Incorporating stakeholder decision support needs into an integrated regional Earth system model

A new modeling effort exploring the opportunities, constraints, and interactions between mitigation and adaptation at regional scale is utilizing stakeholder engagement in an innovative approach to guide model development and demonstration, including uncertainty characterization, to effectively inform regional decision making. This project, the integrated Regional Earth System Model (iRESM), employs structured stakeholder interactions and literature reviews to identify the most relevant adaptation and mitigation alternatives and decision criteria for each regional application of the framework. The information is used to identify important model capabilities and to provide a focus for numerical experiments. This paper presents the stakeholder research results from the first iRESM pilot region. The pilot region includes the Great Lakes Basin in the Midwest portion of the United States as well as other contiguous states. This geographic area (14 states in total) permits cohesive modeling of hydrologic systems while also providing strong gradients in climate, demography, land cover/land use, and energy supply and demand. The results from the stakeholder research indicate that, for this region, iRESM should prioritize addressing adaptation alternatives in the water resources, urban infrastructure, and agriculture sectors, including water conservation, expanded water quality monitoring, altered reservoir releases, lowered water intakes, urban infrastructure upgrades, increased electric power reserves in urban areas, and land use management/crop selection changes. For mitigation in this region, the stakeholder research implies that iRESM should focus on policies affecting the penetration of renewable energy technologies, and the costs and effectiveness of energy efficiency, bioenergy production, wind energy, and carbon capture and sequestration.     

Pastoral farmland habitat diversity in south-east Ireland

In this study, habitat surveys were undertaken on 50 grass-based farms in SE Ireland and data digitised onto aerial photography. Additional data i.e. stocking rates, and participation (or otherwise) in the Irish Rural Environment Protection Scheme (REPS) were collected and analysed as possible explanatory variables for farm habitat composition.Results indicated that approximately 14.3% of the land area of sampled farms comprised of semi-natural habitat types, a proportion substantially greater than has been reported for many other European countries. The most frequently recorded semi-natural habitats included, field boundaries, scrub, and deciduous and riparian woodlands.Multivariate analysis of farm habitat configuration showed a strong dichotomy between dairy and non-dairy farming systems. Habitats such as intensively managed grassland and built ground were closely associated with dairy-based enterprises. In contrast, the incidence of other habitat types was associated with non-dairy and/or REPS participating enterprises. The ecological quality of field boundaries as assessed by the Field Boundary Evaluation and Grading System (FBEGS) Index was significantly greater on dairy, compared with dry-stock farms.This dichotomy in farm habitat composition is not reflected within current Agri-Environment (AE) policy. Integration of locally important drivers of habitat diversity into the design and implementation of AE policy, is integral to the successful performance of AE schemes.     

Muskox status,recent variation,and uncertain future

Ambio - Muskoxen (Ovibos moschatus) are an integral component of Arctic biodiversity. Given low genetic diversity, their ability to respond to future and rapid Arctic change is unknown, although...     

Characterization of Benthic Communities and Physical Habitat in the Stanislaus, Tuolumne, and Merced Rivers, California

The primary goal of this study was to characterize physical habitat and benthic communities (macroinvertebrates) in the Stanislaus, Tuolumne and Merced Rivers in California’s San Joaquin Valley in 2003. These rivers have been listed as impaired water bodies (303 (d) list) by the State of California due to the presence of organophosphate (OP) insecticides chlorpyrifos and diazinon, Group A pesticides (i.e., organochlorine pesticides), mercury, or unknown toxicity. Based on 10 instream and riparian physical habitat metrics, total physical habitat scores in the Stanislaus River ranged from 124 to 188 (maximum possible total score is 200). The highest total habitat score was reported at the upstream site. Tuolumne River physical habitat scores ranged from 86 to 167. Various Tuolumne River physical habitat metrics, including total habitat score, increased from downstream to upstream in this river. Merced River physical habitat scores ranged from 121 to 170 with a significant increase in various physical habitat metrics, including total habitat score, reported from downstream to upstream. Channel flow (an instream metric) and bank stability (a riparian metric) were the most important physical habitat metrics influencing the various benthic metrics for all three rivers. Abundance measures of benthic macroinvertebrates (5,100 to 5,400 individuals) were similar among the three rivers in the San Joaquin watershed. Benthic communities in all three rivers were generally dominated by: (1) Baetidae species (mayflies) which are a component of EPT taxa generally considered sensitive to environmental degradation; (2) Chironomidae (midges) which can be either tolerant or sensitive to environmental stressors depending on the species; (3) Ephemerellidae (mayflies) which are considered sensitive to pollution stress; and (4) Naididae (aquatic worms) which are generally considered tolerant to environmental stressors. The presence of 117 taxa in the Stanislaus River, 114 taxa in the Tuolumne River and 96 taxa in the Merced River implies that the benthic communities in these streams are fairly diverse but without a clear definition of benthic community expectations it is unknown if these water bodies are actually impaired.     

