Fluxes of dissolved organic matter in larch forests in the cryolithozone of central Siberia

Fluxes of dissolved organic matter (DOM) in larch biogeocenoses and its export from the drainage basin have been studied in the zone of continuous permafrost. A comparative assessment of DOM input into the soil has been made on slopes of northern and southern exposures (as variants reflecting the current state and warming). The dynamics of DOM export in a creek depending on the increasing depth of the active soil horizon in the drainage area have been revealed. It is concluded that an increase in the depth of the seasonally thawing layer induced by global warming will not have any significant effect on the amount of annual DOM export. Reduction of DOM export may be expected upon a decrease in litter stocks under the effect of their mineralization and forest fires.     

WATER SUPPLY IMPACTS OF NUCLEAR FALL1

ABSTRACT: “Nuclear winter,” more properly called “nuclear fall,” could be caused by injection of large amounts of dust into the atmosphere. Besides causing a decrease in temperature, it could be accompanied by “nuclear drought,” a catastrophic decrease in precipitation. Dry land agriculture would then be impossible, and municipal, industrial, and irrigation water supplies would be diminished. It has been argued that nuclear winter/fall poses a much greater threat to human survival than do fall out or the direct impacts of a conflict. However, this does not appear to be true, at least for the U.S. Even under the unprecedented drought that could result from nuclear fall, water supplies would be available for many essential activities. For the most part, ground water supplies would be relatively invulnerable to nuclear drought, and adequate surface supplies would be available for potable uses. This assumes that conveyance facilities and power supplies survive a conflict largely intact or can be repaired.     

Yield response of potato to spatially patterned nitrogen application

Although crop response to nitrogen fertilization has long been studied, classical experimental designs have led to inadequate accounting of spatial variability in field properties and yield response. Analytical methods to explicitly account for spatial variability now exist but the complementary modification of experimental design is still developing. There is a need to combine these analytical methods with non-traditional experimental design. A 2-year study was implemented to assess the response of potato (Solanum tuberosum cv. Kennebec) yield to nitrogen fertilizer rate. We used a transect-type plot design where four nitrogen treatments (0, 56, 112, and 280 kg N ha^?1) were applied systematically in a continuous sinusoidal pattern along longitudinal transects. Measured field properties included topography, soil texture, pre-application soil nitrate levels, and plant available soil water content. A random field linear model was used to simultaneously account for treatment effects and soil properties. The results showed that treatment effects were significantly different from each other; however, if spatially correlated errors were accounted for, these differences were smaller and significance levels lower. Nitrogen response functions varied widely throughout the field. Of the covariates, only clay content proved important in explaining spatial differences in response to N. The sinusoidal response pattern of N was similar over the 2 years but the amplitudes varied due to differences in weather. Interactions between uncharacteristically high rainfall and a sandy field soil may have minimized discernable effects of the other covariates. The results demonstrated how the statistical analysis of potato response to a patterned application of nitrogen fertilizer can take advantage of spatial correlations to understand the response of potato to nitrogen application over larger areas.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.