing effectiever is dan maaien.
Discrepancies in flood modelling approaches in transboundary river systems: legacy of the past or well-grounded choices?
Flood modelling in transnational rivers requires efficient cross-border collaboration among the riparian countries. Currently, each country/region usually uses a different hydraulic modelling approach, which may hinder the modelling of the entire river. For the sake of accurate and consistent river modelling there is a necessity for the establishment of a framework that fosters international collaborations. This study investigates the current hydraulic modelling approach across the whole length of the River Meuse, the main course of which crosses three north-western European countries. The numerical models used by French, Belgian, and Dutch agencies and authorities were interconnected by exchanging boundary conditions at the borders. At the central part of the river, the Belgian hydraulic model assumed steady flow conditions, while the rest of the river was modelled in unsteady mode. Results for various flood scenarios revealed a distinctive pattern of water depths at the Belgian-Dutch border. To clarify whether this is a bias induced by the change in modelling approach at the border (steady vs. unsteady), we remodelled a stretch of the river across the Belgian-Dutch border using a consistent unsteady modelling approach. The steady and unsteady approaches led to similar patterns across the border, hence discarding the hypothesis of a bias resulting from a change in the employed model. Instead, the pattern in water depths was attributed to a change in the topography of the Meuse Valley, where there is a transition from a narrow steep corridor with limited water storing capacity in Ardennes massif to wide floodplains in the Dutch lowlands. The associated flood damping for the 100-year discharge is less than 1% in the Ardennes and exceeds 15% in the Dutch lowlands. It can be inferred that the current differences in regional hydraulic modelling approaches for the River Meuse are generally well-grounded and not just a legacy of the past.