LATE QUATERNARY CHANGES IN PINYON AND JUNIPER DISTRIBUTIONS IN THE WHITE MOUNTAIN REGION OF CALIFORNIA AND NEVADA

Data from packrat middens are used to reconstruct the migration of single-leaf pinyon (Pinus monophylla) and Utah juniper (Juniperus osteosperma) in the White Mountain region of California and Nevada for the last 20,000 years. Today this region is characterized by arid conditions with dry summers. Pinyon woodlands, which are favored by dry summer conditions, are dominant at middle elevations of the White Mountains. The midden record indicates that during the late Pleistocene and early Holocene, juniper was dominant at elevations now occupied by desert shrubs. A late Pleistocene-early Holocene record of desert shrubs was found only in the double rainshadow of the Sierra Nevada and the White Mountains. Pinyon entered the area during the Holocene (ca. 9000 yr B.P.) and subsequently became more important than juniper. During the late Holocene, desert shrubs became established at the lower elevation sites that they now occupy. The late Pleistocene-early Holocene record indicates that there may have been a more zonal upper-level wind flow and associated penetration of Pacific moisture, as indicated by the occurrence of desert shrubs downwind of the north-south-trending mountain ranges. Zonal flow probably became less important during the early Holocene based on the increasing amount of pinyon. The increase in pinyon also may indicate a change from an even seasonal distribution of precipitation to the dry summer conditions presently found in the region. [Key words: biogeography, packrat middens, Great Basin paleoenvironments, Pinus monophylla, Juniperus osteosperma, White Mountains.]     

A SIMPLE SOIL MOISTURE INDEX TO FORECAST CROP YIELDS

Neutron probe soil moisture measurements obtained biweekly during the growing season between 1982 and 1991 from multiple depths under grass-covered plots at 17 Illinois Climate Network sites are used to forecast crop yields. A Soil Moisture Index (SMIX) that combines the effect of intensity, duration, and timing of drought or excessively wet conditions was computed by integrating the quantity of available soil moisture throughout the rooting zone over the growing season. Relationships between the SMIX values and crop yields are evaluated at county, regional, and statewide scales. Coefficients of determination (r ²) for relationships between the SMIX values and maize, soybeans, and hay yields at the statewide level are 0.88, 0.74, and 0.81, respectively, when the period of integration is terminated at the end of the growing season. This new soil index can be employed to forecast yields as early as 12 weeks before harvest for the state of Illinois. However, predictions with RMSE ≤ 10% of the mean yield can be achieved only for SMIX integration periods ending 5, 9, and 6 weeks before harvest for maize, soybeans, and hay, respectively. Nomograms are presented for using the relationships between the SMIX values and crop yields to forecast Illinois's major crops well before harvest.     

Satellite remote sensing of forest type and landcover in the subalpine forest region,Kananaskis Valley,Alberta

Landsat Thematic Mapper (TM) imagery and a digital elevation model (DEM) of the Kananaskis Valley in southwestern Alberta have been used to separate three forest types and eight landcover classes with mapping accuracies up to 76% overall. Image transformations based on a principal components analysis (PCA) were used to distinguish vegetation type and separate surface features in visual interpretations, and to reduce the 10 channel data set (TM 1–7, elevation, slope and incidence) to a more manageable 7 channel data set (PCA 1–4, elevation, slope and incidence). The DEM was shown to be critical in providing explanation of surface cover variability even though the original model was produced from medium scale aerial photography on a relatively coarse 100 metre grid. Discrimination increased up to 50% for pure stands of Lodgepole Pine (Pinus contorta Dougl.) and Englemann Spruce (Picea englemanii Parry) based on analysis of 100 pixels in test areas. Overall increases in map accuracy were between 2 and 11%. Success at this level of classification is required prior to detailed ecological study and modelling of mountain vegetation productivity at the community level using current satellite and aerial remote sensing technology.     

An application of spatial filtering techniques for land cover mapping using TM‐images

Land cover classification using satellite imagery is commonly based on spectral information in the individual pixels. The information in neighbouring pixels is ignored. Spatial filtering techniques using information present in neighbouring pixels may however, contribute significantly to an improvement of the classification. In this study different methods of spatial filtering are applied to a part of a TM‐scene of Kenya to assess their relative reliability. The study area is characterized by extended, relatively homogeneous areas of eucalyptus forests and tea estates and by fragmentated areas of agricultural land use. Spectral information was combined with the results of different spatial filtering methods and then classified. The spatial filtering techniques applied were texture calculation by means of variance, “median minus original” filtering and fractal dimension computations using several sizes of templates. The obtained classification accuracy of several image combinations is compared using the percentage correctly classified and using an overall accuracy measure: the Kappa coefficient. It is concluded that in this case the spatial filtering techniques only slightly improve the classification. From the applied filtering methods texture calculation by means of variance yielded the best results.     

