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Tides are a key component in coastal extreme water levels. Possible changes in the tides caused by mean sea-level rise (SLR) are therefore of importance in the analysis of coastal flooding, as well as many other applications. We investigate the effect of future SLR on the tides globally using a fully global forward tidal model: OTISmpi. Statistical comparisons of the modelled and observed tidal solutions demonstrate the skill of the refined model setup with no reliance on data assimilation. We simulate the response of the four primary tidal constituents to various SLR scenarios. Particular attention is paid to future changes at the largest 136 coastal cities, where changes in water level would have the greatest impact. Spatially uniform SLR scenarios ranging from 0.5 to 10 m with fixed coastlines show that the tidal amplitudes in shelf seas globally respond strongly to SLR with spatially coherent areas of increase and decrease. Changes in the M2 and S2 constituents occur globally in most shelf seas, whereas changes in K1 and O1 are confined to Asian shelves. With higher SLR tidal changes are often not proportional to the SLR imposed and larger portions of mean high water (MHW) changes are above proportional. Changes in MHW exceed ±10% of the SLR at ~10% of coastal cities. SLR scenarios allowing for coastal recession tend increasingly to result in a reduction in tidal range. The fact that the fixed and recession shoreline scenarios result mainly in changes of opposing sign is explained by the effect of the perturbations on the natural period of oscillation of the basin. Our results suggest that coastal management strategies could influence the sign of the tidal amplitude change. The effect of a spatially varying SLR, in this case fingerprints of the initial elastic response to ice mass loss, modestly alters the tidal response with the largest differences at high latitudes., Mathematical Physics

In this paper, we present a novel Kalman filter approach to combine a hydrodynamic model-derived lowest astronomical tide (LAT) surface with tide gauge record-derived LAT values. In the approach, tidal water levels are assimilated into the model. As such, the combination is guided by the model physics. When validating the obtained “Kalman-filtered LAT realization” at all tide gauges, we obtained an overall root-mean-square (RMS) difference of 15.1 cm. At the tide gauges not used in the data assimilation, the RMS is 17.9 cm. We found that the assimilation reduces the overall RMS difference by ∼31% and ∼22%, respectively. In the Dutch North Sea and Wadden Sea, the RMS differences are 6.6 and 14.8 cm (all tide gauges), respectively. Furthermore, we address the problem of LAT realization in intertidal waters where LAT is not defined. We propose to replace LAT by pseudo-LAT, which we suggest to realize similarly as LAT except that all water level boundary conditions and assimilated tidal water levels have to be enlarged by a constant value that is removed afterwards. Using this approach, we obtained a smooth reference surface for the Dutch Wadden Sea that fits LAT at the North Sea boundary within a few centimeters., Physical and Space Geodesy

Water-related disasters, such as fluvial floods and cyclonic storm surges, are a major concern in the world's mega-delta regions. Furthermore, the simultaneous occurrence of extreme discharges from rivers and storm surges could exacerbate flood risk, compared to when they occur separately. Hence, it is of great importance to assess the compound risks of fluvial and coastal floods at a large scale, including mega-deltas. However, most studies on compound fluvial and coastal flooding have been limited to relatively small scales, and global-scale or large-scale studies have not yet addressed both of them. The objectives of this study are twofold: to develop a global coupled river-coast flood model; and to conduct a simulation of compound fluvial flooding and storm surges in Asian mega-delta regions. A state-of-the-art global river routing model was modified to represent the influence of dynamic sea surface levels on river discharges and water levels. We conducted the experiments by coupling a river model with a global tide and surge reanalysis data set. Results show that water levels in deltas and estuaries are greatly affected by the interaction between river discharge, ocean tides and storm surges. The effects of storm surges on fluvial flooding are further examined from a regional perspective, focusing on the case of Cyclone Sidr in the Ganges-Brahmaputra-Meghna Delta in 2007. Modeled results demonstrate that a >3 m storm surge propagated more than 200 km inland along rivers. We show that the performance of global river routing models can be improved by including sea level dynamics., Mathematical Physics

