An analysis of atmospheric water vapor variations over a complex agricultural region using airborne imaging spectrometry.

Understanding atmospheric water vapor patterns can inform regional understanding of water use, climate patterns and hydrologic processes. This research uses Airborne Visible Infrared Imaging Spectrometer (AVIRIS) reflectance and water vapor imagery to investigate spatial patterns of water vapor in California's Central Valley on a June date in 2013, and 2015, and relates these patterns to surface characteristics and atmospheric properties. We analyze water vapor imagery at two scales: regional and agricultural field, to examine how the slope, intercept, and trajectory of water vapor interact with the landscape in a highly diverse and complex agricultural setting. At the field scale, we found significant quadratic relationships between water vapor slope and wind magnitude in both years (p<0.001). Results showed a positive correlation between crop water use and the frequency with which crops showed directional agreement between wind and water vapor (r = 0.23). At the regional scale, we found patterns of water vapor that indicate advection of moisture across the scene. Water vapor slope was inversely correlated to field size with correlations of -0.37, and -0.28 for 2013 and 2015. No correlation was found between green vegetation fraction and vapor slope (r = 0.001 in 2013, r = 0.02 in 2015), but a weak correlation was found for the intercept (r = 0.11 in 2013, r = 0.26 in 2015). These results lead us to conclude that accumulation of water vapor above fields in these scenes is observable with AVIRIS-derived water vapor imagery whereas advection at the field level was inconsistent. Based on these results, we identify new opportunities to use and apply water vapor imagery to advance our understanding of hydro-climatic patterns and applied agricultural water use.

Trees grow on money: urban tree canopy cover and environmental justice.

This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman's correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns.

Land-atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to climate.

This paper summarizes and analyses available data on the surface energy balance of Arctic tundra and boreal forest. The complex interactions between ecosystems and their surface energy balance are also examined, including climatically induced shifts in ecosystem type that might amplify or reduce the effects of potential climatic change. High latitudes are characterized by large annual changes in solar input. Albedo decreases strongly from winter, when the surface is snow-covered, to summer, especially in nonforested regions such as Arctic tundra and boreal wetlands. Evapotranspiration (QE ) of high-latitude ecosystems is less than from a freely evaporating surface and decreases late in the season, when soil moisture declines, indicating stomatal control over QE , particularly in evergreen forests. Evergreen conifer forests have a canopy conductance half that of deciduous forests and consequently lower QE and higher sensible heat flux (QH ). There is a broad overlap in energy partitioning between Arctic and boreal ecosystems, although Arctic ecosystems and light taiga generally have higher ground heat flux because there is less leaf and stem area to shade the ground surface, and the thermal gradient from the surface to permafrost is steeper. Permafrost creates a strong heat sink in summer that reduces surface temperature and therefore heat flux to the atmosphere. Loss of permafrost would therefore amplify climatic warming. If warming caused an increase in productivity and leaf area, or fire caused a shift from evergreen to deciduous forest, this would increase QE and reduce QH . Potential future shifts in vegetation would have varying climate feedbacks, with largest effects caused by shifts from boreal conifer to shrubland or deciduous forest (or vice versa) and from Arctic coastal to wet tundra. An increase of logging activity in the boreal forests appears to reduce QE by roughly 50% with little change in QH , while the ground heat flux is strongly enhanced.

The controls on net ecosystem productivity along an Arctic transect: a model comparison with flux measurements.

Assessments of carbon (C) fluxes in the Arctic require detailed data on both how and why these fluxes vary across the landscape. Such assessments are complicated because tundra vegetation has diverse structure and function at both local and regional scales. To investigate this diversity, the Arctic Flux Study has used the eddy covariance technique to generate ecosystem CO2 -exchange data along a transect in northern Alaska. We use an extant process-based model of the soil-plant-atmosphere continuum to make independent predictions of gross photosynthesis and foliar respiration at 9 of the sites along the transect, using data on local canopy structure and meteorology. We make two key assumptions: (i) soil respiration is constant throughout the flux measurement period, so that the diurnal cycle in CO2 exchange is driven by canopy processes only (except at two sites where a soil respiration-temperature relationship was indicated in the data); and (ii) mosses and lichens play an insignificant role in ecosystem C exchange, even though in some locations their live biomass exceeds 300 g m-2 . We found that even with these assumptions the model could explain much of the dynamics of net ecosystem production (NEP) at sites with widely differing vegetation structure and moss/lichen cover. Errors were mostly associated with the predictions of maximum NEP; the likely cause of such discrepancies was (i) a mismatch between vegetation sampled for characterizing the canopy structure and that contained within the footprint of the eddy covariance flux measurements, or (ii) an increase in daytime soil and root respiration. Thus the model results tended to falsify our first assumption but not our second. We also note evidence for an actual reduction in NEP caused by water stress on warm, dry days at some sites. The model-flux comparison also suggests that photosynthesis may be less sensitive to low temperatures than leaf-level gas-exchange measurements have indicated.