Next Article in Journal
Relationship between Submerged Marine Debris and Macrobenthic Fauna in Jeju Island, South Korea
Previous Article in Journal
A Numerical Prediction of the Resistance of Bulk Carriers in Brash Ice Channels
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review

School of Marine Sciences, Sun Yat-Sen University, Zhuhai 519082, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(7), 1426;
Submission received: 29 May 2023 / Revised: 11 July 2023 / Accepted: 13 July 2023 / Published: 16 July 2023
(This article belongs to the Section Coastal Engineering)


Coastal and estuarine morphogenetic evolution has been affected by anthropogenic activities. Human activities have become an important external force for the future prediction of morphological evolution in ecosystem health. We have reviewed the existing literature on modelling the impacts of human activities on morphological changes. Three types of approaches (the application of coastal-area morphodynamic models, equilibrium modelling, and machine learning) are introduced collaboratively to complement each other. The Bayes network of machine learning approaches can be used to include the uncertainties of forces and the intrinsic limitations of the models. Future research should consider the bio-morphodynamic effects of human activities, as human activities have significantly damaged the ecosystem. Modelling morphodynamic processes in benthic communities, as well as feedback to morphodynamics, appears to be increasingly important for ecosystem protection and restoration. From the perspective of a longer time span, the feedback of morphodynamics for human activities should be considered in the modelling, which requires better quantification and understanding of human–nature interactions.

1. Introduction

Coastal and estuarine morphogenetic evolution has been affected by anthropogenic activities over the last few decades, e.g., [1]. Thus, human activities have become an important external force for the future prediction of morphogenetic evolution. Human activities, such as land reclamation, have destroyed ecosystem health. For example, the loss of tidal flats due to land reclamation causes damage to the habitat of tidal wetlands, fishing and nursery grounds, and other ecosystem functions [2,3]. Figure 1 shows that the Chinese government’s policy plays an important role in coastline changes in the Pearl River Estuary [4]. Other noticeable human activities are dam construction and reduced sediment supply to the coast and the estuary [5]. Estuarine regulation and management activities, such as in the Modaomen estuaries, have turned the Modaomen embayment into a channel [6]. Human activity has become an important force in coastal and estuarine morphological changes. Understanding the morphodynamic impact of these human activities has attracted the attention of scientists as well as coastal and estuarine managers. From the perspective of ecosystem conservation and restoration, scientists and managers require modelling approaches to assess and predict the impact of anthropogenic activities on coastal and estuarine morphogenetic evolution. Observational data must always be processed for scientific analysis and modelling. Thus, modelling tools, including data-driven modelling, are crucial for scientific studies and management.
There are many review papers about the modelling approaches of coastal and estuarine evolution, but there is a lack of a review about modelling anthropogenic impacts on morphological evolution. [7] reviewed models for coastal and estuarine evolution at decadal-to-centennial scales based on generalised Exner equations. Sediment fluxes and their gradients are critical for the coastline and for submarine morphological evolution. These morphogenetic models could generate results that deviate from reality owing to uncertainties in the prediction of sediment fluxes and incomplete or simplified boundary forcing. Recently, machine learning techniques for artificial intelligence have emerged because of the increasing amount of available observational data and the advancement of artificial intelligence techniques [8]. As anthropogenic forcing has been widely recognised to be non-negligible during the last decades, how to incorporate the modelling of anthropogenic activities in morphological evolution remains a challenge. To date, few studies have attempted to provide a synthesis review of modelling approaches to the impacts of anthropogenic activities on morphological evolution.
In this paper, we summarise the existing literature in which human activities have impacts on coastal morphodynamics and the modelling approaches are implemented. Modelling that incorporates anthropogenic activities in the literature can be classified into three approaches: (1) the application of coastal-area morphodynamic models, (2) equilibrium modelling approaches, and (3) machine learning approaches. This study discusses these three approaches in the following sections. The following section discusses the preconditions for the use of these approaches. Finally, a future prospective summary is provided.

