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

Perignon, M. C.; Adams, J. M.; Overeem, I.; Passalacqua, Paola

doi:10.5194/esurf-2020-28

Cited by 4 publications

(13 citation statements)

References 43 publications

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“… Map of the Ganges‐Brahmaputra‐Meghna Delta and areas of decline and construction based on Wilson and Goodbred (2015). The map also shows the approximate tidal and backwater extent (Perignon et al., 2020) and the topographic slope break (Wilson & Goodbred, 2015). The background image is a 2020 Landsat 8 composite image.…”

Section: Delta Network As Distributor Of Fluxesmentioning

confidence: 99%

“…One aspect of river deltas that can help the estimation of flux partitioning is the network structure itself, which together with the resulting geometry of the islands (i.e., land masses surrounded by channels) reflects the main processes acting on the system (Passalacqua et al., 2013; Perignon et al., 2020). Statistical distributions of geometric characteristics of islands and channels show scaling breaks corresponding to process transitions making tidal, river, and upstream portions of the delta easily distinguishable from one another (Passalacqua et al., 2013) (Figure 2).…”

Section: Delta Network As Distributor Of Fluxesmentioning

confidence: 99%

“…Statistical distributions of geometric characteristics of islands and channels show scaling breaks corresponding to process transitions making tidal, river, and upstream portions of the delta easily distinguishable from one another (Passalacqua et al., 2013) (Figure 2). Machine learning approaches based on metric analyses and clustering further identify process‐related classes of islands (Perignon et al., 2020) (Figure 2). Work in other systems has shown that network structure can also be used to estimate flux partitioning and the delivery of fluxes at the shore (Tejedor et al., 2015) and that estimation of nutrient and sediment fluxes can be addressed in similar ways (Dong et al., 2020; Knights et al., 2020).…”

Section: Delta Network As Distributor Of Fluxesmentioning

confidence: 99%

“…Thus, quantifying the distribution of fluxes along the network with observations, particularly from remote sensing, and modeling, is key to delta management. While models may not be able to simulate water and sediment transport over large systems at high resolutions, analysis of surface patterns, for example, based on machine learning (Perignon et al., 2020) can identify subareas of the delta that behave similarly. It may also help to identify which areas are suitable for building land, and which are too sediment‐starved to feasibly be nourished.…”

Section: A Network View Of Delta Sustainabilitymentioning

confidence: 99%

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

et al. 2021

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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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Section: Delta Network As Distributor Of Fluxesmentioning

confidence: 99%

Section: Delta Network As Distributor Of Fluxesmentioning

confidence: 99%

Section: Delta Network As Distributor Of Fluxesmentioning

confidence: 99%

Section: A Network View Of Delta Sustainabilitymentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

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“…Unlike displacement‐based measurements, the pixel‐based approach does not require spatially coherent channels. Due to this advantage, the pixel‐based approach has since been adopted in experiments, numerical simulations, and field studies (Bufe et al., 2016, 2019; Lauzon et al., 2019; Liang et al., 2016; Perignon et al., 2020). However, the pixel‐based approach also comes with a disadvantage: it does not differentiate between motion due to channel migration and channel avulsion, nor does it provide information about the direction of motion.…”

Section: Introductionmentioning

confidence: 99%

Channel Migration in Experimental River Networks Mapped by Particle Image Velocimetry

et al. 2021

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Deltaic river networks naturally reorganize as interconnected channels move to redistribute water, sediment, and nutrients across the delta plain. Network change is documented in decades of satellite imagery and laboratory experiments, but our ability to measure and understand channel movements is limited: existing methods are difficult to employ efficiently and struggle to distinguish between gradual movements (channel migration) and abrupt shifts in river course (channel avulsions). Here, we present a method to extract channel migration from plan‐view imagery using particle image velocimetry (PIV). Although originally designed to track particles moving in a fluid, PIV can be adapted to track channels moving on the delta surface, based on input estimates of channel width, migration timescale, and maps of the wet‐dry interface. Results for a delta experiment show that PIV‐derived vector fields accurately capture channel‐bank movements, as compared to manually drawn maps and an independent image‐registration technique. Unlike other methods, PIV targets the process of channel migration, excluding changes associated with channel avulsions and overbank flow. PIV‐derived migration rates from the experiment span an order of magnitude and are reduced under lower sediment supply and during sea‐level rise, supporting recent models. Together, results indicate that PIV offers a fast and reliable way to measure channel migration in river networks, that channel migration rates under non‐cohesive conditions can displace channels a distance comparable to their width in the time needed to aggrade ∼10% of the channel depth, and that migration direction is ∼60% orthogonal to mean flow direction and ∼40% flow‐parallel overall.

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Linking the Surface and Subsurface in River Deltas—Part 1: Relating Surface and Subsurface Geometries

Hariharan

Michael

et al. 2021

Water Resources Research

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River deltas are densely populated regions of the world with vulnerable groundwater reserves. Contamination of these groundwater aquifers via saline water intrusion and pollutant transport is a growing threat due to both anthropogenic and climate changes. The arrangement and composition of subsurface sediment is known to have a significant impact on aquifer contamination; however, developing accurate depictions of the subsurface is challenging. In this work, we explore the relationship between surface and subsurface properties and identify the metrics most sensitive to different forcing conditions. To do so, we simulate river delta evolution with the rule‐based numerical model, DeltaRCM, and test the influence of input sand fraction and steady sea level rise (SLR) on delta evolution. From the model outputs, we measure a variety of surface and subsurface metrics chosen based on their applicability to imagery and modeling results. The Kullback‐Leibler (KL) divergence is then used to quantitatively gauge which metrics are most indicative of the imposed forcings. Both qualitative observations and the KL divergence analysis suggest that estimates of subsurface connectivity can be constrained using surface information. In particular, more variable shoreline roughness values and higher surface wetted fraction values correspond to increased subsurface connectivity. These findings complement traditional methods of estimating subsurface structure in river‐dominated delta systems and represent a step toward the identification of a direct link between surface observations and subsurface form.

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

Cited by 4 publications

References 43 publications

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

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

Channel Migration in Experimental River Networks Mapped by Particle Image Velocimetry

Linking the Surface and Subsurface in River Deltas—Part 1: Relating Surface and Subsurface Geometries

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