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- Earth surface dynamics discussions
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- National Science Foundation
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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.more » « less
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.
River deltas are dynamic systems whose channels can widen, narrow, migrate, avulse, and bifurcate to form new channel networks through time. With hundreds of millions of people living on these globally ubiquitous systems, it is critically important to understand and predict how delta channel networks will evolve over time. Although much work has been done to understand drivers of channel migration on the individual channel scale, a global-scale analysis of the current state of delta morphological change has not been attempted. In this study, we present a methodology for the automatic extraction of channel migration vectors from remotely sensed imagery by combining deep learning and principles from particle image velocimetry (PIV). This methodology is implemented on 48 river delta systems to create a global dataset of decadal-scale delta channel migration. By comparing delta channel migration distributions with a variety of known external forcings, we find that global patterns of channel migration can largely be reconciled with the level of fluvial forcing acting on the delta, sediment flux magnitude, and frequency of flood events. An understanding of modern rates and patterns of channel migration in river deltas is critical for successfully predicting future changes to delta systems and for informing decision makers striving for deltaic resilience.
A recently funded US National Science Foundation project seeks to investigate monsoon variability within the Ganges-Brahmaputra-Meghna (GBM) river basin as a potential predictor for annual shoreline erosion rates in the lower coastal delta region. Many previous studies have examined the interannual variability of South Asian precipitation either within political boundaries or across large spans of latitudes and longitudes, but few have taken a more hydrologic approach by analyzing the atmospheric-oceanic forcings that lead to precipitation falling only within the GBM basin. The temporal climate patterns would likely be different from previous studies and are hypothesized to have a more direct effect on outlet discharge and erosion rates. In the present study, mean monsoon precipitation (June-July-August-September) for the 2,309 0.25° grid boxes of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) was extracted using geospatial methods. A Principal Component (PC) analysis was performed over the period 1983 to 2015. The first PC explains 88.7% of the variance and resembles climatology with the center of action over Bangladesh. The eigenvector shows a downward trend consistent with studies reporting a recent decline in monsoon rainfall. The second PC explains 2.9% of the variance and concentrates rainfall in the western portion of the basin. The 2nd component has greater temporal variability than the 1st component and an apparent decadal cycle. An analysis of global precipitation indicates that the rainfall patterns obtained within the GBM are localized. Surface and upper-air atmospheric height fields suggest the 2nd PC pattern is forced by a Rossby wave train stemming from the North Atlantic.more » « less
Deltaic islands are distinct hydro-environmental zones with global significance in food security, biodiversity conservation, and fishery industry. These islands are the fundamental building blocks of many river deltas. However, deltaic islands are facing severe challenges due to intensive anthropogenic activities, sea level rise, and climate change. In this study, dynamic changes of deltaic islands in Wax Lake Delta (WLD) and Atchafalaya Delta (AD), part of the Atchafalaya River Delta Complex (ARDC) in Louisiana, USA, were quantified based on remote sensing images from 1991 to 2019 through a machine learning method. Results indicate a significant increase in deltaic islands area for the whole ARDC at a rate of 1.29 km2/yr, with local expansion rates of 0.60 km2/yr for WLD and 0.69 km2/yr for AD. All three parts of the WLD naturally prograded seaward, with the western part (WP) and central part (CP) expanding southwestward to the sea, while the eastern part (EP) prograding southeastwards. Differently from WLD, the three parts of AD irregularly expanded seaward under the impacts of anthropogenic activities. The WP and CP of the AD expanded respectively northwestwards and southwestwards, while the EP remained stable. Different drivers dominate the growth of deltaic islands in the WLD and AD. Specifically, fluvial suspended sediment discharge and peak flow events were responsible for the shift in the spatial evolution of WLD, while dredging and sediment disposal contributed to the expansion of AD. Tropical storms with different intensity and landing locations caused short-term deltaic island erosion or expansion. Tropical storms mainly generated erosion on the deltaic islands of the WLD, while causing transient erosion or siltation on the deltaic islands of the AD. In addition, high-intensity hurricanes that made landfall east of the deltas caused more erosion in the AD. Finally, sea level rise, at the current rate of 8.17 mm/yr, will not pose a threat to the deltaic island of WLD, while the eastern part of AD may be at risk of drowning. This study recognizes the complexity of factors influencing the growth of deltaic islands, suggesting that quantitative studies on the deltaic island extent are of critical for the restoration and sustainable management of the Mississippi River Delta and other deltas around the world.more » « less