2. Application of Coastal-Area Morphodynamic Models

The most common modelling approaches are coastal-area morphodynamic models such as Delft3D [9] or Telemac modelling system [10] models. The Xbeach model is frequently applied for simulating short-term processes for the beach, dune and barrier due to storm impacts [11]. Direct modelling of human activities and their impacts on morphodynamic evolution is rare in the published literature. Often, the impacts of human activities on the initial and boundary conditions are utilised to run morphodynamic models. For example, the exploratory simplified model of barrier island evolution was applied to a real-world coast with human modifications to topography and sediment fluxes [12]. This approach does not simulate human activities directly, but it uses the observed values of terrain information such as barrier height, island width, and back-barrier depths in human-modified coastal sections. Modelling approaches provide long-term (decadal-to-millennian) evolution with the impacts of human activities.
For the engineering and management timescale, the Delft3D model was applied to simulate morphological changes at different time intervals: the accretion period (1958–1978), erosion period (1986–1997), and accretion period due to human activities (2002–2010) [13]. The first two periods were mainly due to a human-induced reduction in riverine sediment supply, whereas the third period can be attributed to estuarine engineering projects [14]. Anthropogenic-induced sea-level rise can serve as a boundary input for developed coastal-area morphodynamic models [15]. IPCC scenarios of sea-level rise and human activities can be added as boundary forcing for the process-based models for simulating estuarine morphodynamic evolution [16]. In this study, with the established 2DH morphodynamic model for estuarine evolution, the impacts of anthropogenic intervention, such as land reclamation with embankment construction, are modelled with the modification of the coastline boundaries. The model was implemented for a centennial time scale to explore estuarine resilience to sea-level rise under different degrees of land reclamation activities.
Unlike long-term modelling, short-term modelling requires a high accuracy due to short-term morphological changes being small. Furthermore, short-term processes are rapid and instantaneous. For example, the nearshore numerical Xbeach model can simulate the short-term processes of dune erosion, overwashing, and breaching [11]. The Xbeach model has advanced the modelling approaches for dune erosion [17,18] to incorporate vegetation effects for natural-based solutions [19]. Beach-dune systems are often observed at most beaches in the world. However, human influences have strongly modified the natural landscape of the beach-dune systems [12,20]. For example, the existing portions of dune habitat are subjected, particularly during the summer, to intense trampling and degradation due to the uncontrolled access of tourists. Problems with disruption of vegetation cover commonly arise where pedestrians’ and vehicles’ beach access are poorly managed. Furthermore, infrastructures are built behind the beach for tourism. Thus, it is evident that there is an urgent need to preserve such valuable ecosystems from erosion. Therefore, the coastal community is paying attention to finding new environmentally friendly solutions for dune restoration. In this respect, the Xbeach model that includes the simulation of vegetation effects is useful for engineering and management activities. Regarding the short-term impact of human activities on morphodynamic evolution, artificial dunes are incorporated by modifying the initial bathymetry to large elevations at the dune location for the implementation of the Xbeach model [21]. In this study, a natural-based solution with artificial vegetation was also considered to simulate waves and flows in the Xbeach model. The Xbeach model provides an assessment tool for natural-based solutions to protect coastal communities from coastal storms. With the increasing need for ecosystem-based solutions for coastal protection, including the effects of artificial wetlands or other vegetation, such as seagrass, in modelling appears to be a pressing matter.
The coastal morphodynamic model is a process-based model that can simulate coastal morphodynamic changes caused by storm events and relative sea-level rise. Human modifications such as hard structures can be simply added by changing the topography, which usually requires a high grid resolution that can reflect hard structures such as jetties. Beach nourishment acts as an additional sediment source for coastal morphodynamic models. These human activities do not require modification of the primitive equations and internal formulas of the model. This implies that no further development of the model is required. Human activities are reflected in the implementation of the model. These approaches are effective in the case of hard structures and available data representing human activities. Also, these human activities can be accounted for by using their impacts on the boundary forcing or the initial setting of the models. The computation methods of sediment fluxes need to be modified [7,22] because of human activities such as vegetation effects. Biological effects on morphodynamic evolution can also be incorporated, such as benthos, in tidal flats [23].

3. Equilibrium Modelling Approaches

The equilibrium status of coastal and estuarine systems is often observed. For example, beach equilibrium profiles exist in many observed beach topographic datasets [24,25]. These coastal profiles can be described by mathematical functions such as power-law or exponential functions [25,26]. Tide-dominated estuaries have funnel-shaped coastlines. The width of the estuary from the lower to the upper part can be described by an exponential function [27,28]. Recent studies have indicated that the cross-sectional areas of tide-dominated estuaries can also be described by an exponential function [29]. However, not all estuaries, such as hypersynchronous alluvial estuaries, can reach a morphodynamic equilibrium [28]. Geological boundary constraints in coastal plain estuaries also prevent the formation of the equilibrium morphology of funnel shapes. Prism-Area relationships are often used to model the equilibrium morphology of the tidal inlets [30]. The P-A relationships were extended to model tidal-dominated estuarine environments. For example, the ASMITA (aggregated scale morphological interaction between a tidal inlet and the adjacent coast) model schematises the tidal inlet into several components, which are described by the equilibrium P-V (Prism-Volume) relationships [31]. Figure 2 shows an example with subaqueous components of a tidal flat, channel delta, and boundary. Each element was characterised by the water volume. Sediment exchanges between the elements and boundaries were computed based on sediment-diffusive transport. The key principle is that each component attempts to reach equilibrium. The underlying assumption is that the equilibrium state exists at the component. Nevertheless, the ASMITA model is robust for investigating human impacts because the equilibrium state is an excellent metric for studying morphological responses. The predefined equilibrium allows the study of the adaptation time scale after human intervention [32]. The adaptation time scale further suggests that the equilibrium state and processes reaching equilibrium are important research objectives regarding human impacts. A threshold may exist from one system state to another (Figure 3c). If an equilibrium status does not exist or requires a time scale that is not of interest, the dynamic equilibrium tendency after human intervention is also used to study human impacts (Figure 3b).
These mathematical functions can be used to model beach profiles. For example, the Dynamic Equilibrium Shore Model (DESM) uses an exponential function to approximate coastal profile morphology, in which the parameter of the function is determined by numerical iteration to meet the sediment mass balance of the semi-enclosed coast [26]. The DESM can be applied to beach nourishment because of its capability to estimate the sediment budget [26]. As the DESM model is an inverse modelling approach, its application requires the input of coastline changes, the modern Digital Elevation Model, and relative sea level changes. For its practical application, the coastline configuration needs to be determined before the implementation of the DESM model.

4. Machine Learning (ML) Approaches

In recent years, the amount of observational data on coastal areas has increased significantly. These data not only include water and sediment discharge upstream of river mouths, but also encompass data on coastal topography, bathymetry, flow velocity, and high-resolution satellite remote sensing images. These data are essential for studying empirical relationships and testing numerical models. However, with the rapid development of artificial intelligence (AI) technology and its increasingly widespread, effective application in various fields, data-driven models have gained attention from coastal modellers and scientists [8]. Data-driven models rely on a large amount of data for training, allowing them to extract insights, quantify relationships, and make predictions from high-dimensional datasets [8]. These emerging machine-learning techniques provide new approaches for modelling human impacts on morphodynamics [34,35]. Human activities cannot be described by physical equations because of the subjective nature of human behaviour. In this regard, machine learning approaches appear to have advantages over physics-based models. Machine learning approaches are data-driven models that require measured or simulated data as input. The new data can be easily updated using the ML model. The inductive nature of these data-driven approaches relies on data accuracy. ML approaches in the case of imperfect data can be improved with a priori known physics, which is called physics-informed machine learning (PIML) [36]. The combination of physics-based and data-driven models has become increasingly important in terms of maximum utilisation of the observed data within the frame of known physics.
Traditional process-based morphodynamic models typically simulate natural processes but fail to adequately consider uncertain human activities. Bayesian Networks (BN), a type of probabilistic graphical model, are commonly used for uncertainty modelling and inference. They used nodes to represent random variables and directed edges to represent the influence relationships between variables. The principle behind Bayesian networks is the Bayesian theorem, which can be formulated as follows:
P(X1, X2, …, Xn) = P(X1) ⋅ P(X2|X1) ⋅ P(X3|X1,X2) ⋅ …⋅ P(Xn|X1,X2, …, Xn−1)
Here, X1, X2, …, and Xn are the node variables in the Bayesian network, and P(Xn|X1, X2, …, Xn−1) represents the conditional probability of variable Xn given the occurrence of X1, X2, …, Xn−1 simultaneously. In the literature, Bayesian networks have been widely applied to predict coastal morphodynamic changes caused by human activities [34,35]. A Bayesian network can quantify uncertainties from the input and output. Uncertainty quantification is the key difference between a Bayesian network and other ML approaches. Even coastal morphodynamic models cannot provide direct probabilistic estimations.
The Joint Probability Model [37] provides a method for probabilistic estimation of risk assessment based on existing simple or complex morphodynamic models. Uncertainties in human activities cannot be modelled using the JPM approach. The JPM approach uses the process-based model as the core model, with the statistical approach to obtain the probability distribution of boundary forcing. The JPM approach is commonly used to obtain the risk estimation of coastal erosion induced by extreme events [38,39]. The way to incorporate human impacts by the JPM approach is to modify the boundary conditions. The application of the JPM approach for modelling human impacts has not been found in the existing literature. The BN approach is a data-driving model, which first generates a probability distribution based on the observed data and conditional relationship between the variables. The descriptive BN approach [34] can help to investigate the relative importance of the variables on the target to be predicted. Furthermore, the uncertainties of the predicted target variables can be obtained using a probability distribution. Therefore, in case of a high amount of data about discrete human activities that are dynamically changing, the BN approach is useful to obtain the impacts of different degrees of human activity.
The Bayesian network approach was introduced to predict coastal vulnerability due to sea-level rise, in which BN exploration indicates that high sea-level rise and moderate-to-high vulnerability geomorphology result in a high probability of erosion [40]. The BN approach can also use decadal- and barrier-scale morphological variables to predict short-term dune changes [41]. The decadal-scale variables of the barrier islands are important for short-term barrier island dune morphological changes. The BN network was also used to explore the cause–effect relationship for coastal morphodynamics. The log-likelihood ratio measures the relative effects of individual variables on the predicted error rates.
LR = i log { P i posterior ( O i ) } i log { P i prior ( O i ) }
In this equation, P i posterior is the predicted probability distribution function obtained from the Bayes network, and P i prior is the prior probability distribution derived from the observations. Oi is the discrete variable in the Bayes network. LR can be used to measure the effect of an increasing number of variables or a decreasing number of variables on the outcome. Different human activities can be described by different variables of Oi [34,35,40]. The long-likelihood ratio, LR, can quantify the relative importance of different human activities. For example, human activities are quantified by classifying the types of infrastructures [41]. Nourishment events at different locations can be classified by using a nourishment index [35]. The flexible utilisation of anthropogenic data enables the wide application of the BN approach. Later research studied the effects of beach grass on dune morphological changes with the application of the hybrid BN approach including continuous variables [42]. The BN approach is useful for both prediction and scientific studies. However, the BN model has some limitations. First, its data-driven nature means that it cannot consider the actual physical processes of estuary development but rather focuses more on the inductive value of the data. Moreover, its ability to predict situations that are not present in a dataset is limited. Conditional probability tables for the outcome factor often exhibit similar probabilities across all states, indicating a high level of uncertainty.

