Fokke Saathoff

The design of coastal protection structures requires design parameters that accurately represent the hydrodynamic conditions along the coast. Currently, these input variables are based on univariate probability models, which do not consider the joint probability of water level and statistical wave parameters such as significant wave height. Bivariate probability modeling using copula models offers an alternative. Copulas can be used to describe the dependencies between water level and significant wave height and to compute joint probabilities of occurrence. First, various copulas are fitted to samples of physically consistent combinations of water level and significant wave height extracted from storm surge events along the German Baltic Sea coast of Mecklenburg-Western Pomerania. Next, the most appropriate copula model is used to compute design combinations of water level and significant wave height for selected return periods. The bivariate design parameters are compared with the univariate ones in a simplified design example for wave run-up on a dike. The validation of various models shows that the Frank copula best describes the dependence structure. The bivariate design parameters obtained for the same return periods are lower than those determined using the univariate method. The available data only allow a limited application of the copulas for engineering design in the study area. Nevertheless, copulas have the potential to replace univariate methods for determining design parameters and thus contribute to more reliable and cost-efficient coastal protection structure design.

One of the world’s major issues is climate change, which has a significant impact on ecosystems, human beings, agricultural productivity, water resources, and environmental management. The General Circulation Models (GCMs), specially the recently released (coupled model intercomparison project six) CMIP6 are very indispensable to understand and support decision makers to identify adaptation strategies in response to future climate change in a basin. However, proper selection of skillful GCMs and future climate assessment is a prior task to climate impact studies. The objective of the study is an attempt to appraise the climate model’s performance and future climate scenarios of Shared Socioeconomic Pathways (SSPs) in the Omo River Basin. The performance evaluation of 20 GCMs of the CMIP6 was properly performed to reproduce the precipitation and the maximum temperature in the basin. Their performance has been carried out against the best selected mean monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) precipitation and European Community Medium Water Range Weather Forecasts Version 5 (ECMWF-ERA5) maximum temperature. The GCMs of the CMIP6 were selected and ranked using the compromise programming method of multi-criteria decision making. The result shows that ensemble models and NorESM2-MM models have been designated to reproduce the precipitation and maximum temperature in the basin respectively. The Mann–Kendall trend test was executed to appraise the trend of selected CMIP6 models, and subsequently, downscaling and bias correction techniques were conducted. The projected seasonal precipitation of June, July, August, September (JJAS) and March, April, May (MAM) shows an increasing trend with 10.86, 17.66, 38.96 and 11.85, 22.1, and 40.7% under SSP2452031-2060, SSP5852031-2060, and SSP5852071-2100 scenarios respectively. Furthermore, increasing trends were detected in MAM by 12.8% and decreasing trends in JJAS were detected by 15.23% under SSP2452071-2100 scenario. The maximum temperature projection will be increased on average by 0.95, 1.78, 1.4, and 3.88 °C in JJAS and 1.53, 2.24, 1.56, and 3.89 °C in MAM under climate change scenarios of near-future SSP2452031-2060, SSP5852031-2060, far-future SSP2452071-2100, and SSP5852071-2100, respectively. Additionally, the basin has shown temporal-spatial climate fluctuation in terms of precipitation and maximum temperature.

A large part of the Baltic Sea coastline in the north-eastern provinces of Germany (Mecklenburg-Vorpommern and Schleswig-Holstein) is protected by coastal protection dunes, which are constructed and maintained to preserve a quasi-natural beach environment. The latest IPCC report predicts a significant sea level rise even in the Baltic, together with an increase in storm surges within the next decades. Sand dune constructions are particularly affected by these developments, in close connection to the increasing problems of sediment mining for beach nourishment and dune maintenance. In the BMBF research project PADO (Processes and Implications of Dune Breaching at the German Baltic Sea Coast), scientists from Rostock University and RWTH Aachen University investigated together with various partners how constructed sand dunes would fail during a storm surge, focusing on the erosion and the initiation and development of a breach. Therefore, a large-scale field experiment was installed at the beach of Rostock-Warnemünde, which was instrumented and surveyed in high resolution during the storm surge in November 2018, leading to the collapse of the dune, using innovative measurement systems, including terrestrial laser scanning and photogrammetry. The measurements were used to run a numerical model (XBeach) to simulate dune breaching for three different sections of existing sand dunes at the coast of Mecklenburg-Vorpommern. Additional investigations were made to analyse the filling and emptying of the polder areas behind the coastal protection dunes and the consequences of the floods with respect to saltwater intrusion. In this paper, the field experiment and the results from measurements and simulations are compared to one of the real reference dunes, and the transfer of results to a dune breach incident near Rostock in 2019 is discussed.

This study has two goals: First, to fill a gap in the use of the predictive approach for evaluating road safety performance in Ethiopia, the most recent analytical methods of the HSM predictive approach in IHSDM software were used to evaluate the safety and operational effects of the existing roadway geometric design. Second, to assure safety and a sustainable transportation system, the relative safety effects of design changes made to hazardous road segments were quantified. Based on the Crash Prediction Module (CPM) evaluation of IHSDM software, the study identified fifteen hazardous road segments on the existing rural two-lane roads of Addis Ababa to Chacha and Addis Ababa to Dillela. The design changes made to the hazardous road segments have resulted in a remarkable reduction in crash rate, especially on the first top five hazardous segments, where incredible improvements have been observed. The total safety benefits acquired by applying engineering mitigations to the fifteen identified hazardous segments are described as follows: 17.18% reduction in total crash frequency (crashes/year), 58.94% reduction in crash rate (crashes/km/year), and 58.86% reduction in travel crash rate (crashes/million veh-km). In general, the study’s findings underlined the effectiveness of performance-based road safety evaluation and design in providing safe, efficient, and economically-feasible roadway infrastructure. The IHSDM reports and graphical outputs assist decision-making in the roadway design process by providing a quantitative evaluation of the safety impact of various design features and identifying roadway segments with safety concerns. Additionally, IHSDM is a tool capable of saving time for Road Safety Audit (RSA) teams. The paper also outlined the need for a computerized crash database recording system in Ethiopia to develop jurisdiction-specific Safety Performance Functions (SPFs).

