The Climate Hazards Center performs a variety of activities, from field work to remote and in-person trainings on CHC data products, and more.
CHC Early Career Webinar Series
The Climate Hazards Center’s Early Career Webinar Series seeks to provide opportunities to early career—PhD candidates or postdoctoral—researchers to present their work through the CHC. We aim to place an emphasis on diversity (racial, gender, and geographical) by providing this platform, which also affords us the opportunity to network with up-and-coming researchers as potential future collaborators.
The Early Career Webinar Series focuses not only on research methods and results, but also on the societal impacts of the research. This series also focuses on challenges faced during the research process and the ways in which they were overcome. This approach allows for a learning-based methodology that creates a space for discussing inevitable obstacles and pinpointing practices for moving past such challenges.
Uday K. Thapa
Angel S. Fernandez-Bou
March 30th, 2023 at 10:00 AM PST
Dr. Brooks is an Assistant Professor in the School of Public Policy at the University of Connecticut. Her research interests lie at the intersection of environment, health, and development economics. Her current research examines the environmental and health consequences of brick manufacturing in Bangladesh, how food security affects fertility in sub-Saharan Africa, and the impacts of abortion policies on women’s health and economic outcomes. Her research employs a range of disciplinary approaches from economics, demography, data science, geography, and epidemiology.
Dr. Nachiketa Acharya is an expert in statistical and machine learning modeling in climate sciences, especially sub-seasonal to seasonal forecasting. He is a CIRES/University of Colorado Research Scientist III working with the NOAA Earth System Research Laboratories's Physical Sciences Laboratory. Previously, he has held influential positions at the Department of Meteorology and Atmospheric Sciences at the Pennsylvania State University, the International Research Institute for Climate and Society at Columbia University, the Institute for Sustainable Cities at the City University of New York, the National Centre for Medium-Range Weather Forecasting in India, the Indian Institute of Technology Delhi, and Bhubaneswar. He is actively engaged in several Regional Climate Outlook Forum by WMO as an expert and trainer of S2S forecast and verification.
Dr. Sadegh presented on the findings of human exposure to wildfires and its occurrence on the backbone of existing social vulnerability.
An increasing number of wildfire disasters occurred in recent years in the U.S. Here, we demonstrate that cumulative primary human exposure – population residing within large wildfires’ perimeters – was 594,850 people from 2000-2019 across the Contiguous U.S. (CONUS), 82% of which occurred in the Western U.S. Primary population exposure increased by 125% in CONUS inthe past two decades, noting large uncertainty ranges. We show that population dynamics from 2000-2019 alone accounted for 24% of the observed increase rate in human exposure, whereas increased wildfire extent drove a majority of the observed trends. Additionally, we document widespread exposure of roads (412,155 km) and transmission power lines (14,835 km) to large wildfires in CONUS, with an increased rate of 58% and 70% from 2000-2019, respectively. We then assess the social vulnerability landscape of human exposure to wildfires in California, Oregon and Washington that accounted for nearly three-quarters of wildfire exposures in CONUS. We show widely different vulnerability distributions for the exposed population to wildfires between these three states, but all three states observed a large rate of increase in the exposure of their most vulnerable population. Certain dimensions of vulnerability of exposed population, such as “age 65 and older” and “disability”, observed a large rate of worsening in the past two decades, but changes in other dimensions, such as “poverty”, are not as notable. We also show major differences in the landscape of social vulnerability for the entire state population versus those exposed to wildfires, especially in California and Washington, pointing to inequality of wildfire exposure. Our findings highlight the benefits of equitable mitigation and adaptation efforts to help societies cope with wildfires.
