Search for: All records

Estimates of nitrate loading to the Arctic Ocean are limited by the lack of field observations within deltas partly due to logistical constraints. To overcome this limitation, we use a remote sensing framework to estimate retention of nitrate in Arctic deltas. We achieve this by coupling hydrological and biogeochemical process models at the network scale for five major Arctic deltas. Binary masks of delta channels were used to simulate flow direction and magnitude through networks. Models were parameterized using historical and seasonal observations. Simulated nitrate retention ranged from 2.9% to 15% of the incoming load. Retention rates were largest during winter but smallest during spring conditions when increased discharges export large nitrate masses to the coast. Under future climate scenarios, retention rates fall by ∼1%–10%. Arctic deltas have an important effect on the magnitude of nitrate entering Arctic seas and the inclusion of processing in deltas can improve flux estimates.

Understanding the way fluvially transported materials are partitioned in river deltas is essential for predicting their morphological change and the fate of environmental constituents and contaminants. Translating water‐based partitioning estimates into fluxes of nonwater materials is often difficult to constrain because most materials are not uniformly distributed in the water column and may have characteristic transport pathways that differ from the mean flow. Here, we present a novel reduced‐complexity modeling approach for simulating the patterns of transport of a diverse range of suspended fluvial inputs influenced by vertical stratification and topographic steering. We utilize a mixed Eulerian‐Lagrangian modeling approach to estimate the patterns of nourishment and connectivity in the Wax Lake and Atchafalaya Deltas in coastal Louisiana. Using the reduced‐complexity particle routing modeldorado, in conjunction with a calibratedANUGAhydrodynamic model, we quantify how transport patterns in each system change as a function of a material's Rouse number and environmental conditions. We find that even small changes to local topographic steering lead to emergent system‐scale changes in patterns of fluvial nourishment, with greater channel‐island connectivity for positively buoyant materials than negatively buoyant materials, hydraulically sorting different materials in space. We also find that the nourishment patterns of some materials are more sensitive than others to changes in discharge, tidal conditions, and anthropogenic dredging. Our results have important implications for understanding the eco‐geomorphic evolution of deltas, and our modeling framework could have interdisciplinary implications for studying the transport of materials in other systems, including sediments, nutrients, wood, plastics, and biotic materials.

River deltas are home to large populations and can be composed of complex channel networks which convey flows of matter to the shoreline. Knowledge of flow within individual channels is needed to quantify the distribution of discharge across the delta, and thus its sustainability over time. Due to a lack of field measurements at the local channel scale, researchers leverage remote sensing data to estimate the partitioning of flow. We compare data from 15 river deltas to discharge partitioning estimates based on channel network graphs derived from remote sensing imagery. We quantify errors in the common width‐based method and test alternative partitioning techniques to find that width‐based discharge partitioning is universally applicable, suggesting that absent any site‐specific information, discharge partitioning by average channel width is an appropriate approach. We also provide networks, streamflow measurements, and flux partitioning estimates for 28 delta networks as the Discharge In Distributary NeTworks (DIDNT) dataset.

Groundwater is the primary source of water in the Bengal Delta but contamination threatens this vital resource. In deltaic environments, heterogeneous sedimentary architecture controls groundwater flow; therefore, characterizing subsurface structure is a critical step in predicting groundwater contamination. Here, we show that surface information can improve the characterization of the nature and geometry of subsurface features, thus improving the predictions of groundwater flow. We selected three locations in the Bengal Delta with distinct surface river network characteristics—the lower delta with straighter tidal channels, the mid‐delta with meandering and braided channels, and the inactive delta with transitional sinuous channels. We used surface information, including channel widths, depths, and sinuosity, to create models of the subsurface with object‐based geostatistical simulations. We collected an extensive set of lithologic data and filled in gaps with newly drilled boreholes. Our results show that densely distributed lithologic data from active lower and mid‐delta are consistent with the object‐based models generated from surface information. In the inactive delta, metrics from object‐based models derived from surface geometries are not consistent with subsurface data. We further simulated groundwater flow and solute transport through the object‐based models and compared these with simulated flow through lithologic models based only on variograms. Substantial differences in flow and transport through the different geologic models show that geometric structure derived from surface information strongly influences groundwater flow and solute transport. Land surface features in active deltas are therefore a valuable source of information for improving the evaluation of groundwater vulnerability to contamination.

Coastal deltaic aquifers are vulnerable to degradation from seawater intrusion, geogenic and anthropogenic contamination, and groundwater abstraction. The distribution and transport of contaminants are highly dependent on the subsurface sedimentary architecture, such as the presence of channelized features that preferentially conduct flow. Surface deposition changes in response to sea‐level rise (SLR) and sediment supply, but it remains unclear how these surface changes affect the distribution and transport of groundwater solutes in aquifers. Here, we explore the influence of SLR and sediment supply on aquifer heterogeneity and resulting effects on contaminant transport. We use realizations of subsurface heterogeneity generated by a process‐based numerical model, DeltaRCM, which simulates the evolution of a deltaic aquifer with different input sand fractions and rates of SLR. We simulate groundwater flow and solute transport through these deposits in three contamination scenarios: (a) vertical transport from widespread contamination at the land surface, (b) vertical transport from river water infiltration, and (c) lateral seawater intrusion. The simulations show that the vulnerability of deltaic aquifers to seawater intrusion correlates to sand fraction, while vertical transport of contaminants, such as widespread shallow contamination and river water infiltration, is influenced by channel stacking patterns. This analysis provides new insights into the connection between the depositional system properties and vulnerability to different modes of groundwater contamination. It also illustrates how vulnerability may vary locally within a delta due to depositional differences. Results suggest that groundwater management strategies may be improved by considering surface features, location within the delta, and the external forcings during aquifer deposition.