For the selection of global climate models in the upper basin of the Blue Nile, an advanced envelope-based approach was used. Currently, the number of general circulations models (GCM) has increased extremely. The reliability of any general circulation model in a particular region is confronted, so the selection of the appropriate climate models that can predict the climate variable is essential. Representative concentration pathways RCP4.5 and RCP8.5 were taken into account. For RCP4.5 105 GCMs were used and for RCP8.5 78 GCMs were used to select the best performance models for the Upper Blue Nile Basin for a climate change impact study. Three steps were followed to derive the best performing models in the study area based on their range of projected mean temperature and precipitation changes, the range of projected extreme changes, and the ability to reproduce past climates between 1971 and 2000 and 2071–2100. Five corners of the spectrum were used, e.g., wet-warm, wet-cold, dry-warm, dry-cold, and the 50th percentile of the temperatures. For RCP4.5 and RCP8.5, a total of 25 GCMs were chosen based on the range of anticipated mean temperature and rainfall change. Based on the range of extreme changes, 10 GCMs were chosen. Finally, for each RCP4.5 and RCP8.5, five GCMs were chosen by combining all three stages.

Predicting the number of crashes that may occur as a result of specific highway features is critical in evaluating different treatment or design alternatives. Since different highway geometric characteristics can influence crash distribution datasets, Highway Safety Manual's (HSM's) predictive method encourages users to predict crashes based on their severity and collision type proportions. This study used crash data from rural two-way two-lane road segments in the Oromia region over seven years to develop Oromia's fixed crash distribution dataset on Interactive Highway Safety Design Model (IHSDM) software. The crash distribution dataset has two parts; the crash severity proportions and the collision type percentages. The developed Oromia's fixed crash distribution dataset was compared and validated against the default HSM crash configuration. As a result, the Crash Prediction Model (CPM) evaluation results confirmed that the developed crash severity proportion (the first part of the crash distribution dataset) estimates are more accurate and closer to the observed values. Furthermore, the findings show that crashes in the Oromia region are severer than in the states where the HSM crash configuration was developed. According to the second part of the crash distribution dataset evaluation (collision type percentage), the developed fixed crash distribution dataset outperforms the default HSM configuration in most collision type proportions, but not in all. For instance, from the ten collision type proportions developed, Right-Angle and sides-wipe collision proportions are predicted more precisely by the default HSM configuration. This points to the need for developing collision type proportion (the second part of the crash distribution dataset) as a function rather than a fixed configuration for a better result, based on the availability of complete crash data (i.e. crash location). In general, the study revealed that in order to exploit the full potential of HSM's predictive approach, researchers must develop a jurisdiction crash distribution dataset using local crash data. The methodology demonstrated in this study to develop the jurisdiction's crash distribution dataset has been validated as true thus, safety practitioners are encouraged to adopt it.

The sub-working group UG 4 for geosynthetic clay liner properties, design aspects and test methods in geotechnics and hydraulic engineering is established in 1994 as part of the working group Ak 5.1 “Geosynthetics for Geotechnics and Hydraulic Engineering” within the German Geotechnical Society (DGGT). The UG 4 results are fixed in the first German technical recommendations EAG-GTD (2002) relating geosynthetic clay liners. The main objective is giving technical advises in manufacturing aspects, design requirements and installation of geosynthetic clay liners as well as basic information relating quality testing methods, quality assurance, tendering and contracting. The technical recommendations EAG-GTD (2002) are directed to construction clients, civil engineers and practical users in the field of geotechnical and hydraulic engineering applications.

The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.

This paper presents the methodology, assumptions, and functionalities of an application developed during the realization of the project “South Baltic Oil Spill Response through Clean-up with Biogenic Oil Binders” (SBOIL). The SBOIL project is a continuation of the BioBind project, the primary goal of which was to develop and deploy an oil recovery system designed for use in coastal waters and adverse weather conditions. The goal of the SBOIL project was to use this new technology to improve the current response capabilities for cross-border oil spills. The developed application allows for the determination of the position of an aircraft at the time of dropping the oil binders, the determination of the oil binders’ position after falling in terms of a specific aircraft’s position, the determination of the position of oil binders after a certain time in order to plan the action of recovering it from the water surface, and the determination of the time when the binders will be in their assumed position.

A variety of studies recently proved the applicability of different dried, fine-grained dredged materials as replacement material for erosion-resistant sea dike covers. In Rostock, Germany, a large-scale field experiment was conducted, in which different dredged materials were tested with regard to installation technology, stability, turf development, infiltration, and erosion resistance. The infiltration experiments to study the development of a seepage line in the dike body showed unexpected measurement results. Due to the high complexity of the problem, standard geo-hydraulic models proved to be unable to analyze these results. Therefore, different methods of inverse infiltration modeling were applied, such as the parameter estimation tool (PEST) and the AMALGAM algorithm. In the paper, the two approaches are compared and discussed. A sensitivity analysis proved the presumption of a non-linear model behavior for the infiltration problem and the Eigenvalue ratio indicates that the dike infiltration is an ill-posed problem. Although this complicates the inverse modeling (e.g., termination in local minima), parameter sets close to an optimum were found with both the PEST and the AMALGAM algorithms. Together with the field measurement data, this information supports the rating of the effective material properties of the applied dredged materials used as dike cover material.