Spatial variability

Abstract: Spatial distributions of soil properties at the field and watershed scale may affect yield potential, hydrologic responses, and transport of herbicides and NO⁻₃ to surface or groundwater. Our research describes field-scale distributions and spatial trends for 28 different soil parameters at two sites within a watershed in central Iowa. Two of 27 parameters measured at one site and 10 of 14 parameters measured at the second site were normally distributed. Spatial variability was investigated using semivariograms and the ratio of nugget to total semivariance, expressed as a percentage, was used to classify spatial dependence. A ratio of 75% indicated weak spatial dependence. Twelve parameters at Site one, including organic C, total N, pH, and macroaggregation, and four parameters at Site two, including organic C and total N, were strongly spatially dependent. Six parameters at Site one, including biomass C and N, bulk density, and denitrification, and 9 parameters at Site two, including biomass C and N and bulk density, were moderately spatially dependent. Three parameters at Site one, including NO⁻₃ N and ergosterol, and one parameter at Site two, mineral-associated N, were weakly spatially dependent. Distributions of exchangeable Ca and Mg at Site one were not spatially dependent. Spatial distributions for some soil properties were similar for both field sites. We will be able to exploit these similarities to improve our ability to extrapolate information taken from one field to other fields within similar landscapes.

Abstract: FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. At present over 140 sites are operating on a long-term and continuous basis. Vegetation under study includes temperate conifer and broadleaved (deciduous and evergreen) forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra. Sites exist on five continents and their latitudinal distribution ranges from 70°N to 30°S. FLUXNET has several primary functions. First, it provides infrastructure for compiling, archiving, and distributing carbon, water, and energy flux measurement, and meteorological, plant, and soil data to the science community. (Data and site information are available online at the FLUXNET Web site, http://www-eosdis.ornl.gov/FLUXNET/.) Second, the project supports calibration and flux intercomparison activities. This activity ensures that data from the regional networks are intercomparable. And third, FLUXNET supports the synthesis, discussion, and communication of ideas and data by supporting project scientists, workshops, and visiting scientists. The overarching goal is to provide information for validating computations of net primary productivity, evaporation, and energy absorption that are being generated by sensors mounted on the NASA Terra satellite. Data being compiled by FLUXNET are being used to quantify and compare magnitudes and dynamics of annual ecosystem carbon and water balances, to quantify the response of stand-scale carbon dioxide and water vapor flux densities to controlling biotic and abiotic factors, and to validate a hierarchy of soil–plant–atmosphere trace gas exchange models. Findings so far include 1) net CO 2 exchange of temperate broadleaved forests increases by about 5.7 g C m −2 day −1 for each additional day that the growing season is extended; 2) the sensitivity of net ecosystem CO 2 exchange to sunlight doubles if the sky is cloudy rather than clear; 3) the spectrum of CO 2 flux density exhibits peaks at timescales of days, weeks, and years, and a spectral gap exists at the month timescale; 4) the optimal temperature of net CO 2 exchange varies with mean summer temperature; and 5) stand age affects carbon dioxide and water vapor flux densities.

Abstract: Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements—especially reductions in uncertainty—in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. Our findin...