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NASA NTRS2024

Kentucky Disasters: Multi-Hazard Approach to Mapping Flood Susceptibility and Vulnerability in Kentucky

Flooding is the most common and costly natural disaster in Kentucky, with major flood events in 2022 and 2023 highlighting the need for flood risk assessment. In partnership with the National Weather Service Jackson and Paducah Forecast Offices and the Kentucky Climate Center, we mapped flood risk in Kentucky using a multi-hazard approach that considered two dimensions of risk: flood susceptibility based on a weighted combination of seven physical factors and flood vulnerability based on 13 socioeconomic and infrastructure factors. We additionally analyzed NASA Soil Moisture Active Passive (SMAP) observations of surface soil moisture to explore the utility of SMAP observations for future analysis of flood risk. By analyzing flood susceptibility, we found that with equal rainfall, western Kentucky generally displays a higher propensity to flood than eastern Kentucky. In contrast, our flood vulnerability analysis indicated that more vulnerable areas were generally concentrated in the eastern part of the state. Through a combined perspective, our flood risk analysis identified much of the state as having moderate degrees of flood susceptibility and vulnerability. Our parallel analysis of antecedent soil moisture found that SMAP soil moisture levels were variable in the months leading up to each flood event but were drier than normal in the month prior to the 2023 event, as shown by negative soil moisture anomalies. These results were limited by challenges with weighting input parameters and a lack of validation but overall demonstrate the feasibility of using GIS and Earth observations for mapping flood risk and soil moisture.

analytic hierarchy process↗
NASA NTRS2017

Comparing Proxy and Model Estimates of Hydroclimate Variability and Change over the Common Era

Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited because of a paucity of modern instrumental observations that are distributed unevenly across the globe and only span parts of the 20th and 21st centuries. Such data coverage is insufficient for characterizing hydroclimate and its associated dynamics because of its multidecadal to centennial variability and highly regionalized spatial signature. High-resolution (seasonal to decadal) hydroclimatic proxies that span all or parts of the Common Era (CE) and paleoclimate simulations from climate models are therefore important tools for augmenting our understanding of hydroclimate variability. In particular, the comparison of the two sources of information is critical for addressing the uncertainties and limitations of both while enriching each of their interpretations. We review the principal proxy data available for hydroclimatic reconstructions over the CE and highlight the contemporary understanding of how these proxies are interpreted as hydroclimate indicators. We also review the available last-millennium simulations from fully coupled climate models and discuss several outstanding challenges associated with simulating hydroclimate variability and change over the CE. A specific review of simulated hydroclimatic changes forced by volcanic events is provided, as is a discussion of expected improvements in estimated radiative forcings, models, and their implementation in the future. Our review of hydroclimatic proxies and last-millennium model simulations is used as the basis for articulating a variety of considerations and best practices for how to perform proxy-model comparisons of CE hydroclimate. This discussion provides a framework for how best to evaluate hydroclimate variability and its associated dynamics using these comparisons and how they can better inform interpretations of both proxy data and model simulations.We subsequently explore means of using proxy-model comparisons to better constrain and characterize future hydroclimate risks. This is explored specifically in the context of several examples that demonstrate how proxy-model comparisons can be used to quantitatively constrain future hydroclimatic risks as estimated from climate model projections.

Common Era↗
NASA NTRS2001

Remote Sensing, GIS, and Vector-Borne Disease

The concept of global climate change encompasses more than merely an alteration in temperature; it also includes spatial and temporal covariations in precipitation and humidity, and more frequent occurrence of extreme weather events. The impact of these variations, which can occur at a variety of temporal and spatial scales, could have a direct impact on disease transmission through their environmental consequences for pathogen, vector, and host survival, as well as indirectly through human demographic and behavioral responses. New and future sensor systems will allow scientists to investigate the relationships between climate change and environmental risk factors at multiple spatial, temporal and spectral scales. Higher spatial resolution will provide better opportunities for mapping urban features previously only possible with high resolution aerial photography. These opportunities include housing quality (e.g., Chagas'disease, leishmaniasis) and urban mosquito habitats (e.g., dengue fever, filariasis, LaCrosse encephalitis). There are or will be many new sensors that have higher spectral resolution, enabling scientists to acquire more information about parameters such as soil moisture, soil type, better vegetation discrimination, and ocean color, to name a few. Although soil moisture content is now detectable using Landsat, the new thermal, shortwave infrared, and radar sensors will be able to provide this information at a variety of scales not achievable using Landsat. Soil moisture could become a key component in transmission risk models for Lyme disease (tick survival), helminthiases (worm habitat), malaria (vector-breeding habitat), and schistosomiasis (snail habitat).

