Yu Zhang

Permafrost-affected ecosystems of the Arctic-boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the similar to 80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl). Copyright statement. (c) California Institute of Technology. Government funding acknowledged.

Coastal wetlands provide critical ecosystem services but are experiencing disruptions caused by inundation and saltwater intrusion under intensified climate change, sea-level rise, and anthropogenic activities. Recent studies have shown that these disturbances downgraded coastal wetlands mainly through affecting their hydrological processes. However, research on what is the most critical driver for wetland downgrading and how it affects coastal wetlands is still in its infancy. This study examined drivers of three types of wetland downgrading, including woody wetland loss, emergent herbaceous wetland loss, and woody wetlands converting to emergent herbaceous wetlands. By using random forest classification models for the wetland ecosystems in the Alligator River National Wildlife Refuge, North Carolina, USA, during 1995–2019, we determined the relative importance of different hydrogeomorphic processes and the dominant variables in driving the wetland downgrading. Results showed that random forest classification models were accurate (> 97 % overall accuracy) in classifying wetland downgrading. Multiple hydrogeomorphic variables collectively contributed to the coastal wetland downgrading. However, the dominant control factors varied across different types of wetland downgrading. Woody wetlands were most susceptible to saltwater intrusion and were likely to downgrade if the saltwater table was shallower than 0.2 m below the land surface. In contrast, emergent herbaceous wetlands were most vulnerable to inundation and drought. The favorable groundwater table for emergent herbaceous wetlands was between 0.34 m above the land surface and 0.32 m below the land surface, beyond which the emergent herbaceous wetland tended to disappear. For downgraded woody wetlands, their distance to canals/ditches played a crucial role in determining their fates after downgrading. The machine learning approach employed in this study provided critical knowledge about the thresholds of hydrogeomorphic variables for the downgrading of different types of coastal wetlands. Such information can help guide effective and targeted coastal wetland conservation, management, and restoration measures. [Display omitted] •Wetland downgrading was accurately (> 97 %) classified.•Dominant hydrological mechanisms varied for different types of wetland downgrading.•Woody wetlands were most susceptible to saltwater intrusion.•Emergent herbaceous wetlands were most vulnerable to inundation and drought.•Distances to canals were key to determining the fates of downgraded woody wetlands.

Climate change, the rising air temperature and changes in the intensity and frequency of rainfall, and sea level rise (SLR), represents one of the most important threats to coastal wetlands that have numerous ecosystem services from erosion and water quality control to wildlife habitat. Climate change-induced disturbances affect the sustainability of coastal wetland ecosystems mainly through altering their hydrologic functions. However, how to assess wetland hydrological resilience, the ability of wetland hydrology to recover from climate disturbances remain challenging. This chapter first summarizes current knowledge of the coastal hydrologic cycle and the influence of climate change on the coastal hydrologic cycle. Then, we define hydrologic resilience, identify the hydrologic conditions, and quantify the hydrologic resilience. Last, we present a case study on bottomland hardwood forests along the Atlantic coast. We applied a physically based watershed-scale wetland hydrological model (PIHM-Wetland) to the coastal wetland system and quantified the hydrologic resilience in response to climate extreme events during the recent 20 years using a distributed-system approach. The case study shows that the metrics to quantify hydrologic resilience to drought, extreme rainfall events, and sea level rise are effective and may be appliable to other similar regions.

Submarine groundwater discharge (SGD) contributes to the physical and chemical characters of coastal waters by discharging nutrients and contaminants, significantly impacting regional marine ecosystems and contributing to ocean chemical budgets. However, such groundwater discharge varies dramatically across scales and is often not comparable due to different model assumptions and field designs. We used a hydrologic model with integration of fundamental surface and subsurface processes to simulate the coastline level fresh SGD for the Crete Island in the Mediterranean Sea. The modeled hydrological processes suggested that fresh SGD substantially contributes to water flow entering the Mediterranean Sea (2.3 x 10(8) m(3)/yr), amounting to 31% of river discharge and 14% of precipitation. Spatially, fresh SGD varied from 2.4 m(3)/yr/m to 13.4 x 10(4) m(3)/yr/m, with an average of 2.6 x 10(3) m(3)/yr/m. The local maxima were commonly associated with river mouths reflecting larger hydraulic gradients and higher permeable structures. Temporally, fresh SGD was impacted by episodic precipitation in a delayed and prolonged pattern. We found that fresh SGD variability at the coastline segment level was compared to point measurements and fresh SGD magnitudes summered up to the catchment level were consistent with global products. Our results suggest the coupled surface-subsurface hydrologic modeling approach is a promising strategy to quantify and partition large-scale water budgets down to point observations that typically do not capture the full range of fresh SGD dynamics.

The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal timescales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic-ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37 year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an optimization algorithm to improve the fit between observed and simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored internal heat), identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing. To qualify which external and internal factors were most important in driving the interannual variation in summer anoxia, we applied a random-forest classifier and multiple linear regressions to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production). Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50-60 d. The summer heat budget, the timing of thermal stratification, and the gross primary production in the epilimnion prior to summer stratification were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. Interannual variability in anoxia was largely driven by physical factors: earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. A measured step change upward in summer anoxia in 2010 was unexplained by the GLM-AED2 model. Although the cause remains unknown, possible factors include invasion by the predacious zooplankton Bythotrephes longimanus. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region.