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1. Modeling the dynamics of sediment transport, tides, and sea-level rise: Implications for the resilience of coastal Bengal.

   Tasich, C.M.; Gilligan, J.M.; Hornberger, G.M. (January 2024, Proceedings of the 2023 Winter Simulation Conference)

   Corlu, C.G.; Hunter, S.R.; Lam, B.; Onggo, S.; Shortle, J.; Biller, B. (Ed.)

   The coastal zone of the Ganges-Brahmaputra-Meghna (GBM) Delta is widely recognized as one of the most vulnerable places to sea-level rise (SLR), with around 57 million people living within 5m of sea level. Sediment transported by the Ganges, Brahmaputra, and Meghna rivers has the potential to raise the land and offset SLR. There is significant uncertainty in future sediment supply and SLR, which raises questions about the sustainability of the delta. We present a simple model, driven by basic physics, to estimate the evolution of the landscape under different conditions at low computational cost. Using a single tuning parameter, the model can match observed rates of land aggradation. We find a strong negative feedback, which robustly brings land elevation into equilibrium with changing sea level. We discuss how this model can be used to investigate the dynamics of sediment transport and the sustainability of the GBM Delta. 

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2. Projections of Global Delta Land Loss From Sea‐Level Rise in the 21st Century

   https://doi.org/10.1029/2021GL093368  

   Nienhuis, Jaap H.; van de Wal, Roderik S. W. (July 2021, Geophysical Research Letters)

   Abstract River deltas will likely experience significant land loss because of relative sea‐level rise (RSLR), but predictions have not been tested against observations. Here, we use global data of RSLR and river sediment supply to build a model of delta response to RSLR for 6,402 deltas, representing 86% of global delta land. We validate this model against delta land area change observations from 1985–2015, and project future land area change for IPCC RSLR scenarios. For 2100, we find widely ranging delta scenarios, from +94 ± 125 (2 s.d.) km²yr⁻¹for representative concentration pathway (RCP) 2.6 to −1,026 ± 281 km²yr⁻¹for RCP8.5. River dams, subsidence, and sea‐level rise have had a comparable influence on reduced delta growth over the past decades, but if we follow RCP8.5 to 2100, more than 85% of delta land loss will be caused by climate‐change driven sea‐level rise, resulting in a loss of ∼5% of global delta land. 

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3. Stable ≠ Sustainable: Delta Dynamics Versus the Human Need for Stability

   https://doi.org/10.1029/2021EF002121  

   Passalacqua, P.; Giosan, L.; Goodbred, S.; Overeem, I. (July 2021, Earth's Future)

   Abstract Arising from the non‐uniform dispersal of sediment and water that build deltaic landscapes, morphological change is a fundamental characteristic of river delta behavior. Thus, sustainable deltas require mobility of their channel networks and attendant shifts in landforms. Both behaviors can be misrepresented as degradation, particularly in context of the “stability” that is generally necessitated by human infrastructure and economies. Taking the Ganges‐Brahmaputra‐Meghna Delta as an example, contrary to public perception, this delta system appears to be sustainable at a system scale with high sediment delivery and long‐term net gain in land area. However, many areas of the delta exhibit local dynamics and instability at the scale at which households and communities experience environmental change. Such local landscape “instability” is often cited as evidence that the delta is in decline, whereas much of this change simply reflects the morphodynamics typical of an energetic fluvial‐delta system and do not provide an accurate reflection of overall system health. Here we argue that this disparity between unit‐scale sustainability and local morphodynamic change may be typical of deltaic systems with well‐developed distributary networks and strong spatial gradients in sediment supply and transport energy. Such non‐uniformity and the important connections between network sub‐units (i.e., fluvial, tidal, shelf) suggest that delta risk assessments must integrate local dynamics and sub‐unit connections with unit‐scale behaviors. Structure and dynamics of an integrated deltaic network control the dispersal of water, solids, and solutes to the delta sub‐environment and thus the local to unit‐scale sustainability of the system over time. 

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4. Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct

   https://doi.org/10.5194/esurf-8-809-2020  

   Perignon, Mariela; Adams, Jordan; Overeem, Irina; Passalacqua, Paola (January 2020, Earth Surface Dynamics)

   null (Ed.)

   Abstract. The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help, but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas with 10 parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the 10 morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally controlled region of the delta. Islands at the delta front under the mixed influence of tides, fluvial–estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method is not able to distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous “natural” morphology or that the input data (time, resolution) used in this study are preventing the identification of a human signature. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems. 

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5. Dominant process zones in a mixed fluvial-tidal delta are morphologically distinct

   Perignon, M.; Adams, J.; Overeem, I.; Passalacqua, P. (April 2020, Earth surface dynamics discussions)

   The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas from ten parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the ten morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain, with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally-controlled region of the delta. Islands at the delta front, under the mixed influence of tides, fluvial-estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method does not distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous natural morphology. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems. 

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