Field Observation of Water, Sediment, and Nutrient Distribution Patterns in Alluvial Ridge Basins Between the Abandoned Rio Grande Channels (Resacas)
Authors:
Tian Y. Dong1* ([email protected]), Waqid Nabi1, Kynan Rutan1, Jongsun Kim1, Jude Benavides1, Juan Gonzalez1, Jon Schwenk2
Institutions:
1University of Texas Rio Grande Valley; 2Los Alamos National Laboratory
Goals
This project aims to understand mass accumulation processes in alluvial ridge (AR) basins in river deltas across scales by combining field observed and remotely sensed flow data from the Rio Grande Delta (RGD), Texas, with two numerical models, ANUGA (hydrodynamic) and dorado (particle transport). AR basins are topographic depressions bounded by abandoned deltaic channels that are natural depo centers for water, sediment, and nutrients. However, more is needed to know about such processes, as these areas comprise most of the broader populated deltas. Yet, previous studies have focused on understanding the infilling of channelized portions of a delta. Such a project on the RGD is much needed to not only fill in the knowledge gap for basic research but also provide the under-served community with such data to understand better how delta inundation patterns change to inform mitigation policies and engineering practices.
Abstract
River deltas are net depositional landscapes that form at the coast, hosting populated socioeconomic hubs and providing essential ecosystem services, yet are facing stresses like land loss due to ongoing climate change and anthropogenic impacts. Channel avulsions, an abrupt shift in river course, is one of the main processes in that the delta distributes sediment to build land in coastal regions. Previous works have focused on assessing water, sediment, and nutrient transport and deposition in channelized portions of the delta. Through multiple avulsions, however, deltas contain several generations of abandoned channels with high relict levees that bound topographic degressions, known as AR basins, accounting for most of the broader delta region. How mass accumulates in AR basins between the relict deltaic channels still needs to be understood. More importantly, climate change and natural disasters are shown to disproportionately affect underserved communities, especially those of the RGD in south Texas. Hosting ~1 million people, the RGD region has the lowest median household incomes and the highest health risks in the United States. The RGD region contains seven main abandoned channels of the Rio Grande, known locally as Resacas. Despite frequent inundation due to extreme rainfall and storm surges, these AR basins’ water and sediment transport patterns remain elusive.
To fill this knowledge gap, researchers plan to deploy pressure sensors to measure water, nutrients, and sediment transport rates in three alluvial basins: Bahia Grande, Laguna Larga, and Vadia Ancha, spanning a range of potential sediment sources and tidal and human intervention conditions. Researchers will also collect sediment core samples throughout the alluvial basins and conduct radiocarbon age dating analysis to determine sediment and nutrient accumulation rates over multiple millennia. Researchers hypothesize that the accumulation rate is highest near the tidal inlet channels and decreases non-linearly towards the basin interior. In addition, given the shallow flow depth, modern transport conditions in the AR basin are strongly correlated with spring and neap cycles, storm surges, and extreme wind events. The results of this study will be the first systematic field measurements of mass accumulation rates and patterns in AR basins of an extensive delta system, the RGD, i.e., the second largest river delta in the United States. Researchers also plan to use these data to develop a mechanical explanation of how river deltas fill AR basins and provide the underserved community with such data to understand better how delta inundation patterns change to inform mitigation policies and engineering practices.
Funding Information
This research was supported by the DOE Office of Science, BER Program, grant no. DE-SC0024550.