Toward Probabilistic Prediction of Flash Flood Human Impacts.

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance-specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle-related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time- and space-dependent human vulnerability and risk assessment for short-fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact-based predictive models for flash flood casualties using machine-learning approaches in the future.

Evaluating stream sediment chemistry within an agricultural catchment of Lebanon, Northeastern USA.

Recent arsenic pollution of drinking-water wells across Lebanon, northeastern USA has led to a growing concern about possible impact of agricultural activities on the hydrologic system. This study assessed the concentrations and distributions of arsenic and ten other elements (Al, Cd, Cr, Cu, Fe, K, Mn, P, Pb, and Zn) in stream sediments. The overall goal is to determine the extent of these elements within the fluvial systems, as well as overall sediment quality. A total of 65 stream sediments samples were collected, and analyzed for particle size distributions, organic matter contents, trace, and major elements concentrations. Results showed spatial variability in the concentrations of trace elements due to variation in sediments grain sizes, organic matter content, as well as land use activities within the study area. Calculation of sediment enrichment with respect to As, Cd, Cr, Cu, Mn, Pb, and Zn showed that about 48-52% of all the sampling locations are not enriched, nevertheless, approximately 2-11% of all the sampling locations are significantly enriched, an indication of anthropogenic input. However, results of ecological risk assessment showed no connection with sediment enrichment as most sampling locations have concentrations below the threshold probable effect concentration (PEC) value. Statistical analysis using principal component analysis (PCA) extracted three significant components explaining over 72% of total variance covering elements having origin in both natural and anthropogenic sources, thus suggesting that the concentrations and distribution of these elements within stream sediments are related to a combination of weathering processes on the bedrock geology, and anthropogenic activities.

Operational Hydrological Forecasting during the IPHEx-IOP Campaign - Meet the Challenge.

An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May-June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6 hours, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: 1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; 2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and 3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts were significantly improved by assimilating discharge observations into the DCHM. Specifically, Nash-Sutcliff Efficiency (NSE) values as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region demonstrating that the assimilation of discharge observations at the basin's outlet can reduce the errors and uncertainties in soil moisture at very small scales. Success in operational flood forecasting at lead times of 6, 9, 12 and 15hrs was also achieved through discharge assimilation with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. Analysis of experiments using various data assimilation system configurations indicates that the optimal assimilation time window depends both on basin properties and storm-specific space-time-structure of rainfall, and therefore adaptive, context-aware, configurations of the data assimilation system are recommended to address the challenges of flood prediction in headwater basins.

Investigating the relationship between eye movements and situation awareness in weather forecasting.

Physiological indicators, including eye tracking measures, may provide insight into human decision making and cognition in many domains, including weather forecasting. Situation awareness (SA), a critical component of forecast decision making, is commonly conceptualized as the degree to which information is perceived, understood, and projected into a future context. Drawing upon recent applications of eye tracking in the study of forecaster decision making, we investigate the relationship among eye movement measures, automation, and SA assessed through a freeze probe assessment method. In addition, we explore the relationship between an automated forecasting decision aid use and information seeking behavior. In this study, a sample of professional weather forecasters completed a series of tasks, informed by a set of forecasting decision aids, and with variable access to an experimental automated tool, while an eye tracking system captured data related to eye movements and information usage. At the end of each forecasting task, participants responded to a set of questions related to the environmental situation in the framework of a survey-based assessment technique in order to assess their level of situation awareness. Regression analysis revealed a moderate relationship between the SA measure and eye tracking metrics, supporting the hypothesis that eye tracking may have utility in assessing SA. The results support the use of eye tracking in the assessment of specific and measurable attributes of the decision-making process in weather forecasting. The findings are discussed in light of potential benefits that eye tracking could bring to human performance assessment as well as decision-making research in the forecasting domain.

On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates.

The launch of NOAA's latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.

Synthesis and evaluation in monkey of two sensitive 11C-labeled aryloxyanilide ligands for imaging brain peripheral benzodiazepine receptors in vivo.

We sought to develop (11)C-labeled ligands for sensitive imaging of brain peripheral benzodiazepine receptors (PBR) in vivo. Two aryloxyanilides with high affinity for PBR were identified and synthesized, namely, N-acetyl- N-(2-methoxycarbonylbenzyl)-2-phenoxyaniline ( 3, PBR01) and N-(2-methoxybenzyl)- N-(4-phenoxypyridin-3-yl)acetamide ( 10, PBR28). 3 was hydrolyzed to 4, which was esterified with [ (11)C]iodomethane to provide [ (11)C] 3. The O-desmethyl analogue of 10 was converted into [ (11)C] 10 with [ (11)C]iodomethane. [ (11)C] 3 and [ (11)C] 10 were each injected into monkey to assess their brain kinetics with positron emission tomography (PET). After administration of either radioligand there was moderately high brain uptake of radioactivity. Receptor blocking and displacement experiments showed that a high proportion of this radioactivity was bound specifically to PBR. In monkey and rat, 3 and 10 were rapidly metabolized by ester hydrolysis and N-debenzylation, respectively, each to a single polar radiometabolite. [ (11)C] 3 and [ (11)C] 10 are effective for imaging PBR in monkey brain. [ (11)C] 10 especially warrants further evaluation in human subjects.

Vegetation Greening and Climate Change Promote Multidecadal Rises of Global Land Evapotranspiration.

Recent studies showed that anomalous dry conditions and limited moisture supply roughly between 1998 and 2008, especially in the Southern Hemisphere, led to reduced vegetation productivity and ceased growth in land evapotranspiration (ET). However, natural variability of Earth's climate system can degrade capabilities for identifying climate trends. Here we produced a long-term (1982-2013) remote sensing based land ET record and investigated multidecadal changes in global ET and underlying causes. The ET record shows a significant upward global trend of 0.88 mm yr(-2) (P < 0.001) over the 32-year period, mainly driven by vegetation greening (0.018% per year; P < 0.001) and rising atmosphere moisture demand (0.75 mm yr(-2); P = 0.016). Our results indicate that reduced ET growth between 1998 and 2008 was an episodic phenomenon, with subsequent recovery of the ET growth rate after 2008. Terrestrial precipitation also shows a positive trend of 0.66 mm yr(-2) (P = 0.08) over the same period consistent with expected water cycle intensification, but this trend is lower than coincident increases in evaporative demand and ET, implying a possibility of cumulative water supply constraint to ET. Continuation of these trends will likely exacerbate regional drought-induced disturbances, especially during regional dry climate phases associated with strong El Niño events.

Single-step high-yield radiosynthesis and evaluation of a sensitive 18F-labeled ligand for imaging brain peripheral benzodiazepine receptors with PET.

Elevated levels of peripheral benzodiazepine receptors (PBR) are associated with activated microglia in their response to inflammation. Hence, PBR imaging in vivo is valuable for investigating brain inflammatory conditions. Sensitive, easily prepared, and readily available radioligands for imaging with positron emission tomography (PET) are desirable for this purpose. We describe a new 18F-labeled PBR radioligand, namely [18F]N-fluoroacetyl-N-(2,5-dimethoxybenzyl)-2-phenoxyaniline ([18F]9). [18F]9 was produced easily through a single and highly efficient step, the reaction of [18F]fluoride ion with the corresponding bromo precursor, 8. Ligand 9 exhibited high affinity for PBR in vitro. PET showed that [18F]9 was avidly taken into monkey brain and gave a high ratio of PBR-specific to nonspecific binding. [18F]9 was devoid of defluorination in rat and monkey and gave predominantly polar radiometabolite(s). In rat, a low level radiometabolite of intermediate lipophilicity was identified as [18F]2-fluoro-N-(2-phenoxyphenyl)acetamide ([18F]11). [18F]9 is a promising radioligand for future imaging of PBR in living human brain.

Brain and whole-body imaging in nonhuman primates of [11C]PBR28, a promising PET radioligand for peripheral benzodiazepine receptors.

OBJECTIVES: Peripheral benzodiazepine receptors (PBRs) are upregulated on activated microglia and are thereby biomarkers of neuroinflammation. We developed a PET ligand with an aryloxyanilide structure, [O-methyl-(11)C]N-acetyl-N-(2-methoxybenzyl)-2-phenoxy-5-pyridinamine ([(11)C]PBR28), to image PBRs. The objectives of the current study were to evaluate kinetics of brain uptake, and the influence of the peripheral binding on the arterial input function in rhesus monkey. METHODS: Brain (baseline: n=6, blocking: n=1) and whole-body PET imaging (baseline: n=3, blocking: n=1) of [(11)C]PBR28 were performed with the measurement of radiometabolite-corrected arterial input function in all brain and two whole body scans. RESULTS: Saturating doses of nonradioactive PBR ligands markedly increased [(11)C]PBR28 in plasma (approximately 400% increase) and brain (approximately 200%) at 2 min by displacing radioligand from PBRs in peripheral organs. Brain uptake of radioactivity peaked in baseline scans at approximately 40 min after injection of [(11)C]PBR28 and was high (approximately 300% standardized uptake value). The images showed no receptor-free region that could be used for reference tissue analysis. Thus, quantitation of receptor density required measurement of parent radioligand in arterial plasma. Nondisplaceable uptake was estimated from the blocked scans and was only approximately 5% of total distribution volume measured under baseline conditions. Distribution volume of [(11)C]PBR28 was stably determined within 110 min of scanning. CONCLUSIONS: Regional brain uptake of [(11)C]PBR28 in monkey could be quantified as a value proportional to the density of receptors--namely, as equilibrium distribution volume. [(11)C]PBR28 had high levels of specific binding in brain and should provide a sensitive measure of changes in PBRs.

Kinetic evaluation in nonhuman primates of two new PET ligands for peripheral benzodiazepine receptors in brain.

Peripheral benzodiazepine receptors (PBRs) are upregulated on activated microglia and are, thereby, biomarkers of cellular inflammation in brain. We recently developed two PET ligands with an aryloxyanilide structure to image PBRs and now evaluate the kinetics of these radiotracers in monkey to determine whether they are suitable to explore in human. Baseline and receptor-blocking scans were performed with [(11)C]PBR01 and [(18)F]PBR06 in conjunction with serial measurements of the arterial plasma concentration of parent radiotracer separated from radiometabolite. We used brain and plasma data with compartmental modeling to calculate regional brain distribution volume, which is equal to the ratio at equilibrium of the concentration of radioligand in brain to that of plasma. The distribution volume of [(11)C]PBR01 was inaccurately estimated in the baseline scans, possibly because of the short half-life of (11)C or the presence of radiometabolite in brain. In contrast, the distribution volume of [(18)F]PBR06 was stably determined within 200 min of scanning, and nondisplaceable uptake was only approximately 10% of total brain uptake. [(18)F]PBR06 is promising for use in human because brain activity could be quantified with standard compartmental models and showed higher ratios ( approximately 10:1) of specific to nonspecific uptake. A critical factor for human use will be whether the tracer has adequately fast wash out from brain relative to the half-life of the radionuclide to obtain stable values of distribution volume.