Publications by category
Journal articles
de Jong SM, Shen Y, de Vries J, Bijnaar G, van Maanen B, Augustinus P, Verweij P (2021). Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm.
International Journal of Applied Earth Observation and Geoinformation,
97, 102293-102293.
Full text.
de Vries J, van Maanen B, Ruessink G, Verweij PA, de Jong SM (2021). Unmixing water and mud: Characterizing diffuse boundaries of subtidal mud banks from individual satellite observations.
International Journal of Applied Earth Observation and Geoinformation,
95, 102252-102252.
Full text.
Xie D, Schwarz C, Bruckner M, Kleinhans M, Urrego D, Zhou Z, Van Maanen B (2020). Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures.
Environmental Research Letters Full text.
Roversi F, van Maanen B, Colonna Rosman PC, Neves CF, Scudelari AC (2020). Numerical Modeling Evaluation of the Impacts of Shrimp Farming Operations on Long-term Coastal Lagoon Morphodynamics.
ESTUARIES AND COASTS,
43(7), 1853-1872.
Author URL.
Full text.
Leuven JRFW, van Maanen B, Lexmond BR, van der Hoek BV, Spruijt MJ, Kleinhans MG (2018). Dimensions of fluvial-tidal meanders: Are they disproportionally large?.
Geology,
46(10), 923-926.
Full text.
Xie D, Tan Y, Chu A, Zhou T, van Maanen B (2018). Distribution Characteristics of the Extreme Storm Tides in the Radial Sand Ridges Area of the South Yellow Sea in China. Journal of Coastal Research, 85, 856-860.
de Haas T, Pierik HJ, van der Spek AJF, Cohen KM, van Maanen B, Kleinhans MG (2018). Holocene evolution of tidal systems in the Netherlands: Effects of rivers, coastal boundary conditions, eco-engineering species, inherited relief and human interference. Earth-Science Reviews, 177, 139-163.
van Maanen B, Sottolichio A (2018). Hydro- and sediment dynamics in the Gironde estuary (France): Sensitivity to seasonal variations in river inflow and sea level rise. Continental Shelf Research, 165, 37-50.
Payo A, Hall JW, French J, Sutherland J, van Maanen B, Nicholls RJ, Reeve DE (2016). Causal Loop Analysis of coastal geomorphological systems. Geomorphology, 256, 36-48.
Nicholls RJ, French JR, van Maanen B (2016). Simulating decadal coastal morphodynamics. Geomorphology, 256, 1-2.
van Maanen B, Nicholls RJ, French JR, Barkwith A, Bonaldo D, Burningham H, Murray AB, Payo A, Sutherland J, Thornhill G, et al (2016). Simulating mesoscale coastal evolution for decadal coastal management: a new framework integrating multiple, complementary modelling approaches.
GEOMORPHOLOGY,
256, 68-80.
Author URL.
van Maanen B, Coco G, Bryan KR (2015). On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences,
471(2180), 20150115-20150115.
Abstract:
On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting
. An ecomorphodynamic model was developed to study how
. Avicennia marina
. mangroves influence channel network evolution in sandy tidal embayments. The model accounts for the effects of mangrove trees on tidal flow patterns and sediment dynamics. Mangrove growth is in turn controlled by hydrodynamic conditions. The presence of mangroves was found to enhance the initiation and branching of tidal channels, partly because the extra flow resistance in mangrove forests favours flow concentration, and thus sediment erosion in between vegetated areas. The enhanced branching of channels is also the result of a vegetation-induced increase in erosion threshold. On the other hand, this reduction in bed erodibility, together with the soil expansion driven by organic matter production, reduces the landward expansion of channels. The ongoing accretion in mangrove forests ultimately drives a reduction in tidal prism and an overall retreat of the channel network. During sea-level rise, mangroves can potentially enhance the ability of the soil surface to maintain an elevation within the upper portion of the intertidal zone, while hindering both the branching and headward erosion of the landward expanding channels. The modelling results presented here indicate the critical control exerted by ecogeomorphological interactions in driving landscape evolution.
.
Abstract.
Brown S, Nicholls RJ, Hanson S, Brundrit G, Dearing JA, Dickson ME, Gallop SL, Gao S, Haigh ID, Hinkel J, et al (2014). Shifting perspectives on coastal impacts and adaptation. Nature Climate Change, 4(9), 752-755.
van Maanen B, Coco G, Bryan KR, Friedrichs CT (2013). Modeling the morphodynamic response of tidal embayments to sea-level rise. Ocean Dynamics, 63(11-12), 1249-1262.
van Maanen B, Coco G, Bryan KR (2013). Modelling the effects of tidal range and initial bathymetry on the morphological evolution of tidal embayments. Geomorphology, 191, 23-34.
