River deltas distribute fluxes through complex channel networks. Knowing how those fluxes are distributed is valuable for quantifying land-ocean fluxes, and this is often predicted based on channel width. This study provided the first-ever assessment of how accurate width-based flux partitioning is to determine whether it is an appropriate method for estimating discharge in the absence of field observations.
This paper provided a new dataset of width-based flux partitioning on 28 global deltas along with an assessment of the accuracy of this method on 15 deltas with available field data for comparison. We showed that width-based flux partitioning is an appropriate method to estimate river discharge throughout distributary networks and that this method applies to both Arctic and temperate deltas and to deltas dominated by river, wave, and tidal forcings. The accuracy of the method was not sensitive to latitude, forcing, or the complexity of the channel network. This result opens doors for our ability to estimate riverine fluxes to the coast and to quantify the uncertainty in those estimates for global deltas.
River deltas are home to hundreds of millions of people worldwide, yet their sheer size and a high number of channels mean it is not feasible to measure flow in all individual channels. However, knowledge of flow in river channels is critical for understanding the evolution of these systems at the time and space scales relevant to humans, as the flow of water mediates the transport of solutes and sediment. Researchers have proposed methods to estimate the division of flow in delta channels, but the accuracy of these methods has not been quantified. We estimate flow in the channels of 15 river deltas by conceptualizing channels as links and junctions as nodes in a network graph and compare these estimates to reported flow observations. We quantify the error in our estimates and test alternative methods of flow estimation. We find that the division of flow based on average channel width is appropriate across latitudes and environmental conditions. We extract channel networks and apply this method to estimate flow through 28 delta channel networks to form the Discharge In Distributary NeTworks (DIDNT) dataset.