Beck, Louisa R.↗
NASA NTRS2015

Heat-Related Mortality Projections for Cardiovascular and Respiratory Disease Under the Changing Climate in Beijing, China

Because heat-related health effects tend to become more serious at higher temperatures, there is an urgent need to determine the mortality projection of specific heat-sensitive diseases to provide more detailed information regarding the variation of the sensitivity of such diseases. In this study, the specific mortality of cardiovascular and respiratory disease in Beijing was initially projected under five different global-scale General Circulation Models (GCMs) and two Representative Concentration Pathways scenarios (RCPs) in the 2020s, 2050s, and 2080s compared to the 1980s. Multi-model ensembles indicated cardiovascular mortality could increase by an average percentage of 18.4 percent, 47.8 percent, and 69.0 percent in the 2020s, 2050s, and 2080s under RCP 4.5, respectively, and by 16.6 percent, 73.8 percent and 134 percent in different decades respectively, under RCP 8.5 compared to the baseline range. The same increasing pattern was also observed in respiratory mortality. The heat-related deaths under the RCP 8.5 scenario were found to reach a higher number and to increase more rapidly during the 21st century compared to the RCP4.5 scenario, especially in the 2050s and the 2080s. The projection results show potential trends in cause-specific mortality in the context of climate change, and provide support for public health interventions tailored to specific climate-related future health risks.

health↗
NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture with Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Assist with the evaluation and measuring of wetlands hydroperiod at the Plum Brook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: (1) Show the relative length of hydroperiod using available remote sensing datasets, (2) Date linked table of wetlands extent over time for all feasible non-forested wetlands, (3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables (4), A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment; and (5) A MTRI style report summarizing year 2 results.

Brooks, Colin↗
NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Brooks, Colin↗
NASA NTRS2019

Taking Climate Model Evaluation to the Next Level

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

Eyring, Veronika↗
NASA NTRS2024

Vermont Wildland Fires: Investigating the Role of Antecedent Conditions and Recent Environmental Trends in Exacerbating Fire Risk and Potential in Vermont

Under a changing climate, increases in dry conditions and extreme heat events are projected to exacerbate wildfire risk in the northeastern U.S. In recent years, Vermont has observed higher annual temperatures, more frequent heatwaves, increased annual precipitation, extreme flood events, and decreased snowfall. The mechanisms through which environmental factors contribute to increased fire risk in humid environments, such as Vermont, are poorly understood. The team partnered with the National Weather Service, the Vermont Division of Forests, and the University of Vermont to investigate phenological trends and antecedent conditions influencing wildland fire risk. For the phenological analysis, from 2001 to 2023, vegetation data, phenological dates, and snow water equivalent (SWE) values were accessed from Landsat 5Thematic Mapper (TM), Landsat 7Enhanced Thematic Mapper Plus (ETM+), Landsat 8Operational Land Imager (OLI), Landsat 9OLI-2, the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Snow Data Assimilation System (SNODAS), respectively. For the antecedent condition analysis, from 2008 to 2023, soil moisture, Environmental Stress Index, wind speed, minimum relative humidity, and daily precipitation data were obtained from the Global Land Data Assimilation System (GLDAS), SERVIR, gridMET Wind, gridMET Humidity, and NClimGrid, respectively. The study found that green-up dates over the study period remain relatively stable, while snowmelt dates appear increasingly variable. Minimum relative humidity was the most significant environmental variable correlated with wildfire risk in Vermont. Results from this study will inform the National Weather Service's preparation of fire forecasts before prescribed burns and support community outreach by the Vermont Agency of Natural Resources.

Wildland Fire↗
NASA NTRS2022

Sea Level Rise and City-Level Climate Action

Background: Climate change is the greatest threat to global health in the 21st century. Rising sea levels are one particularly concerning manifestation of this and many of the world’s largest cities are vulnerable to sea level rise (SLR). Thus, urban climate adaptation and mitigation policies are increasingly important to protect population health. Objectives: This study aimed to determine whether being at risk of SLR was associated with city-level climate action. It also aimed to assess the wider drivers of climate action in cities, in order to guide ongoing efforts to motivate climate action, assess public health preparedness and identify research gaps. Methods: This is an ecological cross-sectional study using secondary data from CDP, the Urban Climate Change Research Network (UCCRN), World Bank, United Nations Cities and EM-DAT (Emergency Events Database). The study population consisted of 517 cities who participated in CDP’s 2019 Cities Survey. Multivariable logistic regression was utilized to assess the relationship between risk of SLR and city-level climate action, and secondly, to assess the wider determinants of city-level climate action. Results: There was evidence of crude associations between risk of SLR and three outcome variables representing city-level climate action. However, after adjusting for confounding variables, these crude associations disappeared. World region, national income status and urban population were shown to be stronger predictors of city-level climate action. Conclusion: It is concerning for population health that there is no association demonstrated between risk of SLR and climate action. This could indicate a lack of awareness of the risks posed by SLR within urban governance. To fulfil their health protection responsibilities, it is essential that public health professionals take a leading role in advocating for climate action.

sea level rise↗
NASA NTRS2023

Risk Assessment for Asteroid Impact Threat Scenarios

Asteroid impacts can cause a wide range of damage through multiple potential hazards, from localized blast waves or thermal radiation, to tsunami inundation, to global climatic effects. The level of risk posed by these hazards depends not only upon their extent and severity, but also upon the likelihood of the various damage ranges. Some consequences may be more moderate but very likely, while others may be unlikely but catastrophic. Evaluating the risk from these hazards involves substantial uncertainties across all aspects of the problem, including the properties of the asteroid itself, the specifics of its entry, and the complex high-energy damage physics involved. NASA’s Asteroid Threat Assessment Project performs Probabilistic Asteroid Impact Risk (PAIR) assessments that use fast-running entry and hazard models to evaluate millions of impact cases representing the distributions of these many uncertain parameters. This paper presents current probabilistic asteroid impact risk assessment modeling tools and approaches used for evaluating specific asteroid impact threat cases. We give an overview of the current PAIR model used to support impact threat scenarios and discuss some of the key applications of these assessment in supporting response decisions and planetary defense preparedness. We then present the results and key findings from the recent 2023 PDC hypothetical impact exercise as an example of the primary types of risk results and metrics being developed to inform and support those mitigation and response decisions.

SMD↗
NASA NTRS2004

Impact of Urban Surfaces on Precipitation Processes

The Intergovernmental Panel on Climate Change (IPCC) was established in 1988 by two United Nations organizations, the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) to assess the "risk of human-induced climate change". Such reports are used by decision-makers around the world to assess how our climate is changing. Its reports are widely respected and cited and have been highly influential in forming national and international responses to climate change. The Fourth Assessment report includes a section on the effects of surface processes on climate. This sub-chapter provides an overview of recent developments related to the impact of cities on rainfall. It highlights the possible mechanisms that buildings, urban heat islands, urban aerosols or pollution, and other human factors in cities that can affect rainfall.

Shepherd, J. M.↗
NASA NTRS2005

Applications of Principled Search Methods in Climate Influences and Mechanisms

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

Glymour, Clark↗
NASA NTRS2022

Climate Change-Induced Peatland Drying in Southeast Asia

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.

VIIRS↗
NASA NTRS2009

The First Prediction of a Rift Valley Fever Outbreak

El Nino/Southern Oscillation (ENSO) related anomalies were analyzed using a combination of satellite measurements of elevated sea surface temperatures, and subsequent elevated rainfall and satellite derived normalized difference vegetation index data. A Rift Valley fever risk mapping model using these climate data predicted areas where outbreaks of Rift Valley fever in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. This is the first prospective prediction of a Rift Valley fever outbreak.

Anyamba, Assaf↗
NASA NTRS2024

Downscaled and Bias-Adjusted Climate Projections for Afghanistan

Climate change is already having devastating impacts on human communities and natural landscapes, from increasing severity and frequency of natural disasters to drought and desertification that degrade both natural ecosystems and human livelihoods. However, the impacts of climate change are not evenly distributed across the world—some places are at far greater risk than others. These highly vulnerable places should be a higher priority for investments and interventions to help people and nature adapt to changing climatic conditions. Afghanistan is one of the most at-risk countries from climate change. Within the country, the river basins are particularly sensitive as climate change will dramatically alter their water regimes upon which ecosystems and a vast proportion of the Afghan population and millions of people in neighbouring countries rely. As part of an EU-funded project implemented by the Wildlife Conservation Society (WCS), WCS conducted a vulnerability assessment for the Amu Darya River Basin that drains c. 14% of Afghanistan’s land area and is critically important for supporting both people and sensitive biodiversity (Elsen et al., 2023). Central to the vulnerability assessment was the development of downscaled and bias-adjusted climate projections, produced by the Center for Climate Systems Research (CCSR)1. The climate projections were produced at a much higher resolution (1 km2) than were otherwise available and were tailored to the local environment.

climate projections↗
NASA NTRS2011

Future Opportunities and Challenges in Remote Sensing of Drought

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Wardlow, Brian D.↗
NASA NTRS2011

Climate Teleconnections and Recent Patterns of Human and Animal Disease Outbreaks

Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Extremes in rainfall (drought and flood) during the period 2004 - 2009 have privileged different disease vectors. Chikungunya outbreaks occurred during the severe drought from late 2004 to 2006 over coastal East Africa and the western Indian Ocean islands and in the later years India and Southeast Asia. The chikungunya pandemic was caused by a Central/East African genotype that appears to have been precipitated and then enhanced by global-scale and regional climate conditions in these regions. Outbreaks of Rift Valley fever occurred following excessive rainfall period from late 2006 to late 2007 in East Africa and Sudan, and then in 2008 - 2009 in Southern Africa. The shift in the outbreak patterns of Rift Valley fever from East Africa to Southern Africa followed a transition of the El Nino/Southern Oscillation (ENSO) phenomena from the warm El Nino phase (2006-2007) to the cold La Nina phase (2007-2009) and associated patterns of variability in the greater Indian Ocean basin that result in the displacement of the centres of above normal rainfall from Eastern to Southern Africa. Understanding the background patterns of climate variability both at global and regional scale and their impacts on ecological drivers of vector borne-diseases is critical in long-range planning of appropriate response and mitigation measures.

Anyamba, Assaf↗