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Arctic coastal environments are eroding and rapidly changing. A lack of pan-Arctic observations limits our ability to understand controls on coastal erosion rates across the entire Arctic region. Here, we capitalize on an abundance of geospatial and remotely sensed data, in addition to model output, from the North Slope of Alaska to identify relationships between historical erosion rates and landscape characteristics to guide future modeling and observational efforts across the Arctic. Using existing datasets from the Alaska Beaufort Sea coast and a hierarchical clustering algorithm, we developed a set of 16 coastal typologies that captures the defining characteristics of environments susceptible to coastal erosion. Relationships between landscape characteristics and historical erosion rates show that no single variable alone is a good predictor of erosion rates. Variability in erosion rate decreases with increasing coastal elevation, but erosion rate magnitudes are highest for intermediate elevations. Areas along the Alaskan Beaufort Sea coast (ABSC) protected by barrier islands showed a three times lower erosion rate on average, suggesting that barrier islands are critical to maintaining mainland shore position. Finally, typologies with the highest erosion rates are not broadly representative of the ABSC and are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content. All else being equal, warmer permafrost is also associated with higher erosion rates, suggesting that warming permafrost temperatures may contribute to higher future erosion rates on permafrost coasts. The suite of typologies can be used to guide future modeling and observational efforts by quantifying the distribution of coastlines with specific landscape characteristics and erosion rates.

 

Flooding of low-lying Arctic regions has the potential to warm and thaw permafrost by changing the surface reflectance of solar insolation, increasing subsurface soil moisture, and increasing soil thermal conductivity. However, the impact of flooding on permafrost in the continuous permafrost environment remains poorly understood. To address this knowledge gap, we used a combination of available flooding data on the Ikpikpuk delta and a numerical model to simulate the hydro-thermal processes under coastal floodplain flooding. We first constructed the three most common flood events based on water level data on the Ikpikpuk: snowmelt floods in the late spring and early summer, middle and late summer floods, and floods throughout the whole spring and summer. Then the impact of these flooding events on the permafrost was simulated for one-dimensional permafrost columns using the Advanced Terrestrial Simulator (ATSv1.0), a fully coupled permafrost-hydrology and thermal dynamic model. Our results show that coastal floods have an important impact on coastal permafrost dynamics with a cooling effect on the surficial soil and a warming effect on the deeper soil. Cumulative flooding events over several years can cause continuous warming of the deep subsurface but cool down the surficial layer. Flood timing is a primary control of the vertical extent of the permafrost thaw and the active layer deepening.

 

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.

 

Abstract. The abundance of global, remotely sensed surface water observations has accelerated efforts toward characterizing and modeling how water moves across the Earth's surface through complex channel networks. In particular, deltas and braided river channel networks may contain thousands of links that route water, sediment, and nutrients across landscapes. In order to model flows through channel networks and characterize network structure, the direction of flow for each link within the network must be known. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely sensed imagery and knowledge of the network's inlet and outletlocations. We designed a suite of direction-predicting algorithms (DPAs),each of which exploits a particular morphologic characteristic of thechannel network to provide a prediction of a link's flow direction. DPAswere chained together to create “recipes”, or algorithms that set all theflow directions of a channel network. Separate recipes were built for deltasand braided rivers and applied to seven delta and two braided river channelnetworks. Across all nine channel networks, the recipe-predicted flowdirections agreed with expert judgement for 97 % of all tested links, andmost disagreements were attributed to unusual channel network topologiesthat can easily be accounted for by pre-seeding critical links with knownflow directions. Our results highlight the (non)universality ofprocess–form relationships across deltas and braided rivers. 

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.

 

Understanding the feedbacks between water, sediment, and vegetation in deltas is an important part of understanding deltas as ecomorphodynamic systems. We conducted a set of laboratory experiments using alfalfa (Medicago sativa) as a proxy for delta vegetation to investigate: (1) the effects of plants on delta growth and channel network formation; and (2) the timescales controlling delta evolution in the presence of plants. Experiments were conducted with fluctuating discharge (i.e. flood and base flow periods) and variable seeding densities. We found that when deltas were small, channels had no memory across flood cycles, as floods could completely fill the incised channel network. When deltas were large, the larger channel volume could remain underfilled to keep channel memory. Plant patches also helped to increase the number of channels and make a more distributive network. Patchiness increased over time to continually aid in bifurcation, but as vegetation cover and patch sizes increased, patches began to merge. Larger patches blocked the flow to enhance topset deposition and channel filling, even for the case of large deltas with a high channel volume. We conclude that both plant patchiness and delta size affect the development of the channel network, and we hypothesize that their influences are manifested through two competing timescales. The first timescale,T_v, defines the time when the delta is large enough for channels to have memory (i.e. remain underfilled), and the second,T_p, defines the time when vegetation patches merge, amplifying deposition and blocking channels. When run time is between these two timescales, the delta can develop a persistent distributary network of channels aided by bifurcation around plant patches, but onceT_pis reached, the channel network can again be destroyed by vegetation. © 2018 John Wiley & Sons, Ltd.

 

Human activities have increased nitrate export from rivers, degrading coastal water quality. At deltaic river mouths, the flow of water through wetlands increases nitrate removal, and the spatial organization of removal rates influences coastal water quality. To understand the spatial distribution of nitrate removal in a river‐dominated delta, we deployed 23 benthic chambers across ecogeomorphic zones with varying elevation, vegetation, and sediment properties in Wax Lake Delta (Louisiana, USA) in June 2018. Regression analyses indicate that normalized difference vegetation index is a useful predictor of summertime nitrate removal. Mass transfer velocity were approximately three times greater on a vegetated submerged levee (13 mm hr⁻¹), where normalized difference vegetation index was greatest, compared to other locations (4.6 mm hr⁻¹). Two methods were developed to upscale nitrate removal across the delta. The flooded‐delta method integrates spatially explicit potential removal rates across submerged portions of the delta and suggests that intermediate elevations on the delta—including submerged levees—are responsible for 70% of potential nitrate removal despite covering only 33% of the flooded area. The channel network method treats the delta as a network of river channels and suggests that although secondary channels are more efficient than primary channels at removing received nitrate, primary channels collectively contribute more to overall removal because they convey more of the total nitrate load. The two upscaling methods predict similar rates of nitrate removal, equivalent to less than 4% of nitrate entering the delta. To protect coastal waters against high nitrate loads, management policies should aim to reduce upstream nutrient loads.

 

The abundant lakes dotting arctic deltas are hotspots of methane emissions and biogeochemical activity, but seasonal variability in lake extents introduces uncertainty in estimates of lacustrine carbon emissions, typically performed at annual or longer time scales. To characterize variability in lake extents, we analyzed summertime lake area loss (i.e., shrinkage) on two deltas over the past 20 years, using Landsat‐derived water masks. We find that monthly shrinkage rates have a pronounced structured variability around the channel network with the shrinkage rate systematically decreasing farther away from the channels. This pattern of shrinkage is predominantly attributed to a deeper active layer enhancing near‐surface connectivity and storage and greater vegetation density closer to the channels leading to increased evapotranspiration rates. This shrinkage signal, easily extracted from remote sensing observations, may offer the means to constrain estimates of lacustrine methane emissions and to develop process‐based estimates of depth to permafrost on arctic deltas.