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  1. We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values formore »the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.« less
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  3. Abstract. Environmental science is increasingly reliant on remotely sensedobservations of the Earth's surface and atmosphere. Observations frompolar-orbiting satellites have long supported investigations on land coverchange, ecosystem productivity, hydrology, climate, the impacts ofdisturbance, and more and are critical for extrapolating (upscaling)ground-based measurements to larger areas. However, the limited temporalfrequency at which polar-orbiting satellites observe the Earth limits ourunderstanding of rapidly evolving ecosystem processes, especially in areaswith frequent cloud cover. Geostationary satellites have observed theEarth's surface and atmosphere at high temporal frequency for decades, andtheir imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solarmore »radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarseresolution of ABI observations through multisensor fusion to resolvelandscape heterogeneity and to leverage observations from ABI to study thecarbon cycle and ecosystem function at unprecedented temporal frequency.« less
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    Abstract. American bison (Bison bison L.) have recovered from the brink ofextinction over the past century. Bison reintroduction creates multipleenvironmental benefits, but impacts on greenhouse gas emissions are poorlyunderstood. Bison are thought to have produced some 2 Tg yr−1 of theestimated 9–15 Tg yr−1 of pre-industrial enteric methane emissions,but few measurements have been made due to their mobile grazing habits andsafety issues associated with measuring non-domesticated animals. Here, wemeasure methane and carbon dioxide fluxes from a bison herd on an enclosedpasture during daytime periods in winter using eddy covariance. Methaneemissions from the study area were negligible in the absence of bison(mean ± standard deviation = −0.0009 ± 0.008 µmol m−2 s−1) and were significantly greater than zero,0.048 ± 0.082 µmol m−2 s−1, with a positively skeweddistribution, when bison were present. We coupled bison location estimatesfrom automated camera images with two independent flux footprint models tocalculate a mean per-animal methane efflux of 58.5 µmol s−1 per bison, similar to eddy covariance measurements ofmethane efflux from a cattle feedlot during winter. When we sum theobservations over time with conservative uncertainty estimates we arrive at81 g CH4 per bison d−1 with 95 % confidence intervalsbetween 54 and 109 g CH4 per bison d−1. Uncertainty wasdominated by bison location estimates (46 % of the total uncertainty),then the flux footprint model (33 %) and the eddy covariance measurements(21 %), suggesting that making higher-resolution animal location estimatesis a logical starting point formore »decreasing total uncertainty. Annualmeasurements are ultimately necessary to determine the full greenhouse gasburden of bison grazing systems. Our observations highlight the need tocompare greenhouse gas emissions from different ruminant grazing systems anddemonstrate the potential for using eddy covariance to measure methaneefflux from non-domesticated animals.« less
  5. Abstract. Global ecosystems vary in their function, and therefore resilience to disturbance, as a result of their location on Earth, structure, and anthropogenic legacy. Resilience can therefore be difficult to describe solely based on energy partitioning, as it fails to effectively describe how ecosystems use available resources, such as soil moisture. Maximum entropy production (MEP) has been shown to be a better metric to describe these differences as it relates energy use efficiencies of ecosystems to the availability of resources. We studied three sites in a longleaf pine ecosystem with varying levels of anthropogenic legacy and biodiversity, all of which were exposed to extreme drought. We quantified their resilience from radiative, metabolic and overall MEP ratios. Sites with anthropogenic legacy had ~10% lower overall and metabolic energy use efficiency compared to more biodiverse sites. This resulted in lower resilience and a delay in recovery from drought by ~1 year. Additionally, a set of entropy ratios to determine metabolic and overall energy use efficiency explained more clearly site-specific ecosystem function, whereas the radiative entropy budget gave more insights about structural complexities at the sites. Our study provides foundational evidence of how MEP can be used to determine resiliencymore »across ecosystems globally.

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