John E. Hathaway

Investigating the nexus of climate, energy, water, and land at decision-relevant scales: the Platform for Regional Integrated Modeling and Analysis (PRIMA)

The Platform for Regional Integrated Modeling and Analysis (PRIMA) is an innovative modeling system developed at Pacific Northwest National Laboratory (PNNL) to simulate interactions among natural and human systems at scales relevant to regional decision making. PRIMA brings together state-of-the-art models of regional climate, hydrology, agriculture and land use, socioeconomics, and energy systems using a flexible coupling approach. Stakeholder decision support needs underpin the application of the platform to regional issues, and an uncertainty characterization process is used to identify robust decisions. The platform can be customized to inform a variety of complex questions, such as how a policy in one sector might affect the ability to meet climate mitigation targets or adaptation goals in another sector. Current numerical experiments focus on the eastern United States, but the framework is designed to be regionally flexible. This paper provides a high-level overview of PRIMA’s functional capabilities and describes some key challenges and opportunities associated with integrated regional modeling.

21st century United States emissions mitigation could increase water stress more than the climate change it is mitigating

Significance Devising sustainable climate change mitigation policies with attention to potential synergies and constraints within the climate–energy–water nexus is the subject of ongoing integrated modeling efforts. This study employs a regional integrated assessment model and a regional Earth system model at high spatial and temporal resolutions in the Unites States to compare the implications of two of the representative concentration pathways under consistent socioeconomics. The results clearly show, for the first time to our knowledge, that climate change mitigation policies, if not designed with careful attention to water resources, could increase the magnitude, spatial coverage, and frequency of water deficits. The results challenge the general perception that mitigation that aims at reducing warming also would alleviate water deficits in the future. There is evidence that warming leads to greater evapotranspiration and surface drying, thus contributing to increasing intensity and duration of drought and implying that mitigation would reduce water stresses. However, understanding the overall impact of climate change mitigation on water resources requires accounting for the second part of the equation, i.e., the impact of mitigation-induced changes in water demands from human activities. By using integrated, high-resolution models of human and natural system processes to understand potential synergies and/or constraints within the climate–energy–water nexus, we show that in the United States, over the course of the 21st century and under one set of consistent socioeconomics, the reductions in water stress from slower rates of climate change resulting from emission mitigation are overwhelmed by the increased water stress from the emissions mitigation itself. The finding that the human dimension outpaces the benefits from mitigating climate change is contradictory to the general perception that climate change mitigation improves water conditions. This research shows the potential for unintended and negative consequences of climate change mitigation.

Determining the optimum number of increments in composite sampling

Composite sampling can be more cost effective than simple random sampling. This paper considers how to determine the optimum number of increments to use in composite sampling. Composite sampling terminology and theory are outlined and a method is developed which accounts for different sources of variation in compositing and data analysis. This method is used to define and understand the process of determining the optimum number of increments that should be used in forming a composite. The blending variance is shown to have a smaller range of possible values than previously reported when estimating the number of increments in a composite sample. Accounting for differing levels of the blending variance significantly affects the estimated number of increments.

Ultrasonic phased array sound field mapping through large-bore coarse grained cast austenitic stainless steel (CASS) piping materials

A sound field beam mapping exercise was conducted to further understand the effects of coarse-grained microstructures found in cast austenitic stainless steel (CASS) materials on phased array ultrasonic wave propagation. Laboratory measurements were made on three CASS specimens with different microstructures; the specimens were polished and etched to reveal measurable grain sizes, shapes, and orientations. Three longitudinal, phased array probes were fixed on a specimens outside diameter with the sound field directed toward one end (face) of the pipe segment over a fixed range of angles. A point receiver was raster scanned over the surface of the specimen face generating a sound field image. A slice of CASS material was then removed from the specimen end and the beam mapping exercise repeated. The sound fields acquired were analyzed for spot size, coherency, and beam redirection. Qualitative analyses were conducted between the resulting sound fields and the microstructural characteristics of each specimen.

Explosive particle soil surface dispersion model for detonated military munitions

The accumulation of high explosive mass residue from the detonation of military munitions on training ranges is of environmental concern because of its potential to contaminate the soil, surface water, and groundwater. The US Department of Defense wants to quantify, understand, and remediate high explosive mass residue loadings that might be observed on active firing ranges. Previously, efforts using various sampling methods and techniques have resulted in limited success, due in part to the complicated dispersion pattern of the explosive particle residues upon detonation. In our efforts to simulate particle dispersal for high- and low-order explosions on hypothetical firing ranges, we use experimental particle data from detonations of munitions from a 155-mm howitzer, which are common military munitions. The mass loadings resulting from these simulations provide a previously unattained level of detail to quantify the explosive residue source-term for use in soil and water transport models. In addition, the resulting particle placements can be used to test, validate, and optimize particle sampling methods and statistical models as applied to firing ranges. Although the presented results are for a hypothetical 155-mm howitzer firing range, the method can be used for other munition types once the explosive particle characteristics are known.

Cross-combined composite sampling designs for identification of elevated regions

Analyzing soils for contaminants can be costly. Generally, discrete samples are gathered from within a study area, analyzed by a laboratory and the results are used in a site-specific statistical analysis. Because of the heterogeneities that exist in soil samples within study areas, a large amount of variability and skewness may be present in the sample population. This necessitates collecting a large number of samples to obtain reliable inference on the mean contaminant concentration and to understand the spatial patterns for future remediation. Composite, or Incremental, sampling is a commonly applied method for gathering multiple discrete samples and physically combining them, such that each combination of discrete samples requires a single laboratory analysis, which reduces cost and can improve the estimates of the mean concentration. While incremental sampling can reduce cost and improve mean estimates, current implementations do not readily facilitate the characterization of spatial patterns or the detection of elevated constituent regions within study areas. The methods we present in this work provide efficient estimation and inference for the mean contaminant concentration over the entire spatial area and enable the identification of high contaminant regions within the area of interest. We develop sample design methodologies that explicitly define the characteristics of these designs (such as sample grid layout) and quantify the number of incremental samples that must be obtained under a design criteria to control false positive and false negative (Type I and II) decision errors. We present the sample design theory and specifications as well as results on simulated and real data.

Ultrasonic Examination of Double-Shell Tank 241-AW-106 Examination Completed July 2009.

AREVA Federal Services LLC (AFS), under a contract from Washington River Protection Solutions (WRPS), has performed an ultrasonic examination of selected portions of Double-Shell Tank 241-AW-106. PNNL is responsible for preparing a report(s) that describes the results of the AFS ultrasonic examinations.