Biogeochemistry of upland to wetland soils, sediments, and surface waters across Mid-Atlantic and Great Lakes coastal interfaces.

Transferable and mechanistic understanding of cross-scale interactions is necessary to predict how coastal systems respond to global change. Cohesive datasets across geographically distributed sites can be used to examine how transferable a mechanistic understanding of coastal ecosystem control points is. To address the above research objectives, data were collected by the EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Consortium - a regionally distributed network of researchers that collaborated on experimental design, methodology, collection, analysis, and publication. The EXCHANGE Consortium collected samples from 52 coastal terrestrial-aquatic interfaces (TAIs) during Fall of 2021. At each TAI, samples collected include soils from across a transverse elevation gradient (i.e., coastal upland forest, transitional forest, and wetland soils), surface waters, and nearshore sediments across research sites in the Great Lakes and Mid-Atlantic regions (Chesapeake and Delaware Bays) of the continental USA. The first campaign measures surface water quality parameters, bulk geochemical parameters on water, soil, and sediment samples, and physicochemical parameters of sediment and soil.

Modeling the mechanisms of conifer mortality under seawater exposure.

Relative sea level rise (SLR) increasingly impacts coastal ecosystems through the formation of ghost forests. To predict the future of coastal ecosystems under SLR and changing climate, it is important to understand the physiological mechanisms underlying coastal tree mortality and to integrate this knowledge into dynamic vegetation models. We incorporate the physiological effect of salinity and hypoxia in a dynamic vegetation model in the Earth system land model, and used the model to investigate the mechanisms of mortality of conifer forests on the west and east coast sites of USA, where trees experience different form of sea water exposure. Simulations suggest similar physiological mechanisms can result in different mortality patterns. At the east coast site that experienced severe increases in seawater exposure, trees loose photosynthetic capacity and roots rapidly, and both storage carbon and hydraulic conductance decrease significantly within a year. Over time, further consumption of storage carbon that leads to carbon starvation dominates mortality. At the west coast site that gradually exposed to seawater through SLR, hydraulic failure dominates mortality because root loss impacts on conductance are greater than the degree of storage carbon depletion. Measurements and modeling focused on understanding the physiological mechanisms of mortality is critical to reducing predictive uncertainty.

Wildfires increasingly impact western US fluvial networks.

Wildfires are increasing globally in frequency, severity, and extent, but their impact on fluvial networks, and the resources they provide, remains unclear. We combine remote sensing of burn perimeter and severity, in-situ water quality monitoring, and longitudinal modeling to create the first large-scale, long-term estimates of stream+river length impacted by wildfire for the western US. We find that wildfires directly impact ~6% of the total stream+river length between 1984 and 2014, increasing at a rate of 342 km/year. When longitudinal propagation of water quality impacts is included, we estimate that wildfires affect ~11% of the total stream+river length. Our results indicate that wildfire activity is one of the largest drivers of aquatic impairment, though it is not routinely reported by regulatory agencies, as wildfire impacts on fluvial networks remain unconstrained. We identify key actions to address this knowledge gap and better understand the growing threat to fluvial networks, water security, and public health risks.

Water quality impacts of urban and non-urban arid-land runoff on the Rio Grande.

Urban surface runoff from storms impacts the water quality dynamics of downstream ecosystems. While these effects are well-documented in mesic regions, they are not well constrained for arid watersheds, which sustain longer dry periods, receive intense but short-lived storms, and where stormwater drainage networks are generally isolated from sewage systems. We used a network of high-frequency in situ water quality sensors located along the Middle Rio Grande to determine surface runoff origins during storms and track rapid changes in physical, chemical, and biological components of water quality. Specific conductivity (SpCond) patterns were a reliable indicator of source, distinguishing between runoff events originating primarily in urban (SpCond sags) or non-urban (SpCond spikes) catchments. Urban events were characterized by high fluorescent dissolved organic matter (fDOM), low dissolved oxygen (including short-lived hypoxia <2 mg/L), smaller increases in turbidity and varied pH response. In contrast, non-urban events showed large turbidity spikes, smaller dissolved oxygen sags, and consistent pH sags. Principal component analysis distinguished urban and non-urban events by dividing physical and biogeochemical water quality parameters, and modeling of DO along the same reach demonstrated consistently higher oxygen demand for an urban event compared to a non-urban event. Based on our analysis, urban runoff poses more potential ecological harm, while non-urban runoff poses a larger problem for drinking water treatment. The comparison of our results to other reports of urban stormwater quality suggest that water quality responses to storm events in urban landscapes are consistent across a range of regional climates.

Analyzing and comparing complex environmental time series using a cumulative sums approach.

Cumulative sums (Cusums) are a simple, efficient statistical method developed for process control and increasingly used to determine underlying features of time series. Here, two useful applications of Cusums to environmental time series are presented: Cusums in the time domain and plotting Cusum-transformed variables against non-transformed variables to extract meaning in the context of driver-response relationships. These statistical analyses are simple to conduct and provide valuable information about trends, patterns and thresholds of time-series over time and in relation to potential driver variables. In addition, this work investigates the robustness of the Cusum transform to various characteristics of environmental time series that challenge conventional statistical methods. In summary, this work presents: •Cusum methods to derive meaning from complex environmental time series.•Effects of common time series issues on the Cusums method.•Application to real-world datasets.