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.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Focus on mission success: Process safety for the Atychiphobist

Modern process plants are complex engineering systems. While thorough reviews of system safeguards are performed, catastrophic events continue to occur, often unfolding in unforeseen ways. Success in process safety demands safe processes, and understanding rare, high consequence events is central to the traditional process safety approach. This philosophy is common to all high-hazard industries, offering the potential for sharing approaches, experience, and lessons learned. The problem, however, is that people (and organizations and entire industries) who fear failure (atychiphobia) sometimes obsess about failure so much that they miss opportunities to succeed.This paper examines selected risk management practices in the power generation and aerospace industries and how those practices have led to improved performance. Risk informed decision making (RIDM) has had widespread application in the nuclear and aerospace industries, and is undergoing enhancements to become a key framework for risk management. Additionally, rather than focusing on avoidance of loss, there are emerging approaches supporting achievement of success. This approach provides a more direct link of risk to business and operational objectives, but does challenge conventional risk approaches founded in a loss prevention-centric view. The paper reflects upon risk informed decision making and success modeling, and suggests how these methods may be applied in the field of process safety. Specific examples are drawn from the defense in depth approach from the nuclear power industry and mission success concepts developed for NASA.     

Effect of variations in the redox potential of Gleysol on barium mobility and absorption in rice plants

Two assays were designed to obtain information about the influence of redox potential variations on barium mobility and bioavailability in soil. One assay was undertaken in leaching columns, and the other was conducted in pots cultivated with rice (Oryza sativa) using soil samples collected from the surface of Gleysol in both assays. Three doses of barium (100,300 mg kg⁻¹ and 3000 mg kg⁻¹-soil dry weight) and two redox potential values (oxidizing and reducing) were evaluated. During the incubation period, the redox potential (Eh) was monitored in columns and pots until values of −250 mV were reached. After the incubation period, geochemical partitioning was conducted on the barium using the European Communities Bureau of Reference (BCR) method. Rainfall of 200 mm d⁻¹ was simulated in the columns and in the planting of rice seedlings in the pots. The results of the geochemical partitioning demonstrated that the condition of reduction favors increased barium concentrations in the more labile chemical forms and decreased levels in the chemical forms related to oxides. The highest barium concentrations in leached extracts (3.36 mg L⁻¹) were observed at the highest dose and condition of reduction at approximately five times above the drinking water standard. The high concentrations of barium in the soil did not affect plant dry matter production. The highest levels and accumulation of barium in roots, leaves, and grains of rice were found at the highest dose and condition of reduction. These results demonstrate that reduction leads to solubilization of barium sulfate, thereby favoring greater mobility and bioavailability of this element.     

Volunteer Macroinvertebrate Monitoring: Tensions Among Group Goals,Data Quality,and Outcomes

Volunteer monitoring of natural resources is promoted for its ability to increase public awareness, to provide valuable knowledge, and to encourage policy change that promotes ecosystem health. We used the case of volunteer macroinvertebrate monitoring (VMM) in streams to investigate whether the quality of data collected is correlated with data use and organizers' perception of whether they have achieved these outcomes. We examined the relation between site and group characteristics, data quality, data use, and perceived outcomes (education, social capital, and policy change). We found that group size and the degree to which citizen groups perform tasks on their own (rather than aided by professionals) positively correlated with the quality of data collected. Group size and number of years monitoring positively influenced whether a group used their data. While one might expect that groups committed to collecting good-quality data would be more likely to use it, there was no relation between data quality and data use, and no relation between data quality and perceived outcomes. More data use was, however, correlated with a group's feeling of connection to a network of engaged citizens and professionals. While VMM may hold promise for bringing citizens and scientists together to work on joint conservation agendas, our data illustrate that data quality does not correlate with a volunteer group's desire to use their data to promote regulatory change. Therefore, we encourage scientists and citizens alike to recognize this potential disconnect and strive to be explicit about the role of data in conservation efforts.     

