Published Papers
Preisser, M., Passalacqua, P., Bixler, R. P., & Boyles, S. (2023). A network-based analysis of critical resource accessibility during floods. Frontiers in Water, 5, 1278205, doi: frwa.2023.1278205
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Abstract
Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.
Household Equity
Austin, Texas household redundancy, reliability, and recoverability metrics during the Memorial Day flood at peak flood conditions (22:00 25 May 2015) while under the influence of 25, 50, or 100% indicator rank weight. Rank weights are the degree to which equality Lorenz curves are weighted by the block group indicator ranks.
Bixler, R. P., Paul, S., Bhakta, D., Farchy, T., Olson, J., Preisser, M., & Passalacqua, P. (2023). Adaptive governance for disaster risk reduction. In Handbook on Adaptive Governance (pp. 233-251). Edward Elgar Publishing. doi: 10.4337/9781800888241.00026
Abstract
The objectives of disaster risk reduction (DRR) and adaptive governance (AG) are similar in many ways, yet the application of adaptive governance to natural hazards mitigation, preparedness, response, recovery, and transformation is somewhat limited. Traditional governance arrangements are too rigid, fragmented, siloed, and mono-scalar to effectively manage for resilience and sustainability in coupled social-ecological-technical systems. Adaptive governance that can effectively respond to the complexities and uncertainties associated with reducing risk from climate-related socio-natural disasters is necessary. We refer to this as adaptive hazard governance. We elaborate on four characteristics of adaptive governance - polycentricity, collaboration, self-organisation, and learning - and discuss the relevance for disaster risk reduction. We then explore the examples from the literature and present a case study of Houston, Texas post-Hurricane Harvey. We conclude with the challenges, opportunities, and a toolkit for advancing adaptive natural hazard governance.
Preisser, M., Passalacqua, P., Bixler, R. P., & Hofmann, J. (2022). Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index. Hydrology and Earth System Sciences, 26(15), 3941-3964, doi: hess-26-3941-2022
Abstract
Increased interest in combining compound flood hazards and social vulnerability has driven recent advances in flood impact mapping. However, current methods to estimate event-specific compound flooding at the household level require high-performance computing resources frequently not available to local stakeholders. Government and non-governmental agencies currently lack the methods to repeatedly and rapidly create flood impact maps that incorporate the local variability in both hazards and social vulnerability. We address this gap by developing a methodology to estimate a flood impact index at the household level in near-real time, utilizing high-resolution elevation data to approximate event-specific inundation from both pluvial and fluvial sources in conjunction with a social vulnerability index. Our analysis uses the 2015 Memorial Day flood in Austin, Texas, as a case study and proof of concept for our methodology. We show that 37 % of the census block groups in the study area experience flooding from only pluvial sources and are not identified in local or national flood hazard maps as being at risk. Furthermore, averaging hazard estimates to cartographic boundaries masks household variability, with 60 % of the census block groups in the study area having a coefficient of variation around the mean flood depth exceeding 50 %. Comparing our pluvial flooding estimates to a 2D physics-based model, we classify household impact accurately for 92 % of households. Our methodology can be used as a tool to create household compound flood impact maps to provide computationally efficient information to local stakeholders.
Inundated Block Groups
Percent increase in inundation extent by census block group when comparing fluvial/pluvial flooding with only fluvial sources during the 2015 Memorial Day flood in Austin, Texas. Darker colors signify a greater percent increase, with black block groups only experiencing pluvial sources.
Bixler, R. P., Paul, S., Jones, J., Preisser, M., & Passalacqua, P. (2021). Unpacking adaptive capacity to flooding in urban environments: Social capital, social vulnerability, and risk perception. Frontiers in Water, 3, 728730, doi: frwa.2021.728730
Abstract
To effectively cope with the impacts of climate change and increase urban resilience, households and neighborhoods must adapt in ways that reduce vulnerability to climate-related natural hazards. Communities in the United States and elsewhere are exposed to more frequent extreme heat, wildfires, cyclones, extreme precipitation, and flooding events. Whether and how people respond to increased hazard exposure (adaptive behavior) is widely recognized to be driven by their capacity to adapt, perception of the risk, and past experiences. Underlying these important dimensions, however, is social context. In this paper, we examine how social capital and social vulnerability shape risk perception and household flood mitigation actions. The study, based on a metropolitan-wide survey of households in Austin, Texas, USA, suggests that bonding social capital (personal networks, neighborhood cohesion, and trust) is positively related to mitigation behavior and that social vulnerability is negatively related to risk perception. Importantly, our research demonstrates a positive and significant effect of social capital on adaptive behavior even when controlling for social vulnerability of a neighborhood. This suggests that policies and programs that strengthen the social connectedness within neighborhoods can increase adaptive behaviors thus improving community resilience to flood events.
Social Capital
In this study, we focus primarily on bonding social capital and measure it as three distinct dimensions - trust, cohesion, and personal networks—that form one latent construct of social capital. The specific items come from insights from meta-analysis on the measurement of social capital.
Chaump, K., Preisser, M., Shanmugam, S. R., Prasad, R., Adhikari, S., & Higgins, B. T. (2019). Leaching and anaerobic digestion of poultry litter for biogas production and nutrient transformation. Waste management, 84, 413-422, doi: j.wasman.2018.11.024
Abstract
Anaerobic digestion of poultry litter is a potentially sustainable means of stabilizing this waste while generating biogas. However, technical challenges remain including seasonality of litter production, low C/N ratios, limited digestibility of bedding, and questions about transformation of nutrients during digestion. This study investigated biogas production and nutrient transformations during anaerobic digestion of poultry litter leachate and whole litter. Use of fresh litter collected from within the house was also compared to waste litter cake that was stored outdoors on the farm. The results showed that litter leachates had higher biomethane potential (0.24–0.30 L/gVS) than whole litter (0.15–0.16 L/gVS) and the insoluble bedding material left after leaching (0.08–0.13 L/gVS). Leachates prepared from waste litter cake had lower uric acid and higher acetic acid concentrations than fresh litter indicating that decomposition had occurred during storage. Consequently, waste litter cake had faster initial biogas production but lower final biogas yields compared to fresh litter. In all reactors, uric and acetic acids were completely consumed during digestion, phosphate levels decreased but ammonium levels increased. The results demonstrate that poultry litter leachate is amenable to digestion despite a low C/N ratio and that the remaining insoluble bedding material has been partially stripped of its nutrients. Moreover, litter can be stored prior to digestion but some losses in biomethane potential should be expected due to decomposition of organics during storage.
Under Review and In Preparation Papers
Preisser, M., Passalacqua, P., Bixler, (2024). SVInsight: A python package for calculating social vulnerability indices, Journal of Open Source Software. Under Review.
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Summary
A community’s exposure to environmental hazards, their sensitivity to such events, and their ability to respond (adaptive capacity) are influenced by their social, political, cultural, economic, and demographic information (Cutter et al., 2010; Fatemi et al., 2017; Smit & Wandel, 2006). Understanding the interconnected relationships among exposure, sensitivity, and adaptive capacity is important to estimate the degree to which stakeholders can mitigate environmental hazards (Smit & Wandel, 2006). Social Vulnerability Indices, or SVIs, are built on social and demographic data to serve as proxies for these interconnected variables. Numerous national and international SVIs exist including SoVI ® from The University of South Carolina’s Hazards Vulnerability & Resilience Institute (Cutter et al., 2003), the Center for Disease Control (Flanagan et al., 2011), and the United Nations Development Program (UNDP, 2010), as well as more region specific indicators including Texas A&M’s Hazard Reduction and Recovery Center’s Coastal Indicator (Peacock et al., 2010) and the Austin Area Sustainability Indicator (Bixler et al., 2021). In this paper, we present an open-source Python package, SVInsight, which provides an accessible workflow to calculate social vulnerability indices that are specific to user defined study areas while using the two leading calculation methodologies.
Social Vulnerability of Travis County
Social Vulnerability estimate for Travis County, TX, developed using the SVInsight python package.
In Preparation Papers
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Preisser, M., Passalacqua, P., Bixler, P., Kamath, H., Niyogi, D. (2024). Daily satellite imagery for multi-hazard event detection, In Preparation.
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Rosenheim, N., Mezei, L., Preisser, M., Brelsford, C., Bixler, P., Meyer, M., Ahmed, F. (2024). Social Vulnerability Indices: Exploring Assumptions and Limitations, In Preparation.
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Karimaghaei, S., Passalacqua, P., Preisser, M., Bixler, P. (2024). Connecting morphological hot spots and social vulnerabilities in coastal Louisiana, In Preparation.
Presentations
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Preisser, M., P. Passalacqua, P. Bixler, H. Kamath, D. Niyogi (2023), Multiple hazards from multiple satellites: Analyzing exposure to cascading hazards using daily Earth observation data from the past decade, NH33D-0822 presented at the 2023 Fall Meeting, AGU, 11-15 December.
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Preisser, M., P. Passalacqua, P. Bixler, (2023), A network-based disaster resilience metric for estimating individuals’ loss of access to critical resources during flooding, presented at 2023 Annual Meeting, EGU, 23-28 April.
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Preisser, M., P. Passalacqua, P. Bixler (2022), Helping Those Who Need It Most: Open Access Tool for Modeling Flood Hazards and Community Adaptive Capacity in Near-Real Time, H55M-0745 presented at 2022 Fall Meeting, AGU, 12-16 December.
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Passalacqua, P., M. Preisser, M. Wang, P. Bixler, A. Hooks, J. Hofmann, L. Haselbach, H. Moftakhari, H. Evans, C. Thies, D. Maidment (2022), Preparing for Future Floods: Leveraging Remotely Sensed Data, Modeling, and Social Science Information in a Multilayer Network Approach, H46D-01 Invited talk presented at 2022 Fall Meeting, AGU, 12-16 December.
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Karimaghaei, S., P. Passalacqua, M. Preisser, P. Bixler, (2022) Identifying Morphological Hot Spots and Social Vulnerabilities along the Gulf Coast using Remote Sensing, EP15B-1087 presented at 2022 Fall Meeting, AGU, 12-16 December.
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Preisser, M., P. Passalacqua, P. Bixler (2021), Interconnecting Networks of Social Vulnerability, Resource Access, and High-Resolution Inundation to Quantify Household Flood Impact, H55V-0993 presented at 2021 Fall Meeting, AGU, 13-17 December.
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Preisser, M., P. Passalacqua, P. Bixler (2020), A Multiplex approach to integrate social vulnerability into urban flood mapping, H139-0015A presented at 2020 Fall Meeting, AGU, 1-17 December. doi: 10.1002/essoar.10505385.1
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Preisser, M., N. Molder, K. Benedict, C. Shapiro, C. Straub, T. Stryker (2020), Expanding the Societal Benefits of Earth Observations through Leading Practices for Public Private Partnerships, SY003-0002 presented at 2020 Fall Meeting, AGU, 1-17 December.
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Newman, T., Z. Wu, B. Reed, T. Stryker, M. Preisser (2020), Assessing COVID-19 environmental impacts through remote sensing, U012-05 presented at 2020 Fall Meeting, AGU, 1-17 December.
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Garcia, K., M. Preisser, C. Nauman (2019), Assessing Water Quality in Thailand’s Chao Phraya Watershed by Modeling Sediment Concentration and Urban Footprint, presented at 2019 Annual Meeting, AAG, 5 April, & 2018 Wernher von Braun Memorial Symposium, AAS, 23-25 October.
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Preisser, M., B. Higgins (2018), Anaerobic digestion of poultry litter and food waste for biogas production, presented at National Conference on Undergraduate Research, NCUR, 4-7 April.
Selected presentations shown below (click to download pdf copy). Additional presentations available by request
Convened Sessions
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Ellis, E., P. Becker, A. Price, M. Preisser, (2023) Communicating Science Beyond the Paper: Thinking Outside the Boxplot, INV13A Co-Convened Innovative Session at 2023 Fall Meeting, AGU, 11-15 December.
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Peterson, D., L. Miralha, J. Lerbeck, M. Preisser, J. Allen, (2023), Where and How Can AGU Assist? Discussion About the Future of Early-Career and Student Members Within AGU, TH33G Co-Convened Town Hall at 2023 Fall Meeting, AGU, 11-15 December.
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Preisser, M., A. Jha, J. Ali, X. Sui, S. Adelsperger, (2023), Building Your Network: Sharpening the Soft Skills of Science, TH43B Co-Convened Town Hall at 2023 Fall Meeting, AGU, 11-15 December.
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Patterson, J., L. Miralha, M. Preisser, (2022), Building Your Network: AGU-H3S Networking, TH13D Co-Convened Town Hall at 2022 Fall Meeting, AGU, 12-16 December.