5. How to Select Proper Modelling Approaches for Studying Human Impacts

To model the short-term impacts of human activities, a coastal-area morphodynamic model such as the Xbeach model is suitable for the short-term modelling of rapid changes in processes and morphology. The BN approach can also be applied for short-term predictions, as the BN approach can combine long-term and short-term variables for prediction, as in the example by [41]. If a coastal or estuarine system cannot attain an equilibrium status, equilibrium approaches are not applicable. In this case, coastal morphodynamic models cannot obtain an equilibrium status after a long-term simulation. The equilibrium modelling approaches are often used for coastal or estuarine systems that can attain an equilibrium status. Otherwise, the state transitions and resilience analysis should be considered with the application of the coastal-area morphodynamic models (Figure 3). Except for BN approaches, the current morphodynamic model is used to model the impacts of single or cumulated human activities. Punctuated human activities can be incorporated into BN approaches to predict human impacts. The data-driven BN approaches require a higher amount of data than coastal-area morphodynamic models.
The application of process-based modelling for human impacts requires the precondition that the fundamental principles of the model are not modified by human activities. For example, artificial vegetation at the tidal flat affects bottom friction, which requires modification of the computation of flow and sediment dynamics. If human activities have significantly altered coastal and estuarine ecosystems, eco-morphodynamic modelling should be considered.

6. Future Directions

The application of coastal morphodynamic models by modifying the boundary conditions due to human activities cannot update human activities during long-term simulations (Figure 3a). There is a time lag in human impacts, and human activities can cause state transitions when their impacts exceed a certain threshold (Figure 3b) [43]. If there are discrete events of human activity, coastal and estuarine systems may always be adjusted to new boundaries. However, such a discontinuous impact can be modelled by BN approaches with the classification of the human impact into bins (Figure 3a). Furthermore, the BN can update the human impact data. The limitation of the BN approach is that it relies on preexisting data for training. Increasing amounts of data about the processes facilitate the incorporation of the detailed processes of flow and sediment transport due to human impacts by using the BN approach. With the increasing amount of observation data, the BN approaches are able to include most cases existing in nature, which helps significantly improve the predictive capability of the BN approach. However, the coastal morphodynamic model can predict changes that have not existed in the past, due to the physical equations adopted in the model. Future research should consider combining ML approaches and process-based approaches to obtain both types of information when studying human impacts on morphodynamics (Figure 3a). The BN approach must be combined with physics to predict an unknown in the past. The recent advanced ML concept of physics-informed machine learning (PIML) attempts to combine physical equations and training data to reconstruct physical processes that have limited observed data. The PIML concept may help advance the BN approach that can quantify uncertainties and incorporate physical processes.
The computation of sediment fluxes in coastal and estuarine morphodynamic models is important for accurate prediction [7]. If human activities, such as seagrass planting, can change the bottom friction to influence the flow and sediment dynamics, the model needs to be modified. Usually, human activities are large scale and cannot directly affect the sediment dynamics. These human activities can change the bed and suspended sediment compositions, such as beach nourishment with sediments that are not the same as natural beach sediments. Channel dredging and sand excavation can also modify bed materials [44]. These changes are rarely considered in the modelling. Biological effects, such as benthos, on morphodynamics have received substantial attentions [45]. However, the coastal morphodynamic impact on benthos has rarely been modelled. Hence, bio-morphodynamic modelling is required in the modelling of human impacts for ecosystem protection and restoration.
Furthermore, computation methods for sediment fluxes are still immature. Modelling of the equilibrium morphology of coastal and estuarine systems is often used in coastal-area morphodynamic modelling for modelling constraints [46]. Equilibrium modelling is an important approach to studying human impacts. It should be noted that this approach has the precondition that the equilibrium status exists in the estuarine or coastal geomorphic systems.
The feedback of coastal and estuarine morphodynamics to human activities is hardly considered in the modelling. For example, intensive land reclamation has significantly destroyed estuarine ecosystems; therefore, restrictions on land reclamation have been implemented in recent decades in China [4]. Such feedback on human activities will need to be modelled in future studies.

