Conversion of the BlueSky Framework into collaborative web service architecture and creation of a smoke modeling application

This project addresses the need for a collaborative architecture for scientific modeling that allows various scientific models to easily interact. The need for such a system has been documented by recent studies such as the JFSP Smoke Roundtables and the JFSP review of tools done by the Software Engineering Institute. This project addresses these needs by modifying the BlueSky Modeling Framework so that it can better serve as a collaborative architecture, and then utilizing this architecture to create an advanced application that could not otherwise be created. The BlueSky framework was modified for this purpose, and all changes integrated into all versions of BlueSky from 3.1.0 forward. BlueSky now contains a command line option that will automatically start it as a web-service provider, allowing it to be used by remote clients. When the web-service option is used, all models contained within BlueSky are automatically converted into web-service accessible modules, without need for a specialized web-service enabled version. Simple examples and documentation scripts designed to show a website or user interface creator how to access these models via web-service function calls were created. In addition, a more advanced website interface was created to show some of the advantages of web-service based scientific modeling. This tool, called BlueSky Playground, provides a single user interface into 10 models of fuels, consumption, emissions, plume rise, and smoke dispersion. A user can walk step-by-step through all of the model steps in the framework from fire information to smoke impact maps. At each step the user can choose the model they want to use and alter the modeled information before continuing on, allowing for a game-playing exploratory mode of interaction. Both the ability to access so many models through a single interface as well as the capability to obtain on-the-fly smoke dispersion calculations are novel to this tool. This application will be highlighted in 2010 through RX-410 classes as a way for users to learn about the various component models. It will also serve as a training tool for managers needing to run multiple scenarios and understand the implications of various choices. The web-service oriented architecture utilized in the project offers many potential advantages to scientific research done with the goal of decision support. Separation of the scientific computing portion of such work from the user interface allows scientists to focus on creating the best models and web designers to focus on creating the best interfaces. Remote functioning of the models through the web means that local installation of the model is no longer required solving distribution issues, and allows an Internet user to run a model that requires resources not available to them locally (such as large datasets or fast processors). Modularity allows for “mash-ups” where models are combined in ways not originally foreseen to meet emerging needs, and provides choices to be made on exact modeling pathways at the user or institutional level

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health

The midgut transcriptome of Phlebotomus (Larroussius) perniciosus, a vector of Leishmania infantum: comparison of sugar fed and blood fed sand flies

<p>Abstract</p> <p>Background</p> <p>Parasite-vector interactions are fundamental in the transmission of vector-borne diseases such as leishmaniasis. <it>Leishmania </it>development in the vector sand fly is confined to the digestive tract, where sand fly midgut molecules interact with the parasites. In this work we sequenced and analyzed two midgut-specific cDNA libraries from sugar fed and blood fed female <it>Phlebotomus perniciosus </it>and compared the transcript expression profiles.</p> <p>Results</p> <p>A total of 4111 high quality sequences were obtained from the two libraries and assembled into 370 contigs and 1085 singletons. Molecules with putative roles in blood meal digestion, peritrophic matrix formation, immunity and response to oxidative stress were identified, including proteins that were not previously reported in sand flies. These molecules were evaluated relative to other published sand fly transcripts. Comparative analysis of the two libraries revealed transcripts differentially expressed in response to blood feeding. Molecules up regulated by blood feeding include a putative peritrophin (<it>PperPer1</it>), two chymotrypsin-like proteins (<it>PperChym1 </it>and <it>PperChym2</it>), a putative trypsin (<it>PperTryp3</it>) and four putative microvillar proteins (<it>PperMVP1</it>, <it>2</it>, <it>4 </it>and <it>5</it>). Additionally, several transcripts were more abundant in the sugar fed midgut, such as two putative trypsins (<it>PperTryp1 </it>and <it>PperTryp2</it>), a chymotrypsin (<it>PperChym3</it>) and a microvillar protein (<it>PperMVP3</it>). We performed a detailed temporal expression profile analysis of the putative trypsin transcripts using qPCR and confirmed the expression of blood-induced and blood-repressed trypsins. Trypsin expression was measured in <it>Leishmania infantum</it>-infected and uninfected sand flies, which identified the <it>L. infantum</it>-induced down regulation of <it>PperTryp3 </it>at 24 hours post-blood meal.</p> <p>Conclusion</p> <p>This midgut tissue-specific transcriptome provides insight into the molecules expressed in the midgut of <it>P. perniciosus</it>, an important vector of visceral leishmaniasis in the Old World. Through the comparative analysis of the libraries we identified molecules differentially expressed during blood meal digestion. Additionally, this study provides a detailed comparison to transcripts of other sand flies. Moreover, our analysis of putative trypsins demonstrated that <it>L. infantum </it>infection can reduce the transcript abundance of trypsin <it>PperTryp3 </it>in the midgut of <it>P. perniciosus</it>.</p