The New Bedford Harbor Superfund site long-term monitoring program (1993–2009)

New Bedford Harbor (NBH), located in southeastern Massachusetts, was designated as a marine Superfund site in 1983 due to sediment contamination by polychlorinated biphenyls (PCBs). Based on risks to human health and the environment, the first two phases of the site cleanup involved dredging PCB-contaminated sediments from the harbor. Therefore, a long-term monitoring program (LTM) was developed to measure spatial and temporal chemical and biological changes in sediment, water, and biota to assess the effects and effectiveness of the remedial activities. A systematic, probabilistic sampling design was used to select sediment sampling stations. This unbiased design allowed the three segments of the harbor to be compared spatially and temporally to quantify changes resulting from dredging the contaminated sediments. Sediment was collected at each station, and chemical (e.g., PCBs and metals), physical (e.g., grain size), and biological (e.g., benthic community) measurements were conducted on all samples. This paper describes the overall NBH-LTM approach and the results from the five rounds of sample collections. There is a decreasing spatial gradient in sediment PCB concentrations from the northern boundary (upper harbor) to the southern boundary (outer harbor) of the site. Along this same transect, there is an increase in biological condition (e.g., benthic community diversity). Temporally, the contaminant and biological gradients have been maintained since the 1993 baseline collection; however, since the onset of full-scale remediation, PCB concentrations have decreased throughout the site, and one of the benthic community indices has shown significant improvement in the lower and outer harbor areas.     

Large and mesoscale meteo-oceanographic patterns in local responses of biogeochemical concentrations

Investigations surrounding the variability of productivity in upwelling regions are necessary for a better understanding the physical-biological coupling in these regions by monitoring systems of environmental impacts according to the needs of the regional coastal management. Using a spatial and temporal database from National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric (NCAR) Research reanalysis, Quick Scatterometer vector wind, and surface stations from the Southeast coast of Brazil, we investigate the meteorological influences due to the large-scale systems in the variability of the nutrient and larvae concentration, and chlorophyll a, describing statistically relationships between them in upwelling regions. In addition, we used multivariate analysis, such as PCA and clustering to verify spatial and temporal variances and describe more clear the structure and composition of the ecosystem. Correlation matrix analyses were applied for different water masses present in the study area to identify the relations between physical and biogeochemical parameters in a region, where frequently upwelling occur. Statistical approaches and seasonal variability show that the period of November to March is more sensitive to nutrients (1.20?mg/m(3) for chlorophyll a, 2.20?μmol/l for total nitrogen and 5.5?ml/l for DO) and larvae concentrations (120 org/m(3) for most of the larvae, except for cirripedia that presented values around 370 org/m(3)) relating to the influence of large and mesoescale meteorological patterns. The spatial and temporal variables analyzed with multivariate approach show meaningful seasonality variance of the physical and biological samples, characterizing the principal components responsible for this variance in spring and summer (upwelling period), emphasizing the monitoring of species as crustaceans and mussels that are present in the local economy. Then, the spring and summer season are characterized by high productivity due to the occurrence of upwelling in this period.     

Soil criteria to protect terrestrial wildlife and open-range livestock from metal toxicity at mining sites

Thousands of hard rock mines exist in the western USA and in other parts of the world as a result of historic and current gold, silver, lead, and mercury mining. Many of these sites in the USA are on public lands. Typical mine waste associated with these sites are tailings and waste rock dumps that may be used by wildlife and open-range livestock. This report provides wildlife screening criteria levels for metals in soil and mine waste to evaluate risk and to determine the need for site-specific risk assessment, remediation, or a change in management practices. The screening levels are calculated from toxicity reference values based on maximum tolerable levels of metals in feed, on soil and plant ingestion rates, and on soil to plant uptake factors for a variety of receptors. The metals chosen for this report are common toxic metals found at mining sites: arsenic, cadmium, copper, lead, mercury, and zinc. The resulting soil screening values are well above those developed by the US Environmental Protection Agency. The difference in values was mainly a result of using toxicity reference values that were more specific to the receptors addressed rather than the most sensitive receptor.     

Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km² study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.