A Large-Eddy Simulation Study of the Convective Boundary Layer over Philadelphia during the 1999 Summer NE-OPS Campaign

This study presents a large-eddy simulation (LES) study of the convective boundary layer on August 1, 1999 over Philadelphia, PA during a summer ozone episode. The study is an evaluation of the Colorado State University's Regional Atmospheric Modeling System Version 4.3 (RAMS4.3) with the LES option using Northeast Oxidant and Particulate Study (NE-OPS) data. Simulations were performed with different imposed sensible heat fluxes at the ground surface. The model was initialized with the atmospheric sounding data collected at Philadelphia at 1230 UTC and model integrations continued till 2130 UTC. The resulting mean profiles of temperature and humidity obtained from the LES model were compared with atmospheric soundings, tethered balloon and aircraft data collected during the NE-OPS 1999 field campaign. Also the model-derived vertical profiles of virtual temperature were compared with NE-OPS Radio Acoustic Sounder System (RASS) data while the humidity profiles were compared with NE-OPS lidar data. The comparison of the radiosonde data with the LES model predictions suggests that the growth of the mixing layer is reasonably well simulated by the model. Overall, the agreement of temperature predictions of the LES model with the radiosonde observations is good. The model appears to underestimate humidity values for the case of higher imposed sensible heat flux. However, the humidity values in the mixing layer agree quite well with radiosonde observations for the case of lower imposed sensible heat flux. The model-predicted temperature and humidity profiles are in reasonable agreement with the tethered balloon data except for some small overestimation of temperature at lower layers and some underestimation of humidity values. However, the humidity profiles as simulated by the model agree quite well with the tethered balloon data for the case of lower imposed sensible heat flux. The model-predicted virtual temperature profile is also in better agreement with RASS data for the case of lower imposed sensible heat flux. The model-predicted temperature profile further agrees quite well with aircraft data for the case of lower imposed heat flux. However, the relative humidity values predicted by the model are lower compared with the aircraft data. The model-predicted humidity profiles are only in partial agreement with the lidar data. The results of this study suggest that the explicitly resolved energetic eddies seem to provide the correct forcing necessary to produce good agreement with observations for the case of an imposed sensible heat flux of 0.1 K m s^–1 at the surface.     

Climate change effects on pesticide usage reduction efforts: a case study in China

China has announced plans to stabilize its pesticide use by 2020. Yet, future climate change will possibly increase the difficulty of meeting this goal. This study uses econometric estimation to explore how climate impacts Chinese pesticide usage and subsequently to project the future implications of climate change on pesticide use. The results indicate that both atmospheric temperature and precipitation increase pesticide usage. Under current climate change projections, pesticide usage will rise by +1.1 to 2.5% by 2040, +2.4 to 9.1% by 2070, and +2.6 to 18.3% by 2100. Linearly extrapolating the results to 2020 yields an approximately 0.5 to 1.2% increase. Thus, to achieve stabilization, more severe actions are needed to address this increase. Possible actions to achieve the reductions needed include using better monitoring and early warning networks so as to permit early responses to climate change-stimulated increases, enhancing information dissemination, altering crop mix, and promoting nonchemical control means. Additionally, given that increased pesticide usage generally increases health and environmental damage, there may be a need to more widely disseminate safe application procedure information while also strengthening compliance with food safety regulations. Furthermore, pest control strategies will need to be capable of evolving as climate change proceeds. Globally, efforts could be made to (1) scale up agrometeorological services, especially in developing countries; (2) use international frameworks to better align the environmental and health standards in developing countries with those in developed countries; and (3) adapt integrated pest management practices to climate change, especially for fruits and vegetables.     

Spondyloarthropathy in vertebrae of the aquatic Cretaceous snake <Emphasis Type="Italic">Lunaophis aquaticus</Emphasis>, and its first recognition in modern snakes

Inflammatory arthritis is documented for the first time in snakes. Ossification of the intervertebral capsule and zygapophyseal joints resulting in segmental vertebral fusion was observed in the aquatic Cretaceous snake Lunaophis aquaticus. Such pathologic alterations are pathognomonic for the spondyloarthropathy form of inflammatory arthritis. A survey of 2144 snakes in recent collections, performed to identify Holocene prevalence, revealed only two occurrences in extant snakes. The findings in Bitis gabonica and Elaphe taeniura were indistinguishable from those noted in Lunaophis aquaticus and identical to those previously recognized in modern varanids. The pathology likely represents a form of reactive arthritis related to enteropathic infection. While the disease probably did not affect general locomotion, its vertebral column position may have compromised mating.     

Quantifying structural physical habitat attributes using LIDAR and hyperspectral imagery

Structural physical habitat attributes include indices of stream size, channel gradient, substrate size, habitat complexity, and riparian vegetation cover and structure. The Environmental Monitoring and Assessment Program (EMAP) is designed to assess the status and trends of ecological resources at different scales. High-resolution remote sensing provides unique capabilities in detecting a variety of features and indicators of environmental health and condition. LIDAR is an airborne scanning laser system that provides data on topography, channel dimensions (width, depth), slope, channel complexity (residual pools, volume, morphometric complexity, hydraulic roughness), riparian vegetation (height and density), dimensions of riparian zone, anthropogenic alterations and disturbances, and channel and riparian interaction. Hyperspectral aerial imagery offers the advantage of high spectral and spatial resolution allowing for the detection and identification of riparian vegetation and natural and anthropogenic features at a resolution not possible with satellite imagery. When combined, or fused, these technologies comprise a powerful geospatial data set for assessing and monitoring lentic and lotic environmental characteristics and condition.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Amazonian Tree Mortality during the 1997 El Niño Drought

Abstract: In 1997, the Amazon Basin experienced an exceptionally severe El Niño drought. We assessed effects of this rare event on mortality rates of trees in intact rain forest based on data from permanent plots. Long-term (5- to 13-year) mortality rates averaged only 1.12% per year prior to the drought. During the drought year, annual mortality jumped to 1.91% but abruptly fell back to 1.23% in the year following El Niño. Trees dying during the drought did not differ significantly in size or species composition from those that died previously, and there was no detectable effect of soil texture on mortality rates. These results suggest that intact Amazonian rainforests are relatively resistant to severe El Niño events.