A Protocol for Collecting Burned Area Time Series Cross-Check Data

Data on wildfire growth are useful for multiple research purposes but are frequently unavailable and often have data quality problems. For these reasons, we developed a protocol for collecting daily burned area time series from the InciWeb website, Incident Management Situation Reports (IMSRs), and other sources. We apply this protocol to create the Warehouse of Multiple Burned Area Time Series (WoMBATS) data, which are a collection of burned area time series with cross-check data for 514 wildfires in the United States for the years 2018&ndash;2020. We compare WoMBATS-derived distributions of wildfire occurrence and size to those derived from MTBS data to identify potential biases. We also use WoMBATS data to cross tabulate the frequency of missing data in InciWeb and IMSRs and calculate differences in size estimates. We identify multiple instances where WoMBATS data fails to reproduce wildfire occurrence and size statistics derived from MTBS data. We show that WoMBATS data are typically much more complete than either of the two constituent data sources, and that the data collection protocol allows for the identification of otherwise undetectable errors. We find that although disagreements between InciWeb and IMSRs are common, the magnitude of these differences are usually small. We illustrate how WoMBATS data can be used in practice by validating two simple wildfire growth forecasting models

Modeling very large-fire occurrences over the continental United States from weather and climate forcing

Very large-fires (VLFs) have widespread impacts on ecosystems, air quality, fire suppression resources, and in many regions account for a majority of total area burned. Empirical generalized linear models of the largest fires (>5000 ha) across the contiguous United States (US) were developed at ∼60 km spatial and weekly temporal resolutions using solely atmospheric predictors. Climate−fire relationships on interannual timescales were evident, with wetter conditions than normal in the previous growing season enhancing VLFs probability in rangeland systems and with concurrent long-term drought enhancing VLFs probability in forested systems. Information at sub-seasonal timescales further refined these relationships, with short-term fire weather being a significant predictor in rangelands and fire danger indices linked to dead fuel moisture being a significant predictor in forested lands. Models demonstrated agreement in capturing the observed spatial and temporal variability including the interannual variability of VLF occurrences within most ecoregions. Furthermore the model captured the observed increase in VLF occurrences across parts of the southwestern and southeastern US from 1984 to 2010 suggesting that, irrespective of changes in fuels and land management, climatic factors have become more favorable for VLF occurrence over the past three decades in some regions. Our modeling framework provides a basis for simulations of future VLF occurrences from climate projections

Multi-Model Forecasts of Very-Large Fire Occurences during the End of the 21st Century

Climate change is anticipated to influence future wildfire activity in complicated, and potentially unexpected ways. Specifically, the probability distribution of wildfire size may change so that incidents that were historically rare become more frequent. Given that fires in the upper tails of the size distribution are associated with serious economic, public health, and environmental impacts, it is important for decision-makers to plan for these anticipated changes. However, at least two kinds of structural uncertainties hinder reliable estimation of these quantities&#8212;those associated with the future climate and those associated with the impacts. In this paper, we incorporate these structural uncertainties into projections of very-large fire (VLF)&#8212;those in the upper 95th percentile of the regional size distribution&#8212;frequencies in the Continental United States during the last half of the 21st century by using Bayesian model averaging. Under both moderate and high carbon emission scenarios, large increases in VLF frequency are predicted, with larger increases typically observed under the highest carbon emission scenarios. We also report other changes to future wildfire characteristics such as large fire frequency, seasonality, and the conditional likelihood of very-large fire events

Multi-scalar influence of weather and climate on very large-fires in the Eastern United States

International audienceA majority of area burned in the Eastern United States (EUS) results from a limited number of exceptionally large wildfires. Relationships between climatic conditions and the occurrence of very large-fires (VLF) in the EUS were examined using composite and climate-niche analyses that consider atmospheric factors across inter-annual, sub-seasonal and synoptic temporal scales. While most large-fires in the EUS coincided with below normal fuel moisture and elevated fire weather, VLF preferentially occurred during a long-term drought accompanied by more acute sub-seasonal drought realized through fuel moisture stress and elevated fire-weather conditions. These results were corroborated across the EUS, with varying influences of drought, fire danger and fire weather discriminating VLF from other large fires across different geographical regions. We also show that the probability of VLF conditioned by fire occurrence increases when long-term drought, depleted fuel moisture and elevated fire weather align. This framework illustrates the compounding role of different timescales in VLF occurrence and serves as a basis for improving VLF predictions with seasonal climate forecasts and climate change scenarios

PHASE 1 OF THE SMOKE AND EMISSIONS MODEL INTERCOMPARISON PROJECT (SEMIP): CREATION OF SEMIP AND EVALUATION OF CURRENT MODELS

Managers, regulators, and others often need information on the emissions from wildland fire and their expected smoke impacts. In order to create this information, combinations of models are utilized. The modeling steps follow a logical progression from fire activity through to emissions and dispersion. In general, several models and/or datasets are available for each modeling step, resulting in a large number of combinations that can be created to produce fire emissions or smoke impacts. Researchers, managers, and policy makers need information on how different model choices affect the resulting output, and guidance on what choices to make in selecting the models that best represent their management requirements. Baseline comparisons are needed between available models that highlight how they intercompare and, where possible, how their results compare with observations. As new models and methods are developed, standard protocols and comparison metrics are necessary to allow for these new systems to be understood in light of previous models and methods. The Smoke and Emissions Model Intercomparison Project (SEMIP) was designed to facilitate such comparisons. This project was designed to be the first step in a broader effort, and hence was titled Phase 1 of SEMIP. In Phase 1, SEMIP: • Examined the needs for fire emissions and smoke impact modeling; • Determined what data were available to help evaluate such models; • Identified a number of test cases that can serve as baseline comparisons between existing models and standard comparisons for new models; • Created a data warehouse and data sharing structure to help facilitate future comparisons; and • Performed a number of intercomparison analyses to examine existing models. SEMIP so far has resulted in: • Multiple peer reviewed journal articles and other documents; • Over 20 presentations; • Discussions with the EPA, JFSP, USFS F&AM, DOI, NWCG, and others on how to improve fire emissions calculations; • New fire emissions analysis tools; • Presentations and discussions with the JFSP on how to gather field observations useful to this type of analyses; and • Discussions with the JFSP on data sharing and archiving. SEMIP has also been acknowledged in recent RFAs from both the JFSP and NASA

Identification of Necessary Conditions for Arctic Transport of Smoke from United States Fires

The deposition of black carbon (BC), a dark absorbing aerosol, is a significant contributor to observed warming trends in the Arctic (Hansen and Nazarenko, 2004; Jacobson et al., 2007). Biomass burning outside of the Arctic, including wildland prescribed fires, is a major potential source of Arctic BC. Therefore, limiting or eliminating spring prescribed burning has been suggested to Congress as a BC reduction technique (e.g., Zender, 2007). However, there are large uncertainties in the current estimates of the sources, source regions, and transport and transformation pathways of BC transported to the Arctic region (Shindell et al., 2008; Hegg et al., 2009, Quinn et al., 2008). This study is the first comprehensive examination of the meteorological conditions required for emissions from the contiguous United States (CONUS) to be transported to Arctic. Using a simple trajectory modeling technique, we characterize the potential for transport of emissions from fires in CONUS to reach the Arctic and Greenland. The potential for Arctic transport is examined as • A 30-year climatology (1980-2009) of transport potential based on trajectory modeling using historical meteorology and split out by season, month, starting plume injection height, and time to reach the Arctic. • A real-time (daily) forecast system of transport potential to the Arctic that shows which layers of the atmosphere can reach the Arctic today, tomorrow, and the next day. The methods used here do not include wet or dry deposition and other factors that can further limit the ability of actual emissions to reach and deposit in the Arctic. Instead, by focusing on only one necessary aspect (a necessary but not sufficient condition) – the ability of the atmosphere to transport emissions – this study examines • Under what meteorological transport conditions can CONUS emissions potentially impact the Arctic? However, this allows for the ability to answer the corollary question: • Under what meteorological transport conditions will CONUS emissions not impact the Arctic? This inverse question allows for identification of times, locations, and plume injection heights where emissions sources (such as a prescribed burn) will not have an impact on the Arctic. This knowledge allows for both more targeted future studies and more precise mitigation strategies that do not focus on areas and times where Arctic impact is unlikely

Thermal landscapes in a changing climate: biological implications of water temperature patterns in an extreme year

Record-breaking droughts and high temperatures in 2015 across the Pacific Northwest, USA provide an opportunistic glimpse into potential future thermal regimes of rivers and their implications for freshwater fishes. We applied spatial stream network models (SSNMs) to data collected every 30 min for four years at 42 sites on the Snoqualmie River (Washington, United States) to compare water temperature patterns, summarized with relevance to particular life stages of native and nonnative fishes, in 2015 to more typical conditions (2012-2014). Although 2015 conditions were drier and warmer than what had been observed since 1960, patterns were neither consistent over the year nor on the network. Some locations showed dramatic increases in air and water temperature whereas others had temperatures that differed little from typical years; these results contrasted with existing forecasts of future thermal landscapes. If we will observe years like 2015 more frequently in the future, we can expect conditions to be less favorable to native, coolwater fishes such as Chinook Salmon and Bull Trout but beneficial to warmwater nonnative species such as Largemouth Bass.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author