Title: Himalayan Jet: Trends and variability over the past millennium
The subtropical jet (STJ) controls the climate of the Himalayan region during winter and
spring by guiding storms generated in the Mediterranean region eastward into the
Himalayas. These storms, commonly known as western disturbances, deliver snow and
rainfall, providing water to more than one billion Asian people for agriculture and power
generation during the dry seasons. The STJ also regulates the meridional movement of
tropical air masses. When the STJ over the Himalayas (Himalayan jet) moves poleward,
the region receives fewer storms, and warm tropical air is able to advect into higher
latitudes, making it both hotter and drier. Therefore, changes in the position of the
Himalayan jet have implications for regional water supply and management. In this
seminar, I will show how the latitudinal position of the Himalayan jet has changed in
recent decades as well as over the past four centuries using observation and tree-ring
reconstruction. I will also share my work on the impacts of natural and anthropogenic
forcings on trends and interannual variability of the jet over the past millennium based
on climate model simulations.
Uday Thapa is a climate change scientist at CoreLogic, a company based in Oakland,
where he is involved in hazard risk assessment projects. Before taking this new
position, Dr. Thapa was a NOAA Climate and Global Change Postdoctoral Fellow at
UCSB Bren School for 2020-2022. Uday holds a MS degree in Environmental Science
from Nepal’s Tribhuvan University and a PhD in Geography from the University of
Minnesota. By training, he is a paleoclimatologist, and his research, while being at
academia, involved applications of tree-ring reconstructions and climate model
simulations to improve our understanding of climate and atmospheric variability over the
Title: Field-scale soil moisture for drought monitoring and agricultural yield prediction at the local scales
Soil moisture is highly variable in space and time, and deficits (i.e., droughts) play an important role in modulating crop yields. Limited hydroclimate and yield data, however, hamper drought impact monitoring and assessment at the farm field scale. This work demonstrates the potential of using field-scale soil moisture simulations to support high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field scale. We present a multiscale modeling approach that combines HydroBlocks – a physically based hyper-resolution land surface model (LSM) – with machine learning. We used HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3 h 30 m resolution. These simulations, along with remotely sensed vegetation indices, meteorological data, and descriptors of the physical landscape (related to topography, land cover, and soils) were combined with district-level maize data to train a random forest model to predict maize yields at district and field scales (250-m). Our model predicted yields with an average testing coefficient of determination (R 2 ) of 0.57 and mean absolute error (MAE) of 310 kg ha -1 using year-based cross-validation. Our predicted maize losses due to the 2015–2016 El Niño drought agreed well with losses reported by the Food and Agriculture Organization (FAO). Our results reveal that soil moisture is the strongest
and most reliable predictor of maize yield, driving its spatial and temporal variability. Soil moisture was also a more effective indicator of drought impacts on crops than precipitation, soil and air temperatures, and remotely sensed normalized difference vegetation index (NDVI)-based drought indices. This study demonstrates how field-scale modeling can help bridge the spatial-scale gap between drought monitoring and agricultural impacts.
Title: Introducing SPIRALL — an agent-based model for simulating prosperity in rural and land-based livelihood communities in Kenya
We have developed SPRIALL, an agent-based model written in NetLogo. The purpose of SPIRALL is to simulate, over large regional and temporal scales, and in a spatio-temporally explicit manner, the reciprocal interactions between Kenyan pastoral households and the environment. SPIRALL encompasses monthly household decisions related to accessing ecosystem services to sustain livestock herds and managing these herds to meet household calorie and cash requirements. The availability of ecosystem services and the cumulative impacts of household decisions on these services is simulated via linking SPIRALL to L-Range - a model that simulates ecosystem processes within rangelands. The coupled model tracks livestock populations, rates of food insecurity and seasonal pastoral movements at national, sub-national, monthly and annual scales, as well as ecosystem-wide responses triggered by livestock grazing. We will outline key features of SPIRALL. We will also share results from simulations that explore the impacts of diverse scenarios of rangeland fragmentation and loss on pastoral food security and poverty. We will demonstrate that SPIRALL can be a useful tool for exploring how policies
may drive synergies and trade-offs among multiple Sustainable Development Goals relevant to rural Kenya.
Title: Understanding U 3 : Underrepresented, Understudied, and Underserved Frontline Communities at the Onset of Climate Change
A common approach in scientific research and policy is a commitment to develop projects or legislation trying to improve problems experienced by rural communities; however, lack of interaction with community members during the process tends to produce unsatisfactory results. We visited disadvantaged communities in the San Joaquin Valley of California and interviewed local stakeholders (community members and leaders, policy advocates, attorneys, and educators). Then we analyzed a corpus related to disadvantaged communities from a pool of California-related publications containing 154,000 scientific papers, 2.6 million newspaper articles, and 11,000 state legislation bills from 2017 to 2020 to estimate the frequency and quality of disadvantaged community representation. Here we present our findings describing the biases and gaps of knowledge by scientific papers, California newspaper articles, and legislation bills with respect to disadvantaged communities in California, and we suggest opportunities for scientists, media communicators, and policymakers to amplify the voices of these stakeholders. In all corpus categories, disadvantaged communities are underrepresented: about one in four Californians live in disadvantaged communities, but only one in 2000 news articles and scientific papers cover them. The concerns and priorities of disadvantaged communities do not match the public perspective of them depicted by the corpus. We identify 3 challenges, 3 errors, and 3 solutions regarding frontline communities and climate change. Challenges: (C1) Insufficient understanding of local priorities, (C2) Unequal access to natural resources, and (C3) Unequal access to public services. Errors: (E1) Ignoring local knowledge, (E2) Superiority complex and paternalism, and (E3) System abuse and tokenism. Solutions: (S1) Information exchange and expansion of community-based participatory research, (S2) Buffer zones to enhance environmental protection, green areas, air quality, and water security, and (S3) Multi-benefit projects to create socioeconomic and environmental opportunities inside disadvantaged communities with positive externalities to other stakeholders and to promote public services improvement. The path forward must be grounded in collaboration with frontline community members and practitioners trained in working with vulnerable stakeholders. Addressing co-occurring inequities exacerbated by climate change requires transdisciplinary efforts to identify technical, policy, and engineering solutions.
Title: Learning from Errors: An Application of Machine Learning to Predict Maize Prices and Understand Market Integration in Malawi
Increased market integration is vitally important to ensuring food security in developing countries. By allowing food to move quickly from surplus to deficit areas, integrated markets can reduce the adverse impacts of local shocks and reduce price variability faced by both consumers and producers. In this paper, we propose a novel method which utilizes machine learning algorithms to incorporate market integration in week-ahead retail maize price forecasts. Our methodology is based on a simple model of price transmission which determines how well past prices in neighboring markets predict current prices. We find that incorporating the prices of neighboring markets increases predictive accuracy on average by 18%. Additionally, the machine learning algorithms perform variable selection which allow us to characterize markets which are most responsible for transmitting prices. Contrary to current theory, we find both rural and urban markets play a key role in explaining price transmission occurring between markets.
Title: Urban Food Security in the Age of Climate Change - Are we flying blind?
Abstract: Urbanization has long been thought to increase economic development and to strengthen food systems, resulting in urban populations being less poor and more food secure than rural populations. Yet evidence of "urbanization without growth" in Africa has debunked the notion that urbanization universally improves economic outcomes. In fact, urban inequality is on the rise worldwide, especially in regions that have had unparalleled urban population growth. In tandem, increasing frequency, duration, and severity of extreme events due to climate change is not only impacting food systems, but also the health and well-being of all vulnerable people, from villages to mega cities, across the planet. Here I will review the recent empirical research on the linkages between urbanization, climate change, and food security. I will argue that despite major knowledge gaps, specifically a dearth of socioeconomic and demographic data key to understanding urban food, the existing evidence suggests that both rapid urbanization and climate change may be producing a powder keg of urban food insecurity. The existing research portends wide-spread urban food security emergencies for which many governments are not prepared. This is especially true in conflict-prone countries. But by prioritizing resources to meet the growing challenges of urban food security in the age of climate change, we may be able to deploy tools and policies to strengthen urban food security and reduce the likelihood of urban famine for much of the planet.
Title: The Benefits of Time: Using Spectral and Climate Temporal Features to Characterize Wetland Variability and Dynamics from Space
In this talk, Dr. Kate Fickas discusses her research in using remote sensing to characterize one of the trickiest ecosystems to monitor from space: wetlands. Unlike forests, or more temporally predictable ecosystems, wetlands naturally vary in their ecohydrology from year to year, making classification and change detection a unique problem to solve. Tracking wetlands using earth observation technologies is of timely importance as both freshwater and saltwater wetlands are disappearing across the globe, eliminating their services such as habitat, storm and drought mitigation, and carbon sequestration. Dr. Fickas will discuss how she used the entire Landsat archive, including the oft-omitted MSS data, to examine trends in Oregon wetlands before and after Federal Clean Water Act wetland policy protections.She’ll then discuss her work that helped to define the Landsat spectral-temporal domain by using Spectral-Temporal Features (STFs), which employs the benefit of time to reduce thousands of spectral data points into singular features, by characterizing inter- and intra-annual wetland dynamics and jointly developing the analog concept to climate data, Climate Temporal Features (CTFs). This work emphasized individual feature importance for distinguishing between different wetland habitats and that different features are more important for different climate regions; one-size does not fit all for wetlands. She’ll touch briefly on her EPA-funded postdoctoral work at University of Massachusetts, Amherst, using STFs with dense temporal stacks of drone-collected multispectral data in salt marsh ecosystems as well as her proposed research in her new position as a USGS Mendenhall Fellow with USGS EROS and the UC Santa Barbara Climate Hazard Center. Last, as the Founder and Co-Director of the outreach group Ladies of Landsat, Dr. Fickas will talk about the group’s mission and initiatives and how anyone can be a Lady of Landsat.
Title: Improving and Employing a Hydrologic Model to Enable WaterResource Planning Under Future Climate and Land Cover Uncertainties
Climate and land use land cover (LULC) change are anticipated to significantly impact water resources in the Colorado River Basin (CRB), and the need for actionable information from hydrologic research is growing rapidly. Since land use is a result of complex socio-ecological factors, accurately predicting future patterns of LULC is challenging. Sizable differences among a large number of climate models also necessitate a screening process for impact studies.Process-based models, such as the Variable Infiltration Capacity (VIC) model, offer the ability to quantitatively predict hydrologic behaviors of these complex human-environmental systems. We integrated advances in VIC usingLandsat- and MODIS- based products to create more realistic land surface conditions. We calibrated and verified VIC predictions over the CRB against Q and other observations. We employed VIC to generate streamflow (Q)projections across the CRB under multiple cases of climate and LULC changes.Meteorological data was from downscaled historical (1950-2005) and future projections (2006-2099) of eight climate models that best represent climatology under low- and high- emission scenarios. We used two modeling methods: (1) a ‘top-down’ approach to assess an “envelope of hydrologic possibility” under the16 climate futures; and (2) a ‘bottom-up’ evaluation of hydrologic outcomes in two climates from the ensemble that represent ‘hot and dry’ and ‘warm and wet’ futures. In this latter assessment, we modified land cover composition using regional projections of a LULC model and applied more drastic disturbances to forest cover types. We consulted water managers to ensure a range of views were captured in the modeled cases and to expand the legitimacy of the research for decision-makers. Results indicated a high confidence across the climate ensemble that the spatial attributes of the Millennium Drought are likely to be reinforced during the 21st century. This included basin-wide mean annual decline under most climate models (by -5 to -41% for any given case by end of century), primarily driven by warming that reduced winter snowfall and spring snowmelt and increased year-round soil evaporation in the northeast. Forest disturbances partially offset warming effects (basin-wide mean annual Q was up to 3% larger than without disturbance), allowing more neutral warming impacts. Water managers are using our results to improve long-range plans, signifying that the improved model and methods can be easily translated for planning in other river basins.