A Regional Application of Bayesian Modeling for Coastal Erosion and Sand Nourishment Management
by
Alessio Giardino 1,*, Eleni Diamantidou 2, Stuart Pearson 1,3
, Giorgio Santinelli 1 and Kees Den Heijer 1,3
, Giorgio Santinelli 1 and Kees Den Heijer 1,3
1
Deltares, Unit Marine and Coastal Systems, Boussinesweg 1, 2629 HV Delft, The Netherlands
2
Van Oort, Schaardijk 211, 3063 NH Rotterdam, The Netherlands
3
Civil Engineering and Geoscience, Delft University of Technology, 2628 CN Delft, The Netherlands
*
Author to whom correspondence should be addressed.
Water 2019, 11(1), 61; https://doi.org/10.3390/w11010061
Received: 26 September 2018 / Revised: 13 December 2018 / Accepted: 22 December 2018 / Published: 1 January 2019
(This article belongs to the Special Issue Coastal Vulnerability and Mitigation Strategies: From Monitoring to Applied Research)
This paper presents an application of the Bayesian belief network for coastal erosion management at the regional scale. A “Bayesian ERosion Management Network” (BERM-N) is developed and trained based on yearly cross-shore profile data available along the Holland coast. Profiles collected for over 50 years and at 604 locations were combined with information on different sand nourishment types (i.e., beach, dune, and shoreface) and volumes implemented during the analyzed time period. The network was used to assess the effectiveness of nourishments in mitigating coastal erosion. The effectiveness of nourishments was verified using two coastal state indicators, namely the momentary coastline position and the dune foot position. The network shows how the current nourishment policy is effective in mitigating the past erosive trends. While the effect of beach nourishment was immediately visible after implementation, the effect of shoreface nourishment reached its maximum only 5–10 years after implementation of the nourishments. The network can also be used as a predictive tool to estimate the required nourishment volume in order to achieve a predefined coastal erosion management objective. The network is interactive and flexible and can be trained with any data type derived from measurements as well as numerical models.
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Keywords:
BERM-N; coastal erosion; sea level rise; sand nourishments; Bayesian belief network; JarKus data; coastal state indicators; dune foot; momentary coastline; Holland coast
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MDPI and ACS Style
Giardino, A.; Diamantidou, E.; Pearson, S.; Santinelli, G.; Den Heijer, K. A Regional Application of Bayesian Modeling for Coastal Erosion and Sand Nourishment Management. Water 2019, 11, 61.
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