This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.

Agricultural models, such as the Decision Support System for Agrotechnology Transfer cropping system model (DSSAT-CSM), have been developed for predicting crop yield at field and regional scales and to provide useful information for water resources management. A potentially valuable input to agricultural models is soil moisture. Presently, no observations of soil moisture exist covering the entire United States at adequate time (daily) and space (~10 km or less) resolutions desired for crop yield assessments. Data products from NASA’s upcoming Soil Moisture Active Passive (SMAP) mission will fill the gap. The objective of this study is to demonstrate the usefulness of the SMAP soil moisture data in modeling and forecasting crop yields and irrigation amount. A simple, efficient data assimilation algorithm is presented in which the agricultural crop model DSSAT-CSM is constrained to produce modeled crop yield and irrigation amounts that are consistent with SMAP-type data. Numerical experiments demonstrate that incorporating the SMAP data into the agricultural model provides an added benefit of reducing the uncertainty of modeled crop yields when the weather input data to the crop model are subject to large uncertainty.

In June, 2004 and February, 2007, in field tracer studies were conducted on the Hollywood and South Central outfalls, using sulfur hexafluoride (SF6) as a tracer. The objective of these studies was to determine if the tracer could be detected in the farfield at significant distance, and if so, could this data be used to construct a model of the farfield plume. Prior models for farfield plume movement do not appear to comport well with the conditions in southeast Florida. Extensive research was conducted in southeast Florida on 4 outfalls, which led to the development of nearfield dilution equations for same. However farfield modeling of outfall plumes was difficult to accomplish because the tracers used are not detectable for significant distances. The SF6 resolved that problem and as a result the Hollywood outfall was used to construct a model. Two methods were investigated for modeling the plume, 1) the Eureqa formulation method and 2) the Gamma-Curve method. The concentrations in the x-y plane were first found by using the Eureqa formulation to calculate the concentration at each grid point given its depth and the concentration of the centerline at the same latitude. The plume models were generated using MATLAB that matched with the results actually seen in the field.

Previous studies discovered a spatially heterogeneous expansion of Siberian larch into the tundra of the Polar Urals (Russia). This study reveals that the spatial pattern of encroachment of tree stands is related to environmental factors including topography and snow cover. Structural and allometric characteristics of trees, along with terrain elevation and snow depth were collected along a transect 860 m long and 80 m wide. Terrain curvature indices, as representative properties, were derived across a range of scales in order to characterize microtopography. A density-based clustering method was used here to analyze the spatial and temporal patterns of tree stems distribution. Results of the topographic analysis suggest that trees tend to cluster in areas with convex surface. The clustering analysis also indicates that the patterns of tree locations are linked to snow distribution. Records from the earliest campaign in 1960 show that trees lived mainly at the middle and bottom of the transect across the areas of high snow depth. As trees expanded uphill with a warming climate in recent decades, the high snow depth areas also shifted upward creating favorable conditions for recent trees growth at locations that were previously covered with heavy snow. The identified landscape signatures of increasing above-ground Arctic biomass in terms of tall vegetation can facilitate scaling to larger area regions.

No abstract