DRY AND WET WEATHER FLOW NUTRIENT LOADS FROM A LOS ANGELES WATERSHED1

Effective watershed management requires an accurate assessment of the pollutant loads from the associated point and nonpoint sources. The importance of wet weather flow (WWF) pollutant loads is well known, but in semi‐arid regions where urbanization is significant the pollutant load in dry weather flow (DWF) may also be important. This research compares the relative contributions of potential contaminants discharged in DWF and WWF from the Ballona Creek Watershed in Los Angeles, California. Models to predict DWF and WWF loads of total suspended solids, biochemical oxygen demand, nitrate‐nitrogen, nitrite‐nitrogen, ammonia‐nitrogen, total Kjeldahl nitrogen, and total phosphorus from the Ballona Creek Watershed for six water years dating from 1991 to 1996 were developed. The contaminants studied were selected based on data availability and their potential importance in the degradation of Ballona Creek and Santa Monica Bay beneficial uses. Wet weather flow was found to contribute approximately 75 percent to 90 percent of the total annual flow volume discharged by the Ballona Creek Watershed. Pollutant loads are also predominantly due to WWF, but during the dry season, DWF is a more significant contributor. Wet weather flow accounts for 67 to 98 percent of the annual load of the constituents studied. During the dry season, however, the portion attributable to DWF increases to greater than 40 percent for all constituents except biochemical oxygen demand and total suspended solids. When individual catchments within the watershed are considered, the DWF pollutant load from the largest catchment is similar to the WWF pollutant load in two other major catchments. This research indicates WWF is the most significant source of nonpoint source pollution load on an annual basis, but management of the effects of the nonpoint source pollutant load should consider the seasonal importance of DWF.     

MODELING LIGHT ATTENUATION,SECCHI DISK,AND EFFECTS OF TRIPTON IN SENACA RIVER,NEW YORK,USA1

The development, testing, and application of a probabilistic model framework for the light attenuation coefficient for downwelling irradiance (K_d) and Secchi disc transparency (SD) that resolves the effects of several light attenuating constituents, including phytoplankton and nonliving particles (tripton), is documented. The model is consistent with optical theory, partitioning the magnitudes of the light attenuating processes of absorption and scattering according to the contributions of attenuating constituents as simple summations. The probabilistic framework accommodates variations in the character and concentrations of these constituents and ambient conditions during measurements, and recognizes a linear relationship between the magnitudes of absorption and scattering by tripton. The model is tested and applied for a 21 km reach of the Seneca River, New York, that features optical gradients caused by an intervening hypereutrophic lake and dam, and a severe infestation of the exotic zebra mussel. The model is applied to resolve the roles of phytoplankton and tripton in regulating measured longitudinal patterns of SD along the study reach of the river and increases in SD since the zebra mussel invasion, and to predict decreases in K_d since the invasion.     

MODELING THE DISTRIBUTION OF DIFFUSE NITROGEN SOURCES AND SINKS IN THE NEUSE RIVER BASIN OF NORTH CAROLINA,USA1

This study quantified nonpoint source nitrogen (NPS‐N) sources and sinks across the 14,582 km² Neuse River Basin (NRB) located in North Carolina, to provide tabular data summaries and graphic overlay products to support the development of management approaches to best achieve established N reduction goals. First, a remote sensor derived, land cover classification was performed to support modeling needs. Modeling efforts included the development of a mass balance model to quantify potential N sources and sinks, followed by a precipitation event driven hydrologic model to effectively transport excess N across the landscape to individual stream reaches to support subsequent labeling of transported N values corresponding to source origin. Results indicated that agricultural land contributed 55 percent of the total annual NPS‐N loadings, followed by forested land at 23 percent (background), and urban areas at 21 percent. Average annual N source contributions were quantified for agricultural (1.4 kg/ha), urban (1.2 kg/ha), and forested cover types (0.5 kg/ha). Nonpoint source‐N contributions were greatest during the winter (40 percent), followed by spring (32 percent), summer (28 percent), and fall (0.3 percent). Seasonal total N loadings shifted from urban dominated and forest dominated sources during the winter, to agricultural sources in the spring and summer. A quantitative assessment of the significant NRB land use activities indicated that high (greater than 70 percent impervious) and medium (greater than 35 percent impervious) density urban development were the greatest contributors of NPS‐N on a unit area basis (1.9 and 1.6 kg/ha/yr, respectively), followed by row crops and pasture/hay cover types (1.4 kg/ha/yr).     

A comparative study of US EPA 1996 and 1999 emission inventories in the west Gulf of Mexico coast region, USA

Emission inventory is one of the required inputs to air quality models. To assist in the urban and regional modeling efforts, United States Environmental Protection Agency (EPA) has compiled a National Emission Inventory (NEI) for criterion pollutants, and the precursors of ozone and particulate matter (PM). In December 2002, EPA released the 1999 NEI estimates (NEI99), which represent the most recent national emission data. However, the data sets are not in model-ready format for air quality simulations. This present work converts the NEI99 Final Version 2 data sets into Inventory Data Analyzer (IDA) format and processes the data using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system to generate a gridded emission inventory in a domain covering the west Gulf Coast Region, USA. The spatial and diurnal emission characteristics of the gridded emission inventories are then assessed and compared with those of the National Emission Trend 1996 (NET96). The NEI99 database contains more complete emission records in both area and point sources. It is also found that NEI99 data exhibit greater emissions with respect to point and mobile sources but smaller emissions with respect to area sources when compared to the corresponding gridded NET96 data in the same study domain. The most distinct differences between the NEI99 and NET96 databases are CO emission of mobile sources, SO2 emissions of point sources, and VOC/PM/NH3/NOx emissions of area and non-road sources. The gridded NEI99 data show low VOC/NOx ratios (<2-5) in the urban areas of the study domain.     

Hot,Salty Water: A Confluence of Issues in Managing Stormwater Runoff for Urban Streams

Research increasingly highlights cause and effect relationships between urbanization and stream conditions are complex and highly variable across physical and biological regions. Research also demonstrates stormwater runoff is a key causal agent in altering stream conditions in urban settings. More specifically, thermal pollution and high salt levels are two consequences of urbanization and subsequent runoff. This study describes a demonstration model populated with data from a high gradient headwaters stream. The model was designed to explain surface water‐groundwater dynamics related to salinity and thermal pollution. Modeled scenarios show long‐term additive impacts from salt application and suggest reducing flow rates, as stormwater management practices are typically designed to do, have the potential to greatly reduce salt concentrations and simultaneously reduce thermal pollution. This demonstration model offers planners and managers reason to be confident that stormwater management efforts can have positive impacts.     

Using long-term data for a reintroduced population to empirically estimate future consequences of inbreeding

Inbreeding depression is an important long-term threat to reintroduced populations. However, the strength of inbreeding depression is difficult to estimate in wild populations because pedigree data are inevitably incomplete and because good data are needed on survival and reproduction. Predicting future population consequences is especially difficult because this also requires projecting future inbreeding levels and their impacts on long-term population dynamics, which are subject to many uncertainties. We illustrate how such projections can be derived through Bayesian state-space modeling methods based on a 26-year data set for North Island Robins (Petroica longipes) reintroduced to Tiritiri Matangi Island in 1992. We used pedigree data to model increases in the average inbreeding level (F ) over time based on kinship of possible breeding pairs and to estimate empirically N_e/N (effective/census population size). We used multiple imputation to model the unknown components of inbreeding coefficients, which allowed us to estimate effects of inbreeding on survival for all 1458 birds in the data set while modeling density dependence and environmental stochasticity. This modeling indicated that inbreeding reduced juvenile survival (1.83 lethal equivalents [SE 0.81]) and may have reduced subsequent adult survival (0.44 lethal equivalents [0.81]) but had no apparent effect on numbers of fledglings produced. Average inbreeding level increased to 0.10 (SE 0.001) as the population grew from 33 (0.3) to 160 (6) individuals over the 25 years, giving a ratio of 0.56 (0.01). Based on a model that also incorporated habitat regeneration, the population was projected to reach a maximum of 331–1144 birds (median 726) in 2130, then to begin a slow decline. Without inbreeding, the population would be expected stabilize at 887–1465 birds (median 1131). Such analysis, therefore, makes it possible to empirically derive the information needed for rational decisions about inbreeding management while accounting for multiple sources of uncertainty.     

北斗导航终端设备可靠性加速试验与快速评价技术研究

针对北斗导航终端设备开展传统可靠性试验时间长、成本高,难以实现在短期内完成高可靠性指标考核的现状,借鉴加速试验建模技术和相关标准的加速模型参数,进行北斗导航整机可靠性加速建模,设计加速试验剖面和方案,预先评估出北斗导航终端设备的加速因子,通过一组样品即可完成北斗导航的加速试验,实现对北斗导航终端设备高可靠性指标的快速评估。     

Defining and Evaluating the Umbrella Species Concept for Conserving and Restoring Landscape Connectivity

Conserving or restoring landscape connectivity between patches of breeding habitat is a common strategy to protect threatened species from habitat fragmentation. By managing connectivity for some species, usually charismatic vertebrates, it is often assumed that these species will serve as conservation umbrellas for other species. We tested this assumption by developing a quantitative method to measure overlap in dispersal habitat of 3 threatened species—a bird (the umbrella), a butterfly, and a frog—inhabiting the same fragmented landscape. Dispersal habitat was determined with Circuitscape, which was parameterized with movement data collected for each species. Despite differences in natural history and breeding habitat, we found substantial overlap in the spatial distributions of areas important for dispersal of this suite of taxa. However, the intuitive umbrella species (the bird) did not have the highest overlap with other species in terms of the areas that supported connectivity. Nevertheless, we contend that when there are no irreconcilable differences between the dispersal habitats of species that cohabitate on the landscape, managing for umbrella species can help conserve or restore connectivity simultaneously for multiple threatened species with different habitat requirements. Definición y Evaluación del Concepto de Especie Paraguas para Conservar y Restaurar la Conectividad de Paisajes     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

Constraining SWAT Calibration with Remotely Sensed Evapotranspiration Data

Historically, many watershed studies have been based on using the streamflow flux, typically from a single gauge at the basin's outlet, to support calibration. In this setting, there is great potential for equifinality of parameters during the optimization process, especially for parameters that are not directly related to streamflow. Therefore, some of the optimal parameter values achieved during the autocalibration process may be physically unrealistic. In recent decades a vast array of data from land surface models and remote sensing platforms can help to constrain hydrologic fluxes such as evapotranspiration (ET). While the spatial resolution of these ancillary datasets varies, the continuous spatial coverage of these gridded datasets provides flux measurements across the entire basin, in stark contrast to point‐based streamflow data. This study uses Global Land Evaporation: the Amsterdam Model data to constrain Soil and Water Assessment Tool parameter values associated with ET to a more physically realistic range. The study area is the Little Washita River Experimental Watershed, in southern Oklahoma. Traditional objective metrics such as the Nash‐Sutcliffe coefficients record no performance improvement after application of this method. However, there is a dramatic increase in the number of days with receding flow where simulations match observed streamflow.