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Extreme sea levels on the rise along Europe’s coasts
Future extreme sea levels and flood risk along European coasts will be strongly impacted by global warming. Here, we show changes in all acting components, i.e., sea level rise, tides, waves, and storm surges, until 2100 in view of climate change. We find that by the end of this century the 100-year event along Europe will on average increase between 57 and 81 cm. The North Sea region is projected to face the highest increase, amounting to nearly 1 m under a high emission scenario by 2100, followed by the Baltic Sea and Atlantic coasts of the UK and Ireland. Sea level rise is the main driver of the changes, but intensified climate extremes along most of northern Europe can have significant local effects. Little changes in climate extremes are shown along southern Europe, with the exception of a projected decrease along the Portuguese coast and the Gulf of Cadiz, offseting sea level rise by 20–30%. By the end of this century, 5 million Europeans currently under threat of a 100-year coastal flood event could be annually at risk from coastal flooding under high-end warming.
Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information
Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining (AI & DM) techniques in assisting reservoir streamflow sub-seasonal to seasonal forecasts have been increasing. In this study, Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Regression (SVR), are employed and compared with respect to their capabilities for predicting one-month-ahead reservoir inflows for two headwater reservoirs in USA and China, respectively. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e. PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are auto-correlated with up to several months, and the climatic information and their lags are cross-correlated with local hydrological conditions in our case studies.
NCK-days 2015 : book of abstracts (Strandpaviljoen Struin, Camperduin, 18-20 March 2015)
NCK-days 2016 : book of abstracts (Inspiration Centre Grevelingen, Ouddorp, 16-18 March 2016)
NCK-days 2014 : preparednesss (UNESCO-IHE, Delft, 27-28 March 2014)
NCK-days 2017 : book of abstracts (Den Helder, 15-17 March 2017)
Erodibility of soft fresh water sediments : the role of bioturbation by meiofauna
Markermeer is a large and shallow fresh water lake in The Netherlands. It has a 680 km2 surface and a 3.6 m mean water depth. Markermeer is characterized by its high turbidity, which affects the lake ecosystem seriously. As part of a study that aims to mitigate this high turbidity, we studied the water bed exchange processes of the lake’s muddy bed. The upper cm’s – dm’s of the lake bed sediments mainly consist of soft anoxic mud. Recent measurements have proved the existence of a thin oxic layer on top of the soft anoxic mud. This oxic layer is believed to be responsible for Markermeer high turbidity levels. Our hypothesis is that the oxic layer develops from the anoxic mud, and due to bioturbation. In particular we will refer to bioturbation caused by meiobenthos. The objective of this study is to determine the influence of the development of the oxic layer on the water-bed exchange processes, as well as the role of bioturbation in this processes. This is done by quantifying the erosion rate as a function of bed shear stresses, and at different stages of the development of the oxic layer. Our experiments show that bioturbation increases the erosion rate of Markermeer sediments, and therefore affects the fine sediment dynamics of the lake.
Turning off the DRIP (data-rich, information-poor) : rationalising monitoring with a focus on marine renewable energy developments and the benthos
Marine renewable energy developments (MREDs) are rapidly expanding in size and number as society strives to maintain electricity generation whilst simultaneously reducing climate-change linked CO2 emissions. MREDs are part of an ongoing large-scale modification of coastal waters that also includes activities such as commercial fishing, shipping, aggregate extraction, aquaculture, dredging, spoil-dumping and oil and gas exploitation. It is increasingly accepted that developments, of any kind, should only proceed if they are ecologically sustainable and will not reduce current or future delivery of ecosystem services. The benthos underpins crucial marine ecosystem services yet, in relation to MREDs, is currently poorly monitored: current monitoring programmes are extensive and costly yet provide little useful data in relation to ecosystem-scale-related changes, a situation called ‘data-rich, information-poor’ (DRIP). MRED - benthic interactions may cause changes that are of a sufficient scale to change ecosystem services provision, particularly in terms of fisheries and biodiversity and, via trophic linkages, change the distribution of fish, birds and mammals. The production of DRIPy data should be eliminated and the resources used instead to address relevant questions that are logically bounded in time and space. Efforts should target identifying metrics of change that can be linked to ecosystem function or service provision, particularly where those metrics show strongly non-linear effects in relation to the stressor. Future monitoring should also be designed to contribute towards predictive ecosystem models and be sufficiently robust and understandable to facilitate transparent, auditable and timely decision-making.
Sensitivity analysis and model type evaluation for subsidence above offshore gas reservoirs
This paper describes the results of a sensitivity study conducted to understand how the prediction of subsidence due to gas extraction from offshore gas fields depends on a few key parameters, such as the connection to the adjacent aquifers and the material mechanical properties. The analysis has been performed using an axi-symmetric Finite Element model. A specific gas production field, the Naomi-Pandora gas field in the Northern Adriatic basin, has been assessed in detail. For each of the layers considered in the sand/shale stratification a low value, an intermediate and a high value for the soil stiffness were applied, as determined from oedometer tests, radioactive markers and vertical seismic profiling respectively. The reservoir constitutive behavior has been modeled using different approaches, namely: a linear-elastic, a power law and a Modified Cam Clay model. The study has been performed for different pressure scenarios representing different levels of interaction with adjacent aquifers. The results show the sensitivity of the subsidence bowl as a result of the imposed conditions. These results are compared with the predictions obtained using 3D non-linear elastic and hypoplastic subsidence models of the same gas fields, demonstrating a good agreement. The stiffness of the reservoir is the main factor affecting the surface subsidence. For a gas pressure reduction less than 50 bars the observed seabed subsidence hardly varied for different reservoir material models. The maximum extent of the predicted subsidence bowl (2 cm contour) in 2030 remains far from the Po di Goro parallel and far from the coastline.
Subsidence from geodetic measurements in the Ravenna area
The derivation of subsidence due to a specific cause from geodetic measurements is, in principle, simple, but assumptions implied in the standard approach are never fully correct in practice. Over or underestimation by a factor of up to two may occur. Geodetic measurements alone cannot differentiate between different causes of subsidence. This article describes a modified approach that avoids assumptions on reference point stability and exploits a-priori knowledge of spatial and temporal subsidence patterns. The present integral approach recognizes that geodetic measurements reflect differential, not absolute, vertical displacement of the benchmarks, not of the 'surface', within the area surveyed and recognizes errors that are, or are not correlated in time and/or subsidence in the Ravenna area from the Comune di Ravenna for the period 1982-2002 were revisited.