Doctor of Philosophy

dissertationWildfire is a common hazard in the western U.S. that can cause significant loss of life and property. When a fire approaches a community and becomes a threat to the residents, emergency managers need to take into account both fire behavior and the expected response of the threatened population to warnings before they issue protective action recommendations to the residents at risk. In wildfire evacuation practices, incident commanders use prominent geographic features (e.g., rivers, roads, and ridgelines) as trigger points, such that when a fire crosses a feature, the selected protective action recommendation will be issued to the residents at risk. This dissertation examines the dynamics of evacuation timing by coupling wildfire spread modeling, trigger modeling, reverse geocoding, and traffic simulation to model wildfire evacuation as a coupled human-environmental system. This dissertation is composed of three manuscripts. In the first manuscript, wildfire simulation and household-level trigger modeling are coupled to stage evacuation warnings. This work presents a bottom-up approach to constructing evacuation warning zones and is characterized by fine-grain, data-driven spatial modeling. The results in this work will help improve our understanding and representation of the spatiotemporal dynamics in wildfire evacuation timing and warnings. The second manuscript integrates trigger modeling and reverse geocoding to extract and select prominent geographic features along the boundary of a trigger buffer. A case study using a global gazetteer GeoNames demonstrates the potential value of the proposed method in facilitating communications in real-world evacuation practice. This work also sheds light on using reverse geocoding in other environmental modeling applications. The third manuscript explores the spatiotemporal dynamics behind evacuation timing by coupling fire and traffic simulation models. The proposed method sets wildfire evacuation triggers based on the estimated evacuation times using agent-based traffic simulation and could be potentially used in evacuation planning. In summary, this dissertation enriches existing trigger modeling approaches by coupling fire simulation, reverse geocoding, and traffic simulation. A framework for modeling wildfire evacuation as a coupled human-environmental system using triggers is proposed. Moreover, this dissertation also attempts to advocate and promote open science in wildfire evacuation modeling by using open data and software tools in different phases of modeling and simulation

Doctor of Philosophy

dissertationWildfire is a common hazard in the western U.S. that can cause significant loss of life and property. When a fire approaches a community and becomes a threat to the residents, emergency managers need to take into account both fire behavior and the expected response of the threatened population to warnings before they issue protective action recommendations to the residents at risk. In wildfire evacuation practices, incident commanders use prominent geographic features (e.g., rivers, roads, and ridgelines) as trigger points, such that when a fire crosses a feature, the selected protective action recommendation will be issued to the residents at risk. This dissertation examines the dynamics of evacuation timing by coupling wildfire spread modeling, trigger modeling, reverse geocoding, and traffic simulation to model wildfire evacuation as a coupled human-environmental system. This dissertation is composed of three manuscripts. In the first manuscript, wildfire simulation and household-level trigger modeling are coupled to stage evacuation warnings. This work presents a bottom-up approach to constructing evacuation warning zones and is characterized by fine-grain, data-driven spatial modeling. The results in this work will help improve our understanding and representation of the spatiotemporal dynamics in wildfire evacuation timing and warnings. The second manuscript integrates trigger modeling and reverse geocoding to extract and select prominent geographic features along the boundary of a trigger buffer. A case study using a global gazetteer GeoNames demonstrates the potential value of the proposed method in facilitating communications in real-world evacuation practice. This work also sheds light on using reverse geocoding in other environmental modeling applications. The third manuscript explores the spatiotemporal dynamics behind evacuation timing by coupling fire and traffic simulation models. The proposed method sets wildfire evacuation triggers based on the estimated evacuation times using agent-based traffic simulation and could be potentially used in evacuation planning. In summary, this dissertation enriches existing trigger modeling approaches by coupling fire simulation, reverse geocoding, and traffic simulation. A framework for modeling wildfire evacuation as a coupled human-environmental system using triggers is proposed. Moreover, this dissertation also attempts to advocate and promote open science in wildfire evacuation modeling by using open data and software tools in different phases of modeling and simulation

Modeling the distributed effects of forest thinning on the long-term water balance and streamflow extremes for a semi-arid basin in the southwestern US