7. Conclusions

Human activities are often discontinuous and discrete, which seems to be an event or a series of events. Furthermore, human activities can modify the boundary or initial conditions of estuarine or coastal systems, as well as bottom friction or sediment composition. Modelling human impacts on coastal morphodynamics is more challenging than other events such as coastal storms. This paper summarises the most common approaches for studying human impacts using modelling approaches.
In summary, the direct application of coastal morphodynamic models requires boundary data modified by human activities. Additional updating of the modelling module could be due to changes in the sediment transport processes that were not incorporated in the previous models. Human impacts are not short term and can last for decades. The equilibrium concept and system state analysis can be readily applied to obtain long-term morphodynamic responses of coastal and estuarine systems. Such equilibrium modelling may not be able to predict the future, as future evolution can be disturbed by other forces. Therefore, the Bayes network of machine learning approaches can be used to include the uncertainties of forces and the intrinsic limitations of the models. These three types of approaches can be used collaboratively to complement each other.
Future research should consider the bio-morphodynamic effects of human activities, as human activities have significantly damaged the ecosystem. The modelling of morphodynamic processes in benthic communities, as well as the feedback to the morphodynamics, appears to be increasingly important for ecosystem protection and restoration. From the perspective of a longer time span, the feedback of morphodynamics to human activities should be considered in the modelling, which requires better quantification and understanding of human–nature interactions.

Author Contributions

Conceptualization, J.D.; writing—original draft preparation, J.D. and H.Y.; writing—review and editing, J.D. All authors have read and agreed to the published version of the manuscript.


This research was funded by the National Natural Sciences Foundation of China, grant numbers 41806100 and 42161160305.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Besset, M.; Anthony, E.J.; Bouchette, F. Multi-Decadal Variations in Delta Shorelines and Their Relationship to River Sediment Supply: An Assessment and Review. Earth-Sci. Rev. 2019, 193, 199–219. [Google Scholar] [CrossRef] [Green Version]
  2. Barbier, E.B.; Hacker, S.D.; Hacker, S.; Kennedy, C.J.; Koch, E.W.; Stier, A.C.; Silliman, B.R. The Value of Estuarine and Coastal Ecosystem Services. Ecol. Monogr. 2011, 81, 169–193. [Google Scholar] [CrossRef]
  3. Murray, N.J.; Worthington, T.A.; Bunting, P.; Duce, S.; Hagger, V.; Lovelock, C.E.; Lucas, R.; Saunders, M.I.; Sheaves, M.; Spalding, M.; et al. High-Resolution Mapping of Losses and Gains of Earth’s Tidal Wetlands. Science 2022, 376, 744–749. [Google Scholar] [CrossRef] [PubMed]
  4. Zhang, X.; Lin, J.; Huang, H.; Deng, J.; Chen, A. Analysis on the Dynamics of Coastline and Reclamation in Pearl River Estuary in China for Nearly Last Half Century. Water 2022, 14, 1228. [Google Scholar] [CrossRef]
  5. Chu, Z.X.; Zhai, S.K.; Lu, X.X.; Liu, J.P.; Xu, J.X.; Xu, K.H. A Quantitative Assessment of Human Impacts on Decrease in Sediment Flux from Major Chinese Rivers Entering the Western Pacific Ocean. Geophys. Res. Lett. 2009, 36, L19603. [Google Scholar] [CrossRef]
  6. Jia, L.; Pan, S.; Wu, C. Effects of the Anthropogenic Activities on the Morphological Evolution of the Modaomen Estuary, Pearl River Delta, China. China Ocean Eng. 2013, 27, 795–808. [Google Scholar] [CrossRef]
  7. Deng, J.; Woodroffe, C.D.; Rogers, K.; Harff, J. Morphogenetic Modelling of Coastal and Estuarine Evolution. Earth-Sci. Rev. 2017, 171, 254–271. [Google Scholar] [CrossRef] [Green Version]
  8. Goldstein, E.B.; Coco, G.; Plant, N.G. A Review of Machine Learning Applications to Coastal Sediment Transport and Morphodynamics. Earth-Sci. Rev. 2019, 194, 97–108. [Google Scholar] [CrossRef] [Green Version]
  9. Lesser, G.R.; Roelvink, J.A.; van Kester, J.A.T.M.; Stelling, G.S. Development and Validation of a Three-Dimensional Morphological Model. Coast. Eng. 2004, 51, 883–915. [Google Scholar] [CrossRef]
  10. Villaret, C.; Hervouet, J.M.; Kopmann, R.; Merkel, U.; Davies, A.G. Morphodynamic Modeling Using the Telemac Finite-Element System. Comput. Geosci. 2013, 53, 105–113. [Google Scholar] [CrossRef]
  11. Roelvink, D.; Reniers, A.; van Dongeren, A.; van Thiel de Vries, J.; McCall, R.; Lescinski, J. Modelling Storm Impacts on Beaches, Dunes and Barrier Islands. Coast. Eng. 2009, 56, 1133–1152. [Google Scholar] [CrossRef]
  12. Miselis, J.L.; Lorenzo-Trueba, J. Natural and Human-Induced Variability in Barrier-Island Response to Sea Level Rise. Geophys. Res. Lett. 2017, 44, 922–11,931. [Google Scholar] [CrossRef] [Green Version]
  13. Luan, H.L.; Ding, P.; Wang, Z.B.; Ge, J. Process-Based Morphodynamic Modeling of the Yangtze Estuary at a Decadal Timescale: Controls on Estuarine Evolution and Future Trends. Geomorphology 2017, 290, 347–364. [Google Scholar] [CrossRef]
  14. Luan, H.L.; Ding, P.; Wang, Z.B.; Ge, J.; Ge, J.Z.; Yang, S. Decadal Morphological Evolution of the Yangtze Estuary in Response to River Input Changes and Estuarine Engineering Projects. Geomorphology 2016, 265, 12–23. [Google Scholar] [CrossRef]
  15. Zhang, W.; Harff, J.; Schneider, R.; Wu, C. Development of a Modelling Methodology for Simulation of Long-Term Morphological Evolution of the Southern Baltic Coast. Ocean Dyn. 2010, 60, 1085–1114. [Google Scholar] [CrossRef]
  16. Guo, L.; Zhu, C.; Xu, F.; Xie, W.; Van Der Wegen, M.; Townend, I.; Wang, Z.B.; He, Q. Reclamation of Tidal Flats Within Tidal Basins Alters Centennial Morphodynamic Adaptation to Sea—Level Rise. JGR Earth Surf. 2022, 127, e2021JF006556. [Google Scholar] [CrossRef]
  17. D’Alessandro, F.; Tomasicchio, G.R.; Musci, F.; Ricca, A. Dune erosion physical, analytical and numerical modelling. Coast. Eng. Proc. 2012, 33, 32. [Google Scholar] [CrossRef] [Green Version]