One of the primary observational data sets of sea level is represented by the tide gauge record. We propose a new method to estimate variability on decadal time scales from tide gauge data by using a state space formulation, which couples the direct observations to a predefined state space model by using a Kalman filter. The model consists of a time-varying trend and seasonal cycle, and variability induced by several physical processes, such as wind, atmospheric pressure changes and teleconnection patterns. This model has two advantages over the classical least-squares method that uses regression to explain variations due to known processes: a seasonal cycle with time-varying phase and amplitude can be estimated, and the trend is allowed to vary over time. This time-varying trend consists of a secular trend and low-frequency variability that is not explained by any other term in the model. As a test case, we have used tide gauge data from stations around the North Sea over the period 1980-2013. We compare a model that only estimates a trend with two models that also remove intra-annual variability: one by means of time series of wind stress and sea level pressure, and one by using a two-dimensional hydrodynamic model. The last two models explain a large part of the variability, which significantly improves the accuracy of the estimated time-varying trend. The best results are obtained with the hydrodynamic model. We find a consistent low-frequency sea level signal in the North Sea, which can be linked to a steric signal over the northeastern part of the Atlantic., Physical and Space Geodesy, Mathematical Physics

Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

One of the primary observational data sets of sea level is represented by the tide gauge record. We propose a new method to estimate variability on decadal time scales from tide gauge data by using a state space formulation, which couples the direct observations to a predefined state space model by using a Kalman filter. The model consists of a time-varying trend and seasonal cycle, and variability induced by several physical processes, such as wind, atmospheric pressure changes and teleconnection patterns. This model has two advantages over the classical least-squares method that uses regression to explain variations due to known processes: a seasonal cycle with time-varying phase and amplitude can be estimated, and the trend is allowed to vary over time. This time-varying trend consists of a secular trend and low-frequency variability that is not explained by any other term in the model. As a test case, we have used tide gauge data from stations around the North Sea over the period 1980-2013. We compare a model that only estimates a trend with two models that also remove intra-annual variability: one by means of time series of wind stress and sea level pressure, and one by using a two-dimensional hydrodynamic model. The last two models explain a large part of the variability, which significantly improves the accuracy of the estimated time-varying trend. The best results are obtained with the hydrodynamic model. We find a consistent low-frequency sea level signal in the North Sea, which can be linked to a steric signal over the northeastern part of the Atlantic., Physical and Space Geodesy, Mathematical Physics

Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

Water Management, Civil Engineering and Geosciences

Water Management, Civil Engineering and Geosciences

Variational data assimilation, also sometimes simply called the ‘adjoint method’, is used very often for large scale model calibration problems. Using the available data, the uncertain parameters in the model are identified by minimizing a certain cost function that measures the difference between the model results and the data. A variational scheme requires the implementation of the adjoint of (the tangent linear approximation of) the original model which is a tremendous programming effort, that hampers new applications of the method. Recently a new ensemble approach to variational inverse modelling using Proper Orthogonal Decomposition (POD) model reduction has been proposed that does not require the implementation of the adjoint model. Using an ensemble of forward model simulations an approximation of the covariance matrix of the model variability is determined. A limited number of leading eigenvectors of this matrix are selected to define a model sub space. By projecting the original model onto this subspace an approximate linear model is obtained. Once this reduced model is available the minimization process can be solved completely in reduced space with negligible computational costs. Schemes based on the well-known Kalman filtering algorithm are also used recently for inverse modeling. The last years a number of ensemble based algorithms have been proposed, e.g., the Ensemble Kalman filter (EnKF), the Reduced Rank Square Root filter (RRSQRT) and the Ensemble Square Root filter (ESRF). Although introduced for linear state estimation, these new algorithms 102 Ensemble methods for large scale inverse problems are able to handle nonlinear models accurately and, therefore, are very attractive for solving combined state and parameter estimation problems. It has been shown recently that the so-called symmetric version of the ESRF introduces the smallest increments and, therefore, is in most applications more accurate then the original version of this algorithm.

