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  1. Abstract

    Feedbacks between atmospheric processes like precipitation and land surface fluxes including evapotranspiration are difficult to observe, but critical for understanding the role of the land surface in the Earth System. To quantify global surface-atmosphere feedbacks we use results of a process network (PN) applied to 251 eddy covariance sites from the LaThuile database to train a neural network across the global terrestrial surface. There is a strong land–atmosphere coupling between latent (LE) and sensible heat flux (H) and precipitation (P) during summer months in temperate regions, and betweenHandPduring winter, whereas tropical rainforests show little coupling seasonality. Savanna, shrubland, and other semi-arid ecosystems exhibit strong responses in their coupling behavior based on water availability. Feedback couplings from surface fluxes toPpeaks at aridity (P/potential evapotranspiration ETp) values near unity, whereas coupling with respect to clouds, inferred from reduced global radiation, increases asP/ETpapproaches zero. Spatial patterns in feedback coupling strength are related to climatic zone and biome type. Information flow statistics highlight hotspots of (1) persistent land–atmosphere coupling in sub-Saharan Africa, (2) boreal summer coupling in the central and southwestern US, Brazil, and the Congo basin and (3) in the southern Andes, South Africa and Australia during austral summer. Our data-driven approachmore »to quantifying land atmosphere coupling strength that leverages the global FLUXNET database and information flow statistics provides a basis for verification of feedback interactions in general circulation models and for predicting locations where land cover change will feedback to climate or weather.

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  2. Free, publicly-accessible full text available August 1, 2023
  3. 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
  4. In the United States (US), family forest owners, a group that includes individuals, families, trusts, and estates, are the largest single landowner category, owning approximately one-third of the nation's forests. These landowners' individualized decision-making on forest management has a profound impact on US forest cover and function at both local and regional scales. We sought to understand perceptions among family forest specialists of: climate impacts and adaptation options across different forested US regions; how family forest owners are taking climate adaptation into consideration in their forest management, if at all; and major barriers to more active management for adaptation among family forest owners. We conducted semi-structured interviews with 48 forest experts across the US who work with family forest owners, including extension specialists, state forestry agency employees, and consulting foresters who focus on family forest engagement. Our interviewees shared details on how both climate change impacts and forest management for climate adaptation vary across the US, and they perceived a lack of active forest management by family forest owners. They explained that western forest landowners confronting the imminent threat of catastrophic wildfires are more likely to see a need for active forest management. By contrast, in the east, where mostmore »forestland is privately owned, interviewees said that landowners see relatively fewer climate impacts on their forests and less need for forest management to respond to climate change. Perceived barriers to more active family forest management for climate adaptation include the lack of more robust markets for a wide range of forest products, a higher capacity forestry workforce, education and assistance in planning forest management, and addressing the issue of increased parcelization of family forest lands. We situate these perceptions in conversations on the role of boundary organizations in climate adaptation, how individual adaptation occurs, and how governing methods frame adaptation possibilities.« less
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