  18. van Rijn, L.C.; Walstra, D.-J.; van Ormondt, M. Unified View of Sediment Transport by Currents and Waves. IV: Application of Morphodynamic Model. J. Hydraul. Eng. 2007, 133, 776–793. [Google Scholar] [CrossRef]
  19. Kumar, P.; Debele, S.; Sahani, J.; Rawat, N.; Marti-Cardona, B.; Alfieri, S.M.; Basu, B.; Basu, A.S.; Bowyer, P.; Charizopoulos, N.; et al. Nature-Based Solutions Efficiency Evaluation against Natural Hazards: Modelling Methods, Advantages and Limitations. Sci. Total Environ. 2021, 784, 147058. [Google Scholar] [CrossRef]
  20. Tenebruso, C.; Nichols-O’Neill, S.; Lorenzo-Trueba, J.; Ciarletta, D.J.; Miselis, J.L. Undeveloped and Developed Phases in the Centennial Evolution of a Barrier-Marsh-Lagoon System: The Case of Long Beach Island, New Jersey. Front. Mar. Sci. 2022, 9, 958573. [Google Scholar] [CrossRef]
  21. Unguendoli, S.; Biolchi, L.G.; Aguzzi, M.; Pillai, U.P.A.; Alessandri, J.; Valentini, A. A Modeling Application of Integrated Nature Based Solutions (NBS) for Coastal Erosion and Flooding Mitigation in the Emilia-Romagna Coastline (Northeast Italy). Sci. Total Environ. 2023, 867, 161357. [Google Scholar] [CrossRef] [PubMed]
  22. Xu, Y.; Kalra, T.S.; Ganju, N.K.; Fagherazzi, S. Modeling the Dynamics of Salt Marsh Development in Coastal Land Reclamation. Geophys. Res. Lett. 2022, 49, e2021GL095559. [Google Scholar] [CrossRef]
  23. Brückner, M.Z.M.; Schwarz, C.; Coco, G.; Baar, A.; Boechat Albernaz, M.; Kleinhans, M.G. Benthic Species as Mud Patrol—Modelled Effects of Bioturbators and Biofilms on Large—Scale Estuarine Mud and Morphology. Earth Surf. Process. Landf. 2021, 46, 1128–1144. [Google Scholar] [CrossRef]
  24. Dean, R.G. Equilibrium Beach Profiles: U.S. Atlantic and Gulf Coasts. Ocean Eng. Rep. 1977, 53, ii–45. [Google Scholar]
  25. Dean, R.G. Equilibrium Beach Profiles: Characteristics and Applications. J. Coast. Res. 1991, 7, 53–84. [Google Scholar]
  26. Deng, J.; Zhang, W.; Harff, J.; Schneider, R.; Dudzinska-Nowak, J.; Terefenko, P.; Giza, A.; Furmanczyk, K. A Numerical Approach for Approximating the Historical Morphology of Wave-Dominated Coasts—A Case Study of the Pomeranian Bight, Southern Baltic Sea. Geomorphology 2014, 204, 425–443. [Google Scholar] [CrossRef]
  27. Davies, G.; Woodroffe, C.D. Tidal Estuary Width Convergence: Theory and Form in North Australian Estuaries. Earth Surf. Process. Landf. 2010, 35, 737–749. [Google Scholar] [CrossRef]
  28. Pittaluga, M.B.; Tambroni, N.; Canestrelli, A.; Slingerland, R.; Lanzoni, S.; Seminara, G. Where River and Tide Meet: The Morphodynamic Equilibrium of Alluvial Estuaries. J. Geophys. Res. Earth Surf. 2015, 120, 75–94. [Google Scholar] [CrossRef]
  29. Deng, J.; Yao, Q.; Wu, J. Estuarine Morphology and Depositional Processes in Front of Lateral River-Dominated Outlets in a Tide-Dominated Estuary: A Case Study of the Lingding Bay, South China Sea. J. Asian Earth Sci. 2020, 196, 104382. [Google Scholar] [CrossRef]
  30. Stive, M.J.F. Morphodynamics of Coastal Inlets and Tidal Lagoons. J. Coast. Res. 2006, 1, 28–34. [Google Scholar]
  31. Stive, M.J.F.; Wang, Z.B.; Capobianco, M.; Ruol, P.; Ruol, P.; Buijsman, M.C. Morphodynamics of a Tidal Lagoon and the Adjacent Coast. In Physics of Estuaries and Coastal Seas; Balkema: Rotterdam, The Netherlands, 1998; pp. 397–407. [Google Scholar]
  32. Kragtwijk, N.G.; Zitman, T.J.; Stive, M.J.F.; Wang, Z.B. Morphological Response of Tidal Basins to Human Interventions. Coast. Eng. 2004, 51, 207–221. [Google Scholar] [CrossRef]
  33. Thoms, M.C.; Piégay, H.; Parsons, M. What Do You Mean, ‘Resilient Geomorphic Systems’? Geomorphology 2018, 305, 8–19. [Google Scholar] [CrossRef]
  34. Beuzen, T.; Splinter, K.D.; Marshall, L.A.; Turner, I.L.; Harley, M.D.; Palmsten, M.L. Bayesian Networks in Coastal Engineering: Distinguishing Descriptive and Predictive Applications. Coast. Eng. 2018, 135, 16–30. [Google Scholar] [CrossRef]
  35. Wilson, K.E.; Adams, P.N.; Hapke, C.J.; Lentz, E.E.; Brenner, O. Application of Bayesian Networks to Hindcast Barrier Island Morphodynamics. Coast. Eng. 2015, 102, 30–43. [Google Scholar] [CrossRef]
  36. Karniadakis, G.E.; Kevrekidis, I.G.; Lu, L.; Perdikaris, P.; Wang, S.; Yang, L. Physics-Informed Machine Learning. Nat. Rev. Phys. 2021, 3, 422–440. [Google Scholar] [CrossRef]
  37. Callaghan, D.P.; Ranasinghe, R.; Roelvink, D. Probabilistic Estimation of Storm Erosion Using Analytical, Semi-Empirical, and Process Based Storm Erosion Models. Coast. Eng. 2013, 82, 64–75. [Google Scholar] [CrossRef]
  38. Ranasinghe, R.; Callaghan, D.; Stive, M.J.F. Estimating Coastal Recession Due to Sea Level Rise: Beyond the Bruun Rule. Clim. Chang. 2012, 110, 561–574. [Google Scholar] [CrossRef] [Green Version]
  39. Callaghan, D.P.; Nielsen, P.; Short, A.; Ranasinghe, R. Statistical Simulation of Wave Climate and Extreme Beach Erosion. Coast. Eng. 2008, 55, 375–390. [Google Scholar] [CrossRef]
  40. Gutierrez, B.T.; Plant, N.G.; Thieler, E.R. A Bayesian Network to Predict Coastal Vulnerability to Sea Level Rise. J. Geophys. Res. Earth Surf. 2011, 116, 1–15. [Google Scholar] [CrossRef] [Green Version]
  41. Gutierrez, B.T.; Plant, N.G.; Thieler, E.R.; Turecek, A. Using a Bayesian Network to Predict Barrier Island Geomorphologic Characteristics. J. Geophys. Res. Earth Surf. 2015, 120, 2452–2475. [Google Scholar] [CrossRef] [Green Version]
  42. Mao, Y.; Harris, D.L.; Callaghan, D.P.; Phinn, S. Determining the Shoreline Retreat Rate of Australia Using Discrete and Hybrid Bayesian Networks. JGR Earth Surf. 2021, 126, e2021JF006112. [Google Scholar] [CrossRef]
  43. Liu, J.; Dietz, T.; Carpenter, S.R.; Alberti, M.; Folke, C.; Moran, E.; Pell, A.N.; Deadman, P.; Kratz, T.; Lubchenco, J.; et al. Complexity of Coupled Human and Natural Systems. Science 2007, 317, 1513–1516. [Google Scholar] [CrossRef] [Green Version]
  44. Wei, X.; Cai, S.; Zhan, W.; Li, Y. Changes in the distribution of surface sediment in Pearl River Estuary, 1975–2017, largely due to human activity. Cont. Shelf Res. 2021, 228, 104538. [Google Scholar] [CrossRef]
  45. Arlinghaus, P.; Zhang, W.; Wrede, A.; Schrum, C.; Neumann, A. Impact of Benthos on Morphodynamics from a Modeling Perspective. Earth-Sci. Rev. 2021, 221, 103803. [Google Scholar] [CrossRef]
  46. Dissanayake, D.M.P.K.; Ranasinghe, R.; Roelvink, J.A. The Morphological Response of Large Tidal Inlet/Basin Systems to Relative Sea Level Rise. Clim. Chang. 2012, 113, 253–276. [Google Scholar] [CrossRef] [Green Version]
Figure 1. (a) government policy (b) Human land reclamation activities in Pearl River Estuary (modified from [4]). (b) as result of (a).
Figure 1. (a) government policy (b) Human land reclamation activities in Pearl River Estuary (modified from [4]). (b) as result of (a).
Jmse 11 01426 g001
Figure 2. (a) Aerial image of Shuidongwan Lagoon, South China Sea; (b) schematisation of geomorphic elements and exchange of Shuidongwan Lagoon for ASMITA (the schematisation was based on [32]).
Figure 2. (a) Aerial image of Shuidongwan Lagoon, South China Sea; (b) schematisation of geomorphic elements and exchange of Shuidongwan Lagoon for ASMITA (the schematisation was based on [32]).
Jmse 11 01426 g002
Figure 3. (a)Approaches to modelling human impacts to analyse coastal and estuarine evolution; (b) different states of equilibrium; (c) system states and resilience(Reprinted from What do you mean, ‘resilient geomorphic systems’? 305, with permission from Elsevier [33]).
Figure 3. (a)Approaches to modelling human impacts to analyse coastal and estuarine evolution; (b) different states of equilibrium; (c) system states and resilience(Reprinted from What do you mean, ‘resilient geomorphic systems’? 305, with permission from Elsevier [33]).
Jmse 11 01426 g003
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Deng, J.; Yu, H. Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review. J. Mar. Sci. Eng. 2023, 11, 1426.

AMA Style

Deng J, Yu H. Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review. Journal of Marine Science and Engineering. 2023; 11(7):1426.

Chicago/Turabian Style

Deng, Junjie, and Hongze Yu. 2023. "Modelling Coastal Morphodynamic Evolution under Human Impacts: A Review" Journal of Marine Science and Engineering 11, no. 7: 1426.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop