Operational storm surge forecasting in the Netherlands: developments in the last decade

Verlaan, Martin; Zijderveld, Annette; Vries, H. de; Kroos, J.

doi:10.1098/rsta.2005.1578

Cited by 77 publications

(61 citation statements)

References 9 publications

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“…Despite some uncertainties related to the prediction of storm surges, extreme water levels can be modelled with reasonable results (e.g. Pugh, 1996;Flather et al, 1998;Verlaan et al, 2005;Brown et al, 2010). Additional uncertainties arise when the impacts of climate change are included in the predictions of return periods of extreme sea levels (e.g.…”

Section: Introductionmentioning

confidence: 99%

Consequences to flood management of using different probability distributions to estimate extreme rainfall

Esteves

2013

Journal of Environmental Management

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The design of flood defences, such as pumping stations, takes into consideration the predicted return periods of extreme precipitation depths. Most commonly these are estimated by fitting the Generalised Extreme Value (GEV) or the Generalised Pareto (GP) probability distributions to the annual maxima series or to the partial duration series. In this paper, annual maxima series of precipitation depths obtained from daily rainfall data measured at three selected stations in southeast UK are analysed using a range of probability distributions. These analyses demonstrate that GEV or GP distributions do not always provide the best fit to the data, and that extreme rainfall estimates for long return periods (e.g. 1 in 100 years) can differ by more than 40% depending on the distribution model used. Since a large number of properties in the UK and elsewhere currently benefit from flood defences designed using the GEV or GP probability distributions, the results from this study question whether the level of protection they offer are appropriate in locations where data demonstrate clearly that alternative probability distributions may have a better fit to the local rainfall data. This work: (a) raises awareness of the limitations of common practices in extreme rainfall analysis; (b) suggests a simple way forward to incorporate uncertainties that is easily applicable to local rainfall data worldwide; and thus (c) contributes to improve flood risk management.

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Section: Introductionmentioning

confidence: 99%

Consequences to flood management of using different probability distributions to estimate extreme rainfall

Esteves

2013

Journal of Environmental Management

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“…Later automated calibration procedures based on variational data assimilation were developed (TenBrummelhuis et al 1993;Mouthaan et al 1994). The complete description on the development of these calibrated procedures for DCSM can be found in Verlaan et al (2005).…”

Section: Estimation Of Depthmentioning

confidence: 99%

Simultaneous perturbation stochastic approximation for tidal models

et al. 2011

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The Dutch continental shelf model (DCSM) is a shallow sea model of entire continental shelf which is used operationally in the Netherlands to forecast the storm surges in the North Sea. The forecasts are necessary to support the decision of the timely closure of the moveable storm surge barriers to protect the land. In this study, an automated model calibration method, simultaneous perturbation stochastic approximation (SPSA) is implemented for tidal calibration of the DCSM. The method uses objective function evaluations to obtain the gradient approximations. The gradient approximation for the central difference method uses only two objective function evaluation independent of the number of parameters being optimized. The calibration parameter in this study is the model bathymetry. A number of calibration experiments is performed. The effectiveness of the algorithm is evaluated in terms of the accuracy of the final results as well as the computational costs required to produce these results. In doing so, comparison is made with a traditional steepest descent method and also with a newly developed proper orthogonal decompositionbased calibration method. The SPSA method gives comparable results to steepest descent method with little computational cost. (2) The SPSA method with little computational cost can be used to estimate large number of parameters.

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“…Moreover, operational surge models whose spatial resolution is about a few kilometers use either a constant sea surface drag coefficient or those issued from Wu (1982), Smith and Banke (1975), or Charnock (1955) formulations without taking into account the sea state. For instance, for WAQUA-IN-Simona/DCSM98 (Verlaan et al 2005), DaMSA (Büchmann et al 2011), and Previmar (Le Breton and Perherin 2009) systems, root mean square errors are about 5 to 15 cm for period covering a few days or several years. In France, in addition to Previmar, a forecast system has been developed (PREVIMER www.previmer.org) (Lecornu and De Roeck 2009) providing water levels and surges along the French Atlantic and English Channel coast.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric storm surge modeling methodology along the French (Atlantic and English Channel) coast

et al. 2014

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Storm surge modeling and forecast are the key issues for coastal risk early warning systems. As a general objective, this study aims at improving high-frequency storm surge variations modeling within the PREVIMER system (www.previmer.org), along the French Atlantic and English Channel coasts. The paper focuses on (1) sea surface drag parameterization and (2) uncertainties induced by the meteorological data quality. The modeling is based on the shallowwater version of the model for applications at regional scale (MARS), with a 2-km spatial resolution. The model computes together tide and surge, allowing properly taking into account tide-surge interactions. To select the most appropriate parameterization for the study area, a sensitivity analysis on sea surface drag parameterizations is done, based on comparisons of modeled storm surges (extracted with a tidal component analysis) with four tidal gauges, during four storm events, and over about 7.5 years, where the observed water level is processed in the same way as the modeling results. The tested drag parameterizations are a constant one, as reported by Moon et al. (J Atmos Sci 61: 2321-2333, 2007, Makin (BoundLayer Meteorol 115: 169-176, 2005), and Charnock (J Roy Meteor Soc 81: 639-640, 1955). Charnock's parameterization, either constant with high value (0.022) or relying on a full statistical description of the sea state, enables to improve storm surges forecast with peak errors 10 cm smaller than those computed with the other drag coefficient formulations. The impact of the meteorological forcing quality is evaluated over January 2012 from the comparison between surges modeled with different meteorological data (ARPEGE, ARPEGE High Resolution and AROME) and observations. For event time scale, storm surge computation is highly improved with ARPEGE High Resolution data. For month time scale, statistics of model accuracy are less sensitive to the choice of meteorological forcing. As a conclusion, the Charnock's parameterization is advised to model storm surges on the French Atlantic and English Channel coasts, whereas the quality requirements regarding meteorological inputs depend on the time scale of interest. Within the PREVIMER system, aiming at forecasting events, ARPEGE High Resolution data are used.

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Operational storm surge forecasting in the Netherlands: developments in the last decade

Cited by 77 publications

References 9 publications

Consequences to flood management of using different probability distributions to estimate extreme rainfall

Consequences to flood management of using different probability distributions to estimate extreme rainfall

Simultaneous perturbation stochastic approximation for tidal models

Atmospheric storm surge modeling methodology along the French (Atlantic and English Channel) coast

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