Variational data assimilation, also sometimes simply called the ‘adjoint method’, is used very often for large scale model calibration problems. Using the available data, the uncertain parameters in the model are identified by minimizing a certain cost function that measures the difference between the model results and the data. A variational scheme requires the implementation of the adjoint of (the tangent linear approximation of) the original model which is a tremendous programming effort, that hampers new applications of the method. Recently a new ensemble approach to variational inverse modelling using Proper Orthogonal Decomposition (POD) model reduction has been proposed that does not require the implementation of the adjoint model. Using an ensemble of forward model simulations an approximation of the covariance matrix of the model variability is determined. A limited number of leading eigenvectors of this matrix are selected to define a model sub space. By projecting the original model onto this subspace an approximate linear model is obtained. Once this reduced model is available the minimization process can be solved completely in reduced space with negligible computational costs. Schemes based on the well-known Kalman filtering algorithm are also used recently for inverse modeling. The last years a number of ensemble based algorithms have been proposed, e.g., the Ensemble Kalman filter (EnKF), the Reduced Rank Square Root filter (RRSQRT) and the Ensemble Square Root filter (ESRF). Although introduced for linear state estimation, these new algorithms 102 Ensemble methods for large scale inverse problems are able to handle nonlinear models accurately and, therefore, are very attractive for solving combined state and parameter estimation problems. It has been shown recently that the so-called symmetric version of the ESRF introduces the smallest increments and, therefore, is in most applications more accurate then the original version of this algorithm., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

Miscible solvent injection has received increasing attention in recent years as an efficient method to improve oil recovery from fractured reservoirs. Due to the large permeability difference between fracture and matrix, the success of this method depends to large extent on the degree of enhancement of the mass exchange rate between the solvent flowing through the fracture and the oil residing in the matrix. A series of experiments have been conducted to investigate the mass transfer rate between the fracture and the matrix. Different scenarios have been considered to examine the effect of flow rate, matrix permeability, fracture aperture, and oil properties. To this end a porous medium (fully saturated with oil) is placed in a vertical core holder that can be used in a CT scanner, to simulate the matrix. A small slit between the porous medium and the core holder simulates the fracture. The interaction between the matrix and fracture is visualized for solvent flooding by means of CT-Scanning, which can be used to validate theories of enhanced transfer in fractured media. The experimental data are compared with a simulation model that takes diffusive, gravitational and convective forces into account., Geoscience & Engineering, Civil Engineering and Geosciences

Variational data assimilation, also sometimes simply called the ‘adjoint method’, is used very often for large scale model calibration problems. Using the available data, the uncertain parameters in the model are identified by minimizing a certain cost function that measures the difference between the model results and the data. A variational scheme requires the implementation of the adjoint of (the tangent linear approximation of) the original model which is a tremendous programming effort, that hampers new applications of the method. Recently a new ensemble approach to variational inverse modelling using Proper Orthogonal Decomposition (POD) model reduction has been proposed that does not require the implementation of the adjoint model. Using an ensemble of forward model simulations an approximation of the covariance matrix of the model variability is determined. A limited number of leading eigenvectors of this matrix are selected to define a model sub space. By projecting the original model onto this subspace an approximate linear model is obtained. Once this reduced model is available the minimization process can be solved completely in reduced space with negligible computational costs. Schemes based on the well-known Kalman filtering algorithm are also used recently for inverse modeling. The last years a number of ensemble based algorithms have been proposed, e.g., the Ensemble Kalman filter (EnKF), the Reduced Rank Square Root filter (RRSQRT) and the Ensemble Square Root filter (ESRF). Although introduced for linear state estimation, these new algorithms 102 Ensemble methods for large scale inverse problems are able to handle nonlinear models accurately and, therefore, are very attractive for solving combined state and parameter estimation problems. It has been shown recently that the so-called symmetric version of the ESRF introduces the smallest increments and, therefore, is in most applications more accurate then the original version of this algorithm.

Variational data assimilation, also sometimes simply called the ‘adjoint method’, is used very often for large scale model calibration problems. Using the available data, the uncertain parameters in the model are identified by minimizing a certain cost function that measures the difference between the model results and the data. A variational scheme requires the implementation of the adjoint of (the tangent linear approximation of) the original model which is a tremendous programming effort, that hampers new applications of the method. Recently a new ensemble approach to variational inverse modelling using Proper Orthogonal Decomposition (POD) model reduction has been proposed that does not require the implementation of the adjoint model. Using an ensemble of forward model simulations an approximation of the covariance matrix of the model variability is determined. A limited number of leading eigenvectors of this matrix are selected to define a model sub space. By projecting the original model onto this subspace an approximate linear model is obtained. Once this reduced model is available the minimization process can be solved completely in reduced space with negligible computational costs. Schemes based on the well-known Kalman filtering algorithm are also used recently for inverse modeling. The last years a number of ensemble based algorithms have been proposed, e.g., the Ensemble Kalman filter (EnKF), the Reduced Rank Square Root filter (RRSQRT) and the Ensemble Square Root filter (ESRF). Although introduced for linear state estimation, these new algorithms 102 Ensemble methods for large scale inverse problems are able to handle nonlinear models accurately and, therefore, are very attractive for solving combined state and parameter estimation problems. It has been shown recently that the so-called symmetric version of the ESRF introduces the smallest increments and, therefore, is in most applications more accurate then the original version of this algorithm., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

Miscible solvent injection has received increasing attention in recent years as an efficient method to improve oil recovery from fractured reservoirs. Due to the large permeability difference between fracture and matrix, the success of this method depends to large extent on the degree of enhancement of the mass exchange rate between the solvent flowing through the fracture and the oil residing in the matrix. A series of experiments have been conducted to investigate the mass transfer rate between the fracture and the matrix. Different scenarios have been considered to examine the effect of flow rate, matrix permeability, fracture aperture, and oil properties. To this end a porous medium (fully saturated with oil) is placed in a vertical core holder that can be used in a CT scanner, to simulate the matrix. A small slit between the porous medium and the core holder simulates the fracture. The interaction between the matrix and fracture is visualized for solvent flooding by means of CT-Scanning, which can be used to validate theories of enhanced transfer in fractured media. The experimental data are compared with a simulation model that takes diffusive, gravitational and convective forces into account., Geoscience & Engineering, Civil Engineering and Geosciences

The North Sea is one of the busiest seas in the world with dense ship traffic, fisheries, wind farming, recreation and many other activities. All these activities depend on the ‘marine weather’. Accurate forecasts of waves, currents and sea level are crucial for operational management and for issuing warnings during extreme events. The current operational forecasting system in the Netherlands has been operational since the 90’s. Over the last few years, a new generation forecasting system for the North Sea and the Dutch Seacoastal waters has been developed. This involved both advanced model development as well as the set-up of an operational forecasting shell based on the FEWS (Flood Early Warning System) framework. From the start of the storm season on September 1st 2012, this new system will be used pre-operationally by Rijkswaterstaat (Dutch Ministry of Infrastructure and the Environment) and the Royal Netherlands Meteorological Institute, KNMI. This paper gives an overview of the many developments that are needed for an accurate and reliable forecasting system. In addition to accurate and robust numerical models, one needs scheduling with backup options, quality control of observations, good interactive displays of many datasets, manual override for operators, and much more. The quality of the operational forecasts is as strong as the weakest link., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

