Down-delta hydraulic geometry and its application to the rock record

Data Availability Statement: Data from this paper are publicly available at https://doi.org/10.6084/m9.figshare.19574938.v2.This research was funded by an award from the Indonesia Endowment Fund for Education (LPDP) to Prasojo. The global river discharge dataset is available from The Global Runoff Data Centre (GRDC), 56068 Koblenz, Germany or and via the web (http://www.bafg.de/grdc.htm).Copyright . Palaeodischarge estimation is largely undertaken within fluvial settings. However, there are limited palaeodischarge estimates specifically from delta deposits, despite their significance globally. Estimating water palaeodischarges for deltas using catchment-based approaches developed using data from fluvial settings requires estimating parameters from the rock record (for example, palaeotemperature, palaeoslope and palaeorelief). These may be difficult to determine, leading to under-estimation or over-estimation of palaeodischarge values due to differences in process-form relationships between alluvial rivers and deltas. When a sediment-conveying fluvial channel enters a standing body of water, delta lobes develop through repeating mouth bar deposition due to flow deceleration, forming a deltaic morphology with distributary channel networks that differ morphologically from those developed in unidirectional flowing alluvial rivers. This study provides empirical relationships determined across five climate regions, using 3823 measurements of distributary channel width from 66 river deltas alongside the trunk river bankfull discharge that feeds into the entire delta, using a hydraulic geometry scaling approach. Empirical relationships are developed from the global delta dataset between bankfull discharge and catchment area (Qb–A), and bankfull discharge and median distributary channel width (Qb–Wmed). These empirical relationships produce very strong statistical correlations, especially between Qb and Wmed, across different climate regions (Qb = 0.34 Wmed1.48, R2 = 0.77). However, both Qb–A and Qb–Wmed relationships have outliers that may be explained by particular hydrological or geomorphic conditions. These new empirical relationships derived from modern systems are then applied to Cretaceous outcrops (Ferron Sandstone and Dunvegan Formation). The comparatively simple scaling relationships derived here produced palaeodischarge estimates within the same order of magnitude as palaeodischarge values previously obtained using existing, more complex approaches. This study contributes to source-to-sink investigations by enabling palaeodischarge estimates that intrinsically account for climate impacts on channel geometry at the time of deposition, using measurements of channel width or catchment area of a deltaic outcrop.Lembaga Pengelola Dana Pendidikan. Grant Number: 20190222021387

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

© 2020 The Authors. Cloud-based computing, access to big geospatial data, and virtualization, whereby users are freed from computational hardware and data management logistics, could revolutionize remote sensing applications in fluvial geomorphology. Analysis of multitemporal, multispectral satellite imagery has provided fundamental geomorphic insight into the planimetric form and dynamics of large river systems, but information derived from these applications has largely been used to test existing concepts in fluvial geomorphology, rather than for generating new concepts or theories. Traditional approaches (i.e., desktop computing) have restricted the spatial scales and temporal resolutions of planimetric river channel change analyses. Google Earth Engine (GEE), a cloud-based computing platform for planetary-scale geospatial analyses, offers the opportunity to relieve these spatiotemporal restrictions. We summarize the big geospatial data flows available to fluvial geomorphologists within the GEE data catalog, focus on approaches to look beyond mapping wet channel extents and instead map the wider riverscape (i.e., water, sediment, vegetation) and its dynamics, and explore the unprecedented spatiotemporal scales over which GEE analyses can be applied. We share a demonstration workflow to extract active river channel masks from a section of the Cagayan River (Luzon, Philippines) then quantify centerline migration rates from multitemporal data. By enabling fluvial geomorphologists to take their algorithms to petabytes worth of data, GEE is transformative in enabling deterministic science at scales defined by the user and determined by the phenomena of interest. Equally as important, GEE offers a mechanism for promoting a cultural shift toward open science, through the democratization of access and sharing of reproducible code.Natural Environment Research Council. Grant Number: NE/S00331