Coco G, Zhou Z, van Maanen B, Olabarrieta M, Tinoco R, Townend I (2013). Morphodynamics of tidal networks: Advances and challenges. Marine Geology, 346, 1-16.
van Maanen B, Coco G, Bryan KR (2011). A numerical model to simulate the formation and subsequent evolution of tidal channel networks. Australian Journal of Civil Engineering, 9(1), 61-72.
van Maanen B, Coco G, Bryan KR, Ruessink BG (2010). The use of artificial neural networks to analyze and predict alongshore sediment transport.
Nonlinear Processes in Geophysics,
17(5), 395-404.
Abstract:
The use of artificial neural networks to analyze and predict alongshore sediment transport
Abstract. An artificial neural network (ANN) was developed to predict the depth-integrated alongshore suspended sediment transport rate using 4 input variables (water depth, wave height and period, and alongshore velocity). The ANN was trained and validated using a dataset obtained on the intertidal beach of Egmond aan Zee, the Netherlands. Root-mean-square deviation between observations and predictions was calculated to show that, for this specific dataset, the ANN (εrms=0.43) outperforms the commonly used Bailard (1981) formula (εrms=1.63), even when this formula is calibrated (εrms=0.66). Because of correlations between input variables, the predictive quality of the ANN can be improved further by considering only 3 out of the 4 available input variables (εrms=0.39). Finally, we use the partial derivatives method to "open and lighten" the generated ANNs with the purpose of showing that, although specific to the dataset in question, they are not "black-box" type models and can be used to analyze the physical processes associated with alongshore sediment transport. In this case, the alongshore component of the velocity, by itself or in combination with other input variables, has the largest explanatory power. Moreover, the behaviour of the ANN indicates that predictions can be unphysical and therefore unreliable when the input lies outside the parameter space over which the ANN has been developed. Our approach of combining the strong predictive power of ANNs with "lightening" the black box and testing its sensitivity, demonstrates that the use of an ANN approach can result in the development of generalized models of suspended sediment transport.
.
Abstract.
van Maanen B, de Ruiter PJ, Ruessink BG (2009). An evaluation of two alongshore transport equations with field measurements. Coastal Engineering, 56(3), 313-319.
van Maanen B, de Ruiter PJ, Coco G, Bryan KR, Ruessink BG (2008). Onshore sandbar migration at Tairua Beach (New Zealand): Numerical simulations and field measurements. Marine Geology, 253(3-4), 99-106.
Van Maanen B, Coco G, Swales A, Bryan KR (2008). The role of biomorphodynamics in estuarine evolution in New Zealand. New Zealand Geographer, 64(2), 162-164.
Publications by year
2021
de Jong SM, Shen Y, de Vries J, Bijnaar G, van Maanen B, Augustinus P, Verweij P (2021). Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm.
International Journal of Applied Earth Observation and Geoinformation,
97, 102293-102293.
Full text.
de Vries J, van Maanen B, Ruessink G, Verweij PA, de Jong SM (2021). Unmixing water and mud: Characterizing diffuse boundaries of subtidal mud banks from individual satellite observations.
International Journal of Applied Earth Observation and Geoinformation,
95, 102252-102252.
Full text.
2020
Xie D, Schwarz C, Bruckner M, Kleinhans M, Urrego D, Zhou Z, Van Maanen B (2020). Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures.
Environmental Research Letters Full text.
Roversi F, van Maanen B, Colonna Rosman PC, Neves CF, Scudelari AC (2020). Numerical Modeling Evaluation of the Impacts of Shrimp Farming Operations on Long-term Coastal Lagoon Morphodynamics.
ESTUARIES AND COASTS,
43(7), 1853-1872.
Author URL.
Full text.
2018
Leuven JRFW, van Maanen B, Lexmond BR, van der Hoek BV, Spruijt MJ, Kleinhans MG (2018). Dimensions of fluvial-tidal meanders: Are they disproportionally large?.
Geology,
46(10), 923-926.
Full text.
Xie D, Tan Y, Chu A, Zhou T, van Maanen B (2018). Distribution Characteristics of the Extreme Storm Tides in the Radial Sand Ridges Area of the South Yellow Sea in China. Journal of Coastal Research, 85, 856-860.
de Haas T, Pierik HJ, van der Spek AJF, Cohen KM, van Maanen B, Kleinhans MG (2018). Holocene evolution of tidal systems in the Netherlands: Effects of rivers, coastal boundary conditions, eco-engineering species, inherited relief and human interference. Earth-Science Reviews, 177, 139-163.
van Maanen B, Sottolichio A (2018). Hydro- and sediment dynamics in the Gironde estuary (France): Sensitivity to seasonal variations in river inflow and sea level rise. Continental Shelf Research, 165, 37-50.
2016
Payo A, Hall JW, French J, Sutherland J, van Maanen B, Nicholls RJ, Reeve DE (2016). Causal Loop Analysis of coastal geomorphological systems. Geomorphology, 256, 36-48.
Nicholls RJ, French JR, van Maanen B (2016). Simulating decadal coastal morphodynamics. Geomorphology, 256, 1-2.
van Maanen B, Nicholls RJ, French JR, Barkwith A, Bonaldo D, Burningham H, Murray AB, Payo A, Sutherland J, Thornhill G, et al (2016). Simulating mesoscale coastal evolution for decadal coastal management: a new framework integrating multiple, complementary modelling approaches.
GEOMORPHOLOGY,
256, 68-80.
Author URL.
2015
van Maanen B, Coco G, Bryan KR (2015). On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences,
471(2180), 20150115-20150115.
Abstract:
On the ecogeomorphological feedbacks that control tidal channel network evolution in a sandy mangrove setting
. An ecomorphodynamic model was developed to study how
. Avicennia marina
. mangroves influence channel network evolution in sandy tidal embayments. The model accounts for the effects of mangrove trees on tidal flow patterns and sediment dynamics. Mangrove growth is in turn controlled by hydrodynamic conditions. The presence of mangroves was found to enhance the initiation and branching of tidal channels, partly because the extra flow resistance in mangrove forests favours flow concentration, and thus sediment erosion in between vegetated areas. The enhanced branching of channels is also the result of a vegetation-induced increase in erosion threshold. On the other hand, this reduction in bed erodibility, together with the soil expansion driven by organic matter production, reduces the landward expansion of channels. The ongoing accretion in mangrove forests ultimately drives a reduction in tidal prism and an overall retreat of the channel network. During sea-level rise, mangroves can potentially enhance the ability of the soil surface to maintain an elevation within the upper portion of the intertidal zone, while hindering both the branching and headward erosion of the landward expanding channels. The modelling results presented here indicate the critical control exerted by ecogeomorphological interactions in driving landscape evolution.
.
Abstract.
2014
Brown S, Nicholls RJ, Hanson S, Brundrit G, Dearing JA, Dickson ME, Gallop SL, Gao S, Haigh ID, Hinkel J, et al (2014). Shifting perspectives on coastal impacts and adaptation. Nature Climate Change, 4(9), 752-755.
2013
van Maanen B, Coco G, Bryan KR, Friedrichs CT (2013). Modeling the morphodynamic response of tidal embayments to sea-level rise. Ocean Dynamics, 63(11-12), 1249-1262.
van Maanen B, Coco G, Bryan KR (2013). Modelling the effects of tidal range and initial bathymetry on the morphological evolution of tidal embayments. Geomorphology, 191, 23-34.
Coco G, Zhou Z, van Maanen B, Olabarrieta M, Tinoco R, Townend I (2013). Morphodynamics of tidal networks: Advances and challenges. Marine Geology, 346, 1-16.
2011
van Maanen B, Coco G, Bryan KR (2011). A numerical model to simulate the formation and subsequent evolution of tidal channel networks. Australian Journal of Civil Engineering, 9(1), 61-72.
2010
van Maanen B, Coco G, Bryan KR, Ruessink BG (2010). The use of artificial neural networks to analyze and predict alongshore sediment transport.
Nonlinear Processes in Geophysics,
17(5), 395-404.
Abstract:
The use of artificial neural networks to analyze and predict alongshore sediment transport
Abstract. An artificial neural network (ANN) was developed to predict the depth-integrated alongshore suspended sediment transport rate using 4 input variables (water depth, wave height and period, and alongshore velocity). The ANN was trained and validated using a dataset obtained on the intertidal beach of Egmond aan Zee, the Netherlands. Root-mean-square deviation between observations and predictions was calculated to show that, for this specific dataset, the ANN (εrms=0.43) outperforms the commonly used Bailard (1981) formula (εrms=1.63), even when this formula is calibrated (εrms=0.66). Because of correlations between input variables, the predictive quality of the ANN can be improved further by considering only 3 out of the 4 available input variables (εrms=0.39). Finally, we use the partial derivatives method to "open and lighten" the generated ANNs with the purpose of showing that, although specific to the dataset in question, they are not "black-box" type models and can be used to analyze the physical processes associated with alongshore sediment transport. In this case, the alongshore component of the velocity, by itself or in combination with other input variables, has the largest explanatory power. Moreover, the behaviour of the ANN indicates that predictions can be unphysical and therefore unreliable when the input lies outside the parameter space over which the ANN has been developed. Our approach of combining the strong predictive power of ANNs with "lightening" the black box and testing its sensitivity, demonstrates that the use of an ANN approach can result in the development of generalized models of suspended sediment transport.
.
Abstract.
2009
van Maanen B, de Ruiter PJ, Ruessink BG (2009). An evaluation of two alongshore transport equations with field measurements. Coastal Engineering, 56(3), 313-319.
2008
van Maanen B, de Ruiter PJ, Coco G, Bryan KR, Ruessink BG (2008). Onshore sandbar migration at Tairua Beach (New Zealand): Numerical simulations and field measurements. Marine Geology, 253(3-4), 99-106.
Van Maanen B, Coco G, Swales A, Bryan KR (2008). The role of biomorphodynamics in estuarine evolution in New Zealand. New Zealand Geographer, 64(2), 162-164.
