AN AUTO-CAD-BASED WATERSHED INFORMATION SYSTEM FOR THE HYDROLOGIC MODEL HEC-11

ABSTRACT: A micro computer based Watershed Information System (W.LS.) is developed to assist in the preparation of input files for the hydrologic simulation model HEC-1. This system consists of three phases. Phase I utilizes the capabilities of AutoCAD version 9 and three programs, BASINS, PLANES, and CHANNELS, to extract, organize, and display watershed data. Phase II uses the program CN to calculate some HEC-1 parameter values. Phase II utilizes the program HECUPDATE to create HEC-1 input files. The system input includes topographic, soils, land use, watershed geometry data, and a skeletal HEC-1 input file. Output from the system includes a summary User Reference File, a Soils File, a Land Use File, a Watershed Geometry File, a Curve Number File, and a HEC-1 input file, which is ready to run. The W.I.S. has been applied to Macks Creek Watershed in southwest Idaho.     

Predicting the distributions of marine organisms at the global scale

We present and evaluate AquaMaps, a presence-only species distribution modelling system that allows the incorporation of expert knowledge about habitat usage and was designed for maximum output of standardized species range maps at the global scale. In the marine environment there is a significant challenge to the production of range maps due to large biases in the amount and location of occurrence data for most species. AquaMaps is compared with traditional presence-only species distribution modelling methods to determine the quality of outputs under equivalently automated conditions. The effect of the inclusion of expert knowledge to AquaMaps is also investigated. Model outputs were tested internally, through data partitioning, and externally against independent survey data to determine the ability of models to predict presence versus absence. Models were also tested externally by assessing correlation with independent survey estimates of relative species abundance. AquaMaps outputs compare well to the existing methods tested, and inclusion of expert knowledge results in a general improvement in model outputs. The transparency, speed and adaptability of the AquaMaps system, as well as the existing online framework which allows expert review to compensate for sampling biases and thus improve model predictions are proposed as additional benefits for public and research use alike.     

Arctic source for elevated atmospheric mercury (Hg0) in the western Bering Sea in the summer of 2013

Measurements of gaseous elemental mercury(Hg~0) in the marine boundary layer of the western Bering Sea were performed using an automatic mercury analyzer RA 915 +(Ltd. "Lumex", St. Petersburg, Russia) aboard the Russian research vessel Academician M.A.Lavrentev from 3 to 20 August 2013. Hg~0 concentrations varied from 0.3 to 2.1 ng/m~3(n = 4783);the average value(1.1 ± 0.3 ng/m~3) was lower than both the background range of the Northern Hemisphere(1.5–1.7 ng/m~3) and average values previously observed in the Bering Sea, and corresponded to the background concentrations of the Southern Hemisphere(1.1–1.3 ng/m~3).Maximum Hg~0 concentrations were observed within air masses that came from the lower troposphere of the central Arctic. Under these conditions, Hg~0 ranged between 1.1 and 2.1 ng/m~3 with an average of 1.5 ± 0.2 ng/m~3(n = 1183). Except for these periods, Hg~0 concentrations during the rest of the study varied from 0.3 to 1.8 ng/m~3, with an average value of 1.0 ± 0.2 ng/m~3(n = 3600). Our results support the hypothesis that, in the summer, air masses from the central Arctic Ocean can be an exporter of mercury to lower latitudes. Perhaps the atmospheric transport of elevated concentrations of Hg~0 into lower latitudes may have implications for the biologic and economic health of important fisheries, such as the Bering Sea.     

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.     

Impact of generated solar radiation on simulated crop growth and yield

The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estimation through different methods is necessary to develop long-term data sets for agricultural and environmental applications. The objective of this study was to analyze the impact of using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize, and peanut. Nine locations representing Georgia's major crop belt were selected. Daily weather data from the Georgia Automated Environmental Monitoring Network (AEMN), including solar radiation, maximum and minimum temperature, and precipitation, were duplicated. The OSR was removed from one set and then generated using a stochastic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4 were used to simulate crop growth and yield at each location with both OSR and GSR and for rainfed and irrigated conditions. The statistical analysis included summary statistics, Pearson's coefficient of correlation, mean squared deviation (MSD) and its components, namely: squared bias (SB), squared difference between standard deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), and regressions. Within locations, for the three crops under rainfed and irrigated conditions, GSR did not significantly affect simulated total evapotranspiration and aboveground biomass and yields. For the three crops, deviations of simulated water use and yields from GSR with respect to simulated water use and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the extent of the difference between standard deviations of GSR and OSRG could affect the outputs of the crop models. Nevertheless, for most locations none of the MSD components of the GSR showed significant correlation with simulated yields and the overall performance of the models was not affected. It can be concluded based on the results of this study that GSR can be used as an input for crop model simulation models when OSR is not available.     

AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems

AQUATOX combines aquatic ecosystem, chemical fate, and ecotoxicological constructs to obtain a truly integrative fate and effects model. It is a general, mechanistic ecological risk assessment model intended to be used to evaluate past, present, and future direct and indirect effects from various stressors including nutrients, organic wastes, sediments, toxic organic chemicals, flow, and temperature in aquatic ecosystems. The model has a very flexible structure and provides multiple analytical tools useful for evaluating ecological effects, including uncertainty analysis, nominal range sensitivity analysis, comparison of perturbed and control simulations, and graphing and tabulation of predicted concentrations, rates, and photosynthetic limitations. It can represent a full aquatic food web, including multiple genera and guilds of periphyton, phytoplankton, submersed aquatic vegetation, invertebrates, and fish and associated organic toxicants. It can model up to 20 organic chemicals simultaneously. (It does not model metals.) Modeled processes for organic toxicants include chemodynamics of neutral and ionized organic chemicals, bioaccumulation as a function of sorption and bioenergetics, biotransformation to daughter products, and sublethal and lethal toxicity. It has an extensive library of default biotic, chemical, and toxicological parameters and incorporates the ICE regression equations for estimating toxicity in numerous organisms. The model has been implemented for streams, small rivers, ponds, lakes, reservoirs, and estuaries. It is an integral part of the BASINS system with linkage to the watershed models HSPF and SWAT.     

Estimating soil carbon turnover using radiocarbon data: A case-study for European Russia

Turnover rates of soil carbon for 20 soil types typical for a 3.7 million km² area of European Russia were estimated based on ¹⁴C data. The rates are corrected for bomb radiocarbon which strongly affects the topsoil ¹⁴C balance. The approach is applied for carbon stored in the organic and mineral layers of the upper 1 m of the soil profile. The turnover rates of carbon in the upper 20 cm are relatively high for forest soils (0.16–0.78% year⁻¹), intermediate for tundra soils (0.25% year⁻¹), and low for grassland soils (0.02–0.08% year⁻¹) with the exception of southern Chernozems (0.32% year⁻¹). In the soil layer of 20–100 cm depth, the turnover rates were much lower for all soil types (0.01–0.06% year⁻¹) except for peat bog soils of the southern taiga (0.14% year⁻¹). Combined with a map of soil type distribution and a dataset of several hundred soil carbon profiles, the method provides annual fluxes for the slowest components of soil carbon assuming that the latter is in equilibrium with climate and vegetation cover. The estimated carbon flux from the soil is highest for forest soils (12–147 gC/(m² year)), intermediate for tundra soils (33 gC/(m² year)), and lowest for grassland soils (1–26 gC/(m² year)). The approach does not distinguish active and recalcitrant carbon fractions and this explains the low turnover rates in the top layer. Since changes in soil types will follow changes in climate and land cover, we suggest that pedogenesis is an important factor influencing the future dynamics of soil carbon fluxes. Up to now, both the effect of soil type changes and the clear evidence from ¹⁴C measurements that most soil organic carbon has a millennial time scale, are basically neglected in the global carbon cycle models used for projections of atmospheric CO₂ in 21st century and beyond.     

人因失误的机理及其可靠性研究

随着科技的发展 ,在系统安全中 ,机器设备可靠性越来越高 ,由于人本身的复杂性 ,人因失误变得愈来愈严重。本文基于人行为的原理 ,对人因失误的机理、影响人失误的因素、人行为的模型和失误模型及其可靠性的量化进行了分析 ,建立了计算人可靠性的威布尔分布模型 ,并对其参数进行了讨论 ,可用此来评价人的可靠性。     

A comparison of simulated and observed ozone mixing ratios for the summer of 1998 in Western Europe

This study describes and evaluates the newly developed European scale Eulerian chemistry transport model CHIMERE-continental. The model is designed for seasonal simulations and real time forecasts without the use of super-computers. For the purpose of model evaluation simulated ozone mixing ratios for the period between 1 May 1998 and 30 September 1998 are compared to observational data from 115 European surface sites. In order to facilitate the interpretation of future forecasts a statistic is established to estimate the reliability of a simulated pollution level. Besides this, the comparison is done by means of time series, scatter plots, a spectral analysis and the calculation of RMS-errors and biases of the model results corresponding to each observation site. It turns out that the mean RMS-error of the simulated daily maximum ozone mixing ratio for the sites considered a priori as well suited for a model comparison is about 10 ppb. For the same period but a reduced number of sites observed concentrations of NO₂ and ethene are compared to simulated values. Difficulties encountered with the representativeness of observations when trying to evaluate a mesoscale air pollution model are discussed.