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DOE OSTI2024

Tropical root responses to global changes: A synthesis

Tropical ecosystems face escalating global change. These shifts can disrupt tropical forests' carbon (C) balance and impact root dynamics. Since roots perform essential functions such as resource acquisition and tissue protection, root responses can inform about the strategies and vulnerabilities of ecosystems facing present