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Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches.

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Title: Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches.
Authors: Garlan, Thierry, Almar, Rafael, Bergsma, Erwin W. J.
Source: Remote Sensing; Oct2025, Vol. 17 Issue 19, p3360, 34p
Subject Terms: REMOTE sensing, COASTAL processes (Physical geology), SHORELINE monitoring, SIMULATION methods & models, COASTAL zone management, FIELD research, INTERDISCIPLINARY research, RISK assessment
Geographic Terms: SENEGAL, WEST Africa
Abstract: Highlights: What are the main findings? A comprehensive review identifies 39 multidisciplinary drivers of beach evolu-tion, spanning meteorological, oceanographic, geological, biological, and anthro-pogenic factors. A case study of the Langue de Barbarie sandspit in Senegal (West Africa) demonstrates how integrating in situ measurements with satellite-derived information can reveal key processes that are often overlooked in coastal studies. What is the implication of the main finding? Improved prediction of shoreline evolution requires the combination of remote sensing observations, numerical models and local monitoring in order to capture the multiscale and multidisciplinary drivers of change. Using high-resolution, long-term satellite data alongside in situ surveys provides a pathway toward more reliable, reproducible, and globally transferable approaches to coastal risk assessment and management. This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:Highlights: What are the main findings? A comprehensive review identifies 39 multidisciplinary drivers of beach evolu-tion, spanning meteorological, oceanographic, geological, biological, and anthro-pogenic factors. A case study of the Langue de Barbarie sandspit in Senegal (West Africa) demonstrates how integrating in situ measurements with satellite-derived information can reveal key processes that are often overlooked in coastal studies. What is the implication of the main finding? Improved prediction of shoreline evolution requires the combination of remote sensing observations, numerical models and local monitoring in order to capture the multiscale and multidisciplinary drivers of change. Using high-resolution, long-term satellite data alongside in situ surveys provides a pathway toward more reliable, reproducible, and globally transferable approaches to coastal risk assessment and management. This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms. [ABSTRACT FROM AUTHOR]
ISSN:20724292
DOI:10.3390/rs17193360