Effects of an Omnivorous Katydid,Salinity, and Nutrients on a Planthopper-<Emphasis Type="Italic">Spartina</Emphasis> Food Web

Top–down and bottom–up effects interact to structure communities, especially in salt marshes, which contain strong gradients in bottom–up drivers such as salinity and nutrients. How omnivorous consumers respond to variation in prey availability and plant quality is poorly understood. We used a mesocosm experiment to examine how salinity, nutrients, an omnivore (the katydid Orchelimum fidicinium) and an herbivore (the planthopper Prokelisia spp.) interacted to structure a simplified salt marsh food web based on the marsh grass Spartina alterniflora. Bottom–up effects were strong, with both salinity and nutrients decreasing leaf C/N and increasing Prokelisia abundance. Top–down effects on plants were also strong, with both the herbivore and the omnivore affecting S. alterniflora traits and growth, especially when nutrients or salt were added. In contrast, top–down control by Orchelimum of Prokelisia was independent of bottom–up conditions. Orchelimum grew best on a diet containing both Spartina and Prokelisia, and in contrast to a sympatric omnivorous crab, did not shift to an animal-based diet when prey were present, suggesting that it is constrained to consume a mixed diet. These results suggest that the trophic effects of omnivores depend on omnivore behavior, dietary constraints, and ability to suppress lower trophic levels, and that omnivorous katydids may play a previously unrecognized role in salt marsh food webs.     

Comparison and relative quality assessment of the GLC2000, GLOBCOVER,MODIS and ECOCLIMAP land cover data sets at the African continental scale

Land cover dynamics at the African continental scale is of great importance for global change studies. Actually, four satellite-derived land cover maps of Africa now available, e.g. ECOCLIMAP, GLC2000, MODIS and GLOBCOVER, are based on images acquired in the 2000s. This study aims at stressing the compliances and the discrepancies between these four land cover classifications systems. Each of them used different mapping initiatives and relies on different mapping standards, which supports the present investigation. In order to do a relative comparison of the four maps, a preamble was to reconcile their thematic legends into more aggregated categories after a projection into the same spatial resolution. Results show that the agreement between the four land cover products is between 56 and 69%. While all these land cover datasets show a reasonable agreement in terms of surface types and spatial distribution patterns, mapping of heterogeneous landscapes in the four products is not very successful. Land cover products based on remote sensing imagery can indeed significantly be improved by using smarter algorithms, better timing of image acquisition, improved class definitions. Either will help to improve the accuracy of future land cover maps at the African continental scale. Data producers may use the areas of spatial agreement for training area selection while users might need to verify the information in the areas of disagreement using additional data sources.     

Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980�C2010 period

Several different inventories of global and regional anthropogenic and biomass burning emissions are assessed for the 1980?C2010 period. The species considered in this study are carbon monoxide, nitrogen oxides, sulfur dioxide and black carbon. The inventories considered include the ACCMIP historical emissions developed in support of the simulations for the IPCC AR5 assessment. Emissions for 2005 and 2010 from the Representative Concentration Pathways (RCPs) are also included. Large discrepancies between the global and regional emissions are identified, which shows that there is still no consensus on the best estimates for surface emissions of atmospheric compounds. At the global scale, anthropogenic emissions of CO, NO_x and SO₂ show the best agreement for most years, although agreement does not necessarily mean that uncertainty is low. The agreement is low for BC emissions, particularly in the period prior to 2000. The best consensus is for NO_x emissions for all periods and all regions, except for China, where emissions in 1980 and 1990 need to be better defined. Emissions of CO need better quantification in the USA and India for all periods; in Central Europe, the evolution of emissions during the past two decades needs to be better determined. The agreement between the different SO₂ emissions datasets is rather good for the USA, but better quantification is needed elsewhere, particularly for Central Europe, India and China. The comparisons performed in this study show that the use of RCP8.5 for the extension of the ACCMIP inventory beyond 2000 is reasonable, until more global or regional estimates become available. Concerning biomass burning emissions, most inventories agree within 50?C80%, depending on the year and season. The large differences between biomass burning inventories are due to differences in the estimates of burned areas from the different available products, as well as in the amount of biomass burned.     

On the coupling between vegetation and the atmosphere

Recent studies suggest that vegetation can drive large-scale atmospheric circulations and substantially influence the hydrologic cycle. We present observational evidence to quantify the extent of coupling between vegetation and the overlying atmosphere. Within the context of vegetation–atmospheric interactions, we reanalyze existing climatological data from springtime leaf emergence, emissivity, dew point temperatures, and historical records of precipitation and forest coverage. We construct new rainfall transects based on a robust global climatology. Using isotopic analysis of precipitation, we find that rain in Amazonia comes primarily from large-scale weather systems coupling interior regions to the ocean and is not directly driven by local evaporation. We find that changes in vegetative cover and state influence the temperature and moisture content of the surface and atmospheric boundary layer but are not reflected in observable precipitation changes. This analysis reaffirms the view that changes in precipitation over continental reaches are a product of complex processes only partly influenced but not controlled by local water sources or vegetation.     

High-resolution subtropical summer precipitation derived from dynamical downscaling of the NCEP/DOE reanalysis: how much small-scale information is added by a regional model?

This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5° latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP/Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20?km resolution for 16 summer seasons (1990?C2005). The RSM produces realistic details in the regional summer precipitation at 20?km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93% (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92% (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM.