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Monitoring of a railway piled embankment
How ecological engineering can serve in coastal protection
Traditionally, protection of the coastal area from flooding is approached from an engineering perspective. This approach has often resulted in negative or unforeseen impacts on local ecology and is even known to impact surrounding ecosystems on larger scales. In this paper, the utilization of ecosystem engineering species for achieving civil-engineering objectives or the facilitation of multiple use of limited space in coastal protection is focused upon, either by using ecosystem engineering species that trap sediment and damp waves (oyster beds, mussel beds, willow floodplains and marram grass), or by adjusting hard substrates to enhance ecological functioning. Translating desired coastal protection functionality into designs that make use of the capability of appropriate ecosystem engineering species is, however, hampered by lack of a generic framework to decide which ecosystem engineering species or what type of hard-substrate adaptations may be used where and when. In this paper we review successful implementation of ecosystem engineering species in coastal protection for a sandy shore and propose a framework to select the appropriate measures based on the spatial and temporal scale of coastal protection, resulting in a dynamic interaction between engineering and ecology. Modeling and monitoring the bio-physical interactions is needed, as it allows to upscale successful implementations and predict otherwise unforeseen impacts.
A numerical study on design of coastal groins
EPXMA survey of shelf sediments (Southern Bight, North Sea) : a glance beyond the XRD-invisible
Shelf sediments of the southern North Sea, were studied with a microanalytical [electron probe X-ray microanalysis (EPXMA)] and two bulk [X-ray diffraction (XRD) and X-ray fluorescence (XRF)] techniques.
Challenges and opportunities for integrating lake ecosystem modelling approaches
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others (‘reinventing the wheel’). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available (‘having tunnel vision’). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues.
Benzene degradation at a site amended with nitrate or chlorate
This paper describes the anaerobic degradation ofbenzene at a contaminated site.
An operational drought forecasting system using coupled models for groundwater, surface water and unsaturated zone
Wrapping Fortran libraries
There exist numerous libraries in C or Fortran that can be used to solve all manner of mathematical-numerical problems, such as Lapack for linear algebra problems. These libraries comprise the experience of many mathematicians and software engineers. One of the goals of the Ftcl project is to make Tcl extensions for such libraries. The Wrapfort tool, akin to Critcl, especially, is designed to generate the required code.
Bed composition generation for morphodynamic modeling: case study of San Pablo Bay in California, USA
Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system
Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive framework for Bayesian revision has been recently developed and applied to operational flood forecasting with deterministic weather forecasts. The Bayesian revision yields a posterior density, conditional on all information available to the forecaster at the onset of a forecast run. This conditional density objectively quantifies the uncertainty. Here the Bayesian approach is generalized for use with ensemble weather predictions. An end-to-end application of a Bayesian postprocessor for ensemble streamflow forecasts in the river Rhine forecasting system is presented. A verification of the postprocessor shows good performance when compared in terms of the ranked probability skill score to non-Bayesian uncertainty assessment, such as ranking threshold exceedance probabilities for members of a streamflow ensemble prediction. In this context it is also addressed how the proposed Bayesian processor can serve in supporting rational decision making for flood warning under conditions of uncertainty.