In this study, LAT will be modeled relative to a geoid, after which the ellipsoidal heights of LAT will be obtained by adding geoid heights to the modeled LAT values. The use of a geoid instead of MSL has the advantage that the former can be realized everywhere and does not leave a gap along the coast as satellite radar altimetry does. Modeling LAT relative to a geoid is realized by i) the explicit modeling of the average meteorological and steric conditions as required by the definition of LAT and ii) referencing the hydrodynamic model to the chosen geoid. Another advantage of this approach is that it is conceptually consistent; unlike MSL, both the geoid and the model’s reference surface are equipotential surfaces. Moreover, this approach allows for the inclusion of temporal variations in the definition of the average meteorological conditions. The latter reason follows from a precise interpretation of the LAT definition that the average meteorological conditions in spring and those in fall should be included separately., Geoscience and Remote Sensing, Civil Engineering and Geosciences

The North Sea is one of the busiest seas in the world with dense ship traffic, fisheries, wind farming, recreation and many other activities. All these activities depend on the ‘marine weather’. Accurate forecasts of waves, currents and sea level are crucial for operational management and for issuing warnings during extreme events. The current operational forecasting system in the Netherlands has been operational since the 90’s. Over the last few years, a new generation forecasting system for the North Sea and the Dutch Seacoastal waters has been developed. This involved both advanced model development as well as the set-up of an operational forecasting shell based on the FEWS (Flood Early Warning System) framework. From the start of the storm season on September 1st 2012, this new system will be used pre-operationally by Rijkswaterstaat (Dutch Ministry of Infrastructure and the Environment) and the Royal Netherlands Meteorological Institute, KNMI. This paper gives an overview of the many developments that are needed for an accurate and reliable forecasting system. In addition to accurate and robust numerical models, one needs scheduling with backup options, quality control of observations, good interactive displays of many datasets, manual override for operators, and much more. The quality of the operational forecasts is as strong as the weakest link., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

In this study, LAT will be modeled relative to a geoid, after which the ellipsoidal heights of LAT will be obtained by adding geoid heights to the modeled LAT values. The use of a geoid instead of MSL has the advantage that the former can be realized everywhere and does not leave a gap along the coast as satellite radar altimetry does. Modeling LAT relative to a geoid is realized by i) the explicit modeling of the average meteorological and steric conditions as required by the definition of LAT and ii) referencing the hydrodynamic model to the chosen geoid. Another advantage of this approach is that it is conceptually consistent; unlike MSL, both the geoid and the model’s reference surface are equipotential surfaces. Moreover, this approach allows for the inclusion of temporal variations in the definition of the average meteorological conditions. The latter reason follows from a precise interpretation of the LAT definition that the average meteorological conditions in spring and those in fall should be included separately., Geoscience and Remote Sensing, Civil Engineering and Geosciences

De stormvloed van 1 november 2006, de Allerheiligenvloed genaamd, heeft na analyse van de waterstandsverwachting, de golfmetingen bij Schiermonnikoog Noord (SON), de veekranden en de duinafslag, geleid tot aanbevelingen voor het informatie- en kennisontwikkelingsproces. Het betreft aanbevelingen voor het instrumentarium van de SVSD, voor het inwinnen en interpreteren van gegevens en voor vervolgactiviteiten naar aanleiding van de gegevens van de Allerheiligenvloed 2006. In het volgende worden de conclusies voor het gebeurde tijdens de Allerheiligenvloed en de aanbevelingen voor het vervolg kort samengevat.

De stormvloed van 1 november 2006, de Allerheiligenvloed genaamd, heeft na analyse van de waterstandsverwachting, de golfmetingen bij Schiermonnikoog Noord (SON), de veekranden en de duinafslag, geleid tot aanbevelingen voor het informatie- en kennisontwikkelingsproces. Het betreft aanbevelingen voor het instrumentarium van de SVSD, voor het inwinnen en interpreteren van gegevens en voor vervolgactiviteiten naar aanleiding van de gegevens van de Allerheiligenvloed 2006. In het volgende worden de conclusies voor het gebeurde tijdens de Allerheiligenvloed en de aanbevelingen voor het vervolg kort samengevat.

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science