Development of an extraction method for perchlorate in soils

Perchlorate originates as a contaminant in the environment from its use in solid rocket fuels and munitions. The current US EPA methods for perchlorate determination via ion chromatography using conductivity detection do not include recommendations for the extraction of perchlorate from soil. This study evaluated and identified appropriate conditions for the extraction of perchlorate from clay loam, loamy sand, and sandy soils. Based on the results of this evaluation, soils should be extracted in a dry, ground (mortar and pestle) state with Milli-Q water in a 1 ratio 1 soil ratio water ratio and diluted no more than 5-fold before analysis. When sandy soils were extracted in this manner, the calculated method detection limit was 3.5 microg kg(-1). The findings of this study have aided in the establishment of a standardized extraction method for perchlorate in soil.     

Hyperspectral Characteristics of Canopy Components and Structure for Phenological Assessment of an Invasive Weed

Spectral reflectance values of four canopy components (stems, buds, opening flowers, and postflowers of yellow starthistle (Centaurea solstitialis)) were measured to describe their spectral characteristics. We then physically combined these canopy components to simulate the flowering stage indicated by accumulated flower ratios (AFR) 10%, 40%, 70%, and 90%, respectively. Spectral dissimilarity and spectral angles were calculated to quantitatively identify spectral differences among canopy components and characteristic patterns of these flowering stages. This study demonstrated the ability of hyperspectral data to characterize canopy components, and identify different flowering stages. Stems had a typical spectral profile of green vegetation, which produced a spectral dissimilarity with three reproduction organs (buds, opening flowers, and postflowers). Quantitative differences between simulated flower stages depended on spectral regions and phenological stages examined. Using full-range canopy spectra, the initial flowering stage could be separated from the early peak, peak, and late flowering stages by three spectral regions, i.e. the blue absorption (around 480 nm) and red absorption (around 650 nm) regions and NIR plateau from 730 nm to 950 nm. For airborne CASI data, only the red absorption region and NIR plateau could be used to identify the flowering stages in the field. This study also revealed that the peak flowering stage was more easily recognized than any of the other three stages.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

Risk assessment of trihalomethanes from tap water in Fortaleza, Brazil

The cancer risks (CR) by oral ingestion, dermal absorption, and inhalation exposure of trihalomethanes (THM) from tap water of ten districts in Fortaleza, Brazil were estimated. The mean levels of THM compounds were obtained in Fortaleza tap water as follow: 63.9 microg L(-1) for chloroform (CHCl(3)), 40.0 microg L(-1) for bromodichloromethane (CHBrCl(2)), and 15.6 microg L(-1) for dibromochloromethane (CHBr(2)Cl). Bromoform (CHBr(3)) was not detected. The mean CR for THMs in tap water is 3.96 x 10(-4). The results indicate that Fortaleza residents have a higher CR by inhalation than dermal absorption and oral ingestion. The CR for CHCl(3) contributes with 68% as compared with the total CR, followed by CHBrCl(2) (21%), and CHBr(2)Cl (11%). The hazard index (HI) is about ten times lower than unity, not indicating non-cancer effects.     

Evaluation of geochemical associations as a screening tool for identifying anthropogenic trace metal contamination

Geochemical association plots are used as a screening tool for environmental site assessments and use empirical log–log relationships between total trace metal concentrations and concentrations of a major (i.e., reference) soil metal constituent, such as iron (Fe), to discern sites with naturally elevated trace metal levels from sites with anthropogenic contamination. Log–log relationships have been consistently observed between trace metal and reference metal concentrations and are often considered constant. Consequently, we used a regional geochemistry data set to evaluate background trace metal/Fe log–log associations across soils with highly diverse composition. Our results indicate that, although geochemical associations may be proportional, they significantly differ across predominant United States Department of Agriculture (USDA) soil orders. This suggests that highly complex interactions between soil-forming factors and variable secondary clay mineral composition affect the ratio of trace metals to Fe concentrations in soils. Also, intra-order variability in trace metal/Fe ratios generally ranged multiple orders of magnitude which suggest that the order level of the USDA soil taxonomic system is insufficient to reasonably classify background trace metal concentrations. Consequently, geochemical association plots are a useful screening tool for environmental site assessments, but ubiquitous application of generic background metal data sets could result in erroneous conclusions. Because significantly different ratios were observed across predominant USDA soil orders, an agglomerative clustering technique was used to elucidate hierarchical patterns of association. We present these results as a mechanism to aid environmental assessors in screening candidate background metal data sets for their applicability to site-specific soil composition; although site-specific background metal data should be utilized if ample pristine reference sites with similar (i.e., sub-order) soil composition can be identified and sampled.