Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage

The U.S. has been providing national-scale estimates of forest carbon (C) stocks and stock change to meet United Nations Framework Convention on Climate Change (UNFCCC) reporting requirements for years. Although these currently are provided as national estimates by pool and year to meet greenhouse gas monitoring requirements, there is growing need to disaggregate these estimates to finer scales to enable strategic forest management and monitoring activities focused on various ecosystem services such as C storage enhancement. Through application of a nearest-neighbor imputation approach, spatially extant estimates of forest C density were developed for the conterminous U.S. using the U.S.’s annual forest inventory. Results suggest that an existing forest inventory plot imputation approach can be readily modified to provide raster maps of C density across a range of pools (e.g., live tree to soil organic carbon) and spatial scales (e.g., sub-county to biome). Comparisons among imputed maps indicate strong regional differences across C pools. The C density of pools closely related to detrital input (e.g., dead wood) is often highest in forests suffering from recent mortality events such as those in the northern Rocky Mountains (e.g., beetle infestations). In contrast, live tree carbon density is often highest on the highest quality forest sites such as those found in the Pacific Northwest. Validation results suggest strong agreement between the estimates produced from the forest inventory plots and those from the imputed maps, particularly when the C pool is closely associated with the imputation model (e.g., aboveground live biomass and live tree basal area), with weaker agreement for detrital pools (e.g., standing dead trees). Forest inventory imputed plot maps provide an efficient and flexible approach to monitoring diverse C pools at national (e.g., UNFCCC) and regional scales (e.g., Reducing Emissions from Deforestation and Forest Degradation projects) while allowing timely incorporation of empirical data (e.g., annual forest inventory).     

Source subtraction for the extended Very Small Array and 33-GHz source count estimates

We describe the source subtraction strategy and observations for the extended Very Small Array (VSA), a cosmic microwave background interferometer operating at 33 GHz. A total of 453 sources were monitored at 33 GHz using a dedicated source subtraction baseline. 131 sources brighter than 20 mJy were directly subtracted from the VSA visibility data. Some characteristics of the subtracted sources, such as spectra and variability, are discussed. The 33-GHz source counts are estimated from a sample selected at 15 GHz. The selection of VSA fields in order to avoid bright sources introduces a bias into the observed counts. This bias is corrected and the resulting source count is estimated to be complete in the flux-density range 20–114 mJy. The 33-GHz source counts are used to calculate a correction to the VSA power spectrum for sources below the subtraction limit.     

Possible usage of carbamide tar for creating artificial grounds with high sorption capacity

Filtering a lead solution through model samples prepared on the basis of alluvial sand mixed with carbamide tar at the ratio of 1: 1 and 1: 2 resulted in a sorption capacity of 30 and 160 mg/g of sample, respectively. Direct and reverse potentiometric titration of carbamide tar with a 0.1 N Pb (NO₃)₂ solution showed that these results are due to the fact that the curing of carbamide tar in acidic conditions is accompanied by irreversible lead sorption, which is not extracted by conventional buffer solutions. Thus, lead atoms become tightly integrated in the polymer structure of the tar with N-Pb-N bonding.     

Evaluating the NOAA Coastal and Marine Ecological Classification Standard in estuarine systems: A Columbia River Estuary case study

A common first step in conservation planning and resource management is to identify and classify habitat types, and this has led to a proliferation of habitat classification systems. Ideally, classifications should be scientifically and conceptually rigorous, with broad applicability across spatial and temporal scales. Successful systems will also be flexible and adaptable, with a framework and supporting lexicon accessible to users from a variety of disciplines and locations. A new, continental-scale classification system for coastal and marine habitats—the Coastal and Marine Ecological Classification Standard (CMECS)—is currently being developed for North America by NatureServe and the National Oceanic and Atmospheric Administration (NOAA). CMECS is a nested, hierarchical framework that applies a uniform set of rules and terminology across multiple habitat scales using a combination of oceanographic (e.g. salinity, temperature), physiographic (e.g. depth, substratum), and biological (e.g. community type) criteria. Estuaries are arguably the most difficult marine environments to classify due to large spatio-temporal variability resulting in rapidly shifting benthic and water column conditions. We simultaneously collected data at eleven subtidal sites in the Columbia River Estuary (CRE) in fall 2004 to evaluate whether the estuarine component of CMECS could adequately classify habitats across several scales for representative sites within the estuary spanning a range of conditions. Using outputs from an acoustic Doppler current profiler (ADCP), CTD (conductivity, temperature, depth) sensor, and PONAR (benthic dredge) we concluded that the CMECS hierarchy provided a spatially explicit framework in which to integrate multiple parameters to define macro-habitats at the 100 m² to >1000 m² scales, or across several tiers of the CMECS system. The classification's strengths lie in its nested, hierarchical structure and in the development of a standardized, yet flexible classification lexicon. The application of the CMECS to other estuaries in North America should therefore identify similar habitat types at similar scales as we identified in the CRE. We also suggest that the CMECS could be improved by refining classification thresholds to better reflect ecological processes, by direct integration of temporal variability, and by more explicitly linking physical and biological processes with habitat patterns.     

Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simulation study

ABSTRACT

The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects.     

Atmospheric river orientation determines flood occurrence

Atmospheric Rivers (ARs) have been linked to many of the largest recorded UK winter floods. These large-scale features can be 500–800 km in width but produce markedly different flood responses in adjacent catchments. Here we combine meteorological and hydrological data to examine why two impermeable catchments on the west coast of Britain respond differently to landfalling ARs. This is important to help better understand flood generation associated with ARs and improve flood forecasting and climate-change impact assessment. Analysis of 32 years of a newly available ERA5 high-resolution atmospheric reanalysis and corresponding 15-min river flow data show that the most impactful ARs arise through a combination of the orientation and magnitude of their water vapour flux. At the Dyfi catchment, AR orientations of between 238–258° result in the strongest hydrological responses, whereas at the Teifi the range is 224–243°. We believe this differential flood response is the result of catchment orientation and topography enhancing or suppressing orographic rainfall totals, even in relatively low-relief coastal catchments. Further to the AR orientation, ARs must have an average water vapour flux of 400–450 kg m⁻¹ s⁻¹ across their lifetime. Understanding the preferential properties of impactful ARs at catchments allows for the linking of large-scale synoptic features, such as ARs, directly to winter flood impacts. These results using two test catchments suggest a novel approach to flood forecasts through the inclusion of AR activity.     

Response of reindeer and caribou to human activities

Petroleum and mineral exploration and extraction, hydroelectric development, atmospheric transport of contaminants, timber harvesting and tourism are increasing worldwide, especially in the Arctic. This development may adversely affect populations of reindeer/caribou ( Rangifer tarandus ) which are the basis of subsistence economies for northern indigenous peoples. Our purpose is to present a survey of the literature that has investigated the response of reindeer/caribou to human activities. Individuals and groups of reindeer/caribou: 1) move away from point sources of disturbance; 2) increase activity and energy expenditure near disturbance; 3) delay crossing or fail to cross linear structures; 4) shift away from areas of extensive and intensive development; and 5) are killed by collisions with vehicles and by hunting along roads. Cows and calves during the calving season are the most easily disturbed group. Bulls in general and all reindeer/caribou during insect harassment are least likely to avoid development areas. Estimation of the proportion of a population that is exposed to disturbance and the implications of this exposure to annual energy budgets, survival and productivity of reindeer/caribou has received little attention. Future advances in understanding the implications of human disturbance to reindeer/caribou will require cumulative effects assessment at annual, population and regional scales. Although some level of cumulative effect is likely, clear separation of cumulative effects of development from natural variation in caribou habitat use and demography will be difficult.     

A Landscape Ecology Approach to Assessing Development Impacts in the Tropics: A Geothermal Energy Example in Hawaii

Geographic information systems (GIS) are increasingly being used in environmental impact assessments (EIA) because GIS is useful for analysing spatial impacts of various development scenarios. Spatially representing these impacts provides another tool for landscape ecology in environmental and geographical investigations by facilitating analysis of the effects of landscape pattern on ecological processes and examining change over time. Landscape ecological principles are applied in this study to a hypothetical geothermal development project on the Island of Hawaii. Some common landscape pattern metrics were used to analyse dispersed versus condensed development scenarios and their effect on landscape pattern. Indices of fragmentation and patch shape did not appreciably change with additional development. The amount of forest to open edge, however, greatly increased with the dispersed development scenario. In addition, landscape metrics showed that a human disturbance had a greater simplifying effect on patch shape and also increased fragmentation than a natural disturbance. The use of these landscape pattern metrics can advance the methodology of applying GIS to EIA.