abstract: To achieve water resource sustainability in the water-limited southwestern US, it is critical to understand the potential effects of proposed forest thinning on the hydrology of semi-arid basins, where disturbances to headwater catchments can cause significant changes in the local water balance components and basinwise streamflows. In Arizona, the Four Forest Restoration Initiative (4FRI) is being developed with the goal of restoring 2.4 million acres of ponderosa pine along the Mogollon Rim. Using the physically based, spatially distributed triangulated irregular network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) model, we examine the potential impacts of the 4FRI on the hydrology of Tonto Creek, a basin in the Verde–Tonto–Salt (VTS) system, which provides much of the water supply for the Phoenix metropolitan area. Long-term (20-year) simulations indicate that forest removal can trigger significant shifts in the spatiotemporal patterns of various hydrological components, causing increases in net radiation, surface temperature, wind speed, soil evaporation, groundwater recharge and runoff, at the expense of reductions in interception and shading, transpiration, vadose zone moisture and snow water equivalent, with south-facing slopes being more susceptible to enhanced atmospheric losses. The net effect will likely be increases in mean and maximum streamflow, particularly during El Niño events and the winter months, and chiefly for those scenarios in which soil hydraulic conductivity has been significantly reduced due to thinning operations. In this particular climate, forest thinning can lead to net loss of surface water storage by vegetation and snowpack, increasing the vulnerability of ecosystems and populations to larger and more frequent hydrologic extreme conditions on these semi-arid systems.This article and any associated published material is distributed under the Creative Commons Attribution 3.0 License. View the article as published at: http://www.hydrol-earth-syst-sci.net/20/1241/2016

Spatio-temporal optimization of tree removal to efficiently minimize crown fire potential

High-intensity wildfires have resulted in large financial, social, and environmental costs in the western U.S. This trend is not expected to decline soon, as there are millions of overstocked hectares at medium to high risk of catastrophic wildfires. Thinning is being widely used to restore different types of overstocked forest stands. Typically, thinning prescriptions are derived from average stand attributes and applied to landscapes containing a large number of stands. Stand-level thinning prescriptions have thus limitations when applied for reducing the risk of high-intensity wildfires. They use indicators of crown fire potential (e.g., canopy base height and canopy bulk density) that ignore variability of fuels within stands, location of individual cut- and leave-trees after treatments, and the temporal effects of these prescriptions for reducing crown fire potential over time. To address the limitations of current stand-level thinning prescriptions, a computerized approach to optimize individual tree removal and produce site-specific thinning prescriptions was designed. Based on stem maps and tree attributes derived from light detection and technology (LiDAR), the approach predicts individual tree growth over time, quantifies tree-level fuel connectivity, and estimates skidding costs for individual trees. The approach then selects the spatial combination of cut-trees that most efficiently reduces crown fire potential over time while ensuring cost efficiency of the thinning treatment

Wildland Fire Smoke in the United States

This open access book synthesizes current information on wildland fire smoke in the United States, providing a scientific foundation for addressing the production of smoke from wildland fires. This will be increasingly critical as smoke exposure and degraded air quality are expected to increase in extent and severity in a warmer climate. Accurate smoke information is a foundation for helping individuals and communities to effectively mitigate potential smoke impacts from wildfires and prescribed fires. The book documents our current understanding of smoke science for (1) primary physical, chemical, and biological issues related to wildfire and prescribed fire, (2) key social issues, including human health and economic impacts, and (3) current and anticipated management and regulatory issues. Each chapter provides a summary of priorities for future research that provide a roadmap for developing scientific information that can improve smoke and fire management over the next decade

Carbon exchange in boreal ecosystems: upscaling and the impacts of natural disturbances

Boreal forests and peatlands are globally significant carbon stores but they are threatened by rising air temperatures and changes in precipitation regimes and the frequency of natural disturbances. Predicting how the boreal biome will respond to climate change depends on being able to accurately model and upscale the greenhouse gas fluxes between these ecosystems and the atmosphere. This thesis focuses on developing simple, empirical, remote sensing models of ecosystem respiration (ER) and assessing how ER varies over space and in response to natural disturbances such as drought and wildfire. Paper I and II tested the opportunities and limitations offered by newly available, miniaturized thermal cameras, which can capture images of surface temperature at sub-decimeter resolution. Since temperature is one of the most important factors driving ER, these cameras provide an opportunity to map ER in unprecedented detail. In Paper III, satellite land surface temperature (LST) data was used to model ER across several Nordic peatland sites to examine whether remote sensing models can capture variations in ER between sites. In addition, Paper II and III highlighted the impacts of the extreme 2018 drought that affected large parts of Europe on peatland CO2 fluxes. The drought also led to a severe wildfire season in Sweden and Paper IV investigated how the effects of fire on forest soils depended on burn severity, salvage-logging and stand age.Producing reliable surface temperature measurements from miniaturized thermal cameras requires careful data collection and processing and one of the main outcomes of this thesis is a set of best practices for thermal camera users. Despite including larger uncertainties than traditional soil or air temperature measurements, thermal data from these cameras is suitable for modeling ER in peatland ecosystems. The ER maps that can be produced using UAV thermal cameras offer a unique resource for evaluating how ER varies within a flux tower footprint and could reveal potential biases in flux tower measurements. Indeed, this thesis demonstrated that there is substantial spatial variation in ER, both within and between peatlands. Vegetation composition played a significant role in driving this spatial variation as well as the response of peatlands to drought. Nevertheless, using only LST and the Enhanced Vegetation Index (EVI2) as model inputs captured a large proportion of the variability of daily ER across multiple peatlands. With further developments, such a modeling approach could represent a simple and effective way of estimating peatland ER across Scandinavia. In terms of wildfire impacts on boreal forest soils, stand age had a significant impact on soil respiration, nutrient availability and microclimate, whereas salvage-logging did not, in the first year after fire. Furthermore, the effects of drought and wildfire on ER depended on their severity, but during both extreme water stress in peatlands and after severe burning in forests, ER decreased. It is important that this negative feedback is accounted for in ER models to avoid overestimating carbon loss from northern ecosystems in response to disturbances and climate change

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Remote continental aerosol characteristics in the Rocky Mountains of Colorado and Wyoming

2013 Spring.Includes bibliographical references.The Rocky Mountains of Colorado and Wyoming enjoy some of the cleanest air in the United States, with few local sources of particulate matter or its precursors apart from fire emissions, windblown dust, and biogenic emissions. However, anthropogenic influences are also present with sources as diverse as the populated Front Range, large isolated power plants, agricultural emissions, and more recently emissions from increased oil and gas exploration and production. While long-term data exist on the bulk composition of background fine particulate matter at remote sites in the region, few long-term observations exist of aerosol size distributions, number concentrations and size resolved composition, although these characteristics are closely tied to important water resource issues through the potential aerosol impacts on clouds and precipitation. Recent modeling work suggests sensitivity of precipitation-producing systems to the availability of aerosols capable of serving as cloud condensation nuclei (CCN); however, model inputs for these aerosols are not well constrained due to the scarcity of data. In this work I present aerosol number and volume concentrations, size distributions, chemical composition and hygroscopicity measurements from long-term field campaigns. I also explore the volatility of organic material from biomass burning and the potential impacts on aerosol loading. Relevant aerosol observations were obtained in several long-term field studies: the Rocky Mountain Atmospheric Nitrogen and Sulfur study (RoMANS, Colorado), the Grand Tetons Reactive Nitrogen Deposition Study (GrandTReNDS, Wyoming) and as part of the Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics & Nitrogen project (BEACHON, Colorado). Average number concentrations (0.04 < Dp < 20 &#956;m) measured during the field studies ranged between 1000 - 2000 cm-3 during the summer months and decreased to 200 - 500 cm-3 during the winter. These seasonal changes in aerosol number concentrations were correlated with the frequency of events typical of new particle formation. Measured sub-micron organic mass fractions were between 70 - 90% during the summer months, when new particle formation events were most frequent, suggesting the importance of organic species in the nucleation or growth process, or both. Aerosol composition derived from hygroscopicity measurements indicate organic mass fractions of 50 - 60% for particles with diameters larger than 0.15 &#956;m during the winter. The composition of smaller diameter particles appeared to be organic dominated year-round. High organic mass fractions led to low values of aerosol hygroscopicity, described using the &#954; parameter. Over the entire year-long BEACHON study, &#954; had an average value of 0.16 ± 0.08, similar to values determined during biologically active periods in tropical and boreal forests, and lower than the commonly assumed value of &#954;continental = 0.3. There was also an observed increase in &#954; with size, due to external mixing of the fine mode aerosol. Incorrect representations of &#954; or its size dependence led to erroneous values of calculated CCN concentrations, especially for supersaturation values less than 0.3%. At higher supersaturations, most of the measured variability in CCN concentrations was captured by changes in total measured aerosol number concentrations. While data from the three measurement sites were generally well correlated, indicating similarities in seasonal cycles and in total number concentrations, there were some variations between measurements made at different sites and during different years that may be partly due to the effects of local emissions. The averaged data provide reasonable, observationally-based parameters for modeling of aerosol number size distributions and corresponding CCN concentrations. Field observations clearly indicated the episodic influence of wildfire smoke on particle number concentrations and compositions. However, the semi-volatile nature of the organic carbon species emitted makes it difficult to predict how much of the emitted organic mass will remain in the condensed phase downwind. To better constrain the volatility of organic species in smoke, emissions from laboratory biomass combustion experiments were subjected to quantified dilution, resulting in reduction of aerosol mass concentrations over several orders of magnitude and a corresponding volatilization response of the organic particles that was fit to the commonly-applied Volatility Basis Set. Organic emissions from all burns with initial organic aerosol concentrations greater than 1000 &#956;g m-3 contained material with saturation concentration values ranging between 1 and 10,000 &#956;g m-3, with most of the organic mass falling at the two extremes of this range. For most burns, a single distribution was able to capture the volatility behavior of the organic material, within experimental uncertainty, despite the considerable variability in fuel and fire characteristics, suggesting that a simplified two-product model of gas-aerosol partitioning may be adequate to describe the evolution of biomass burning organic aerosol in models

Artificial neural networks to detect forest fire prone areas in the southeast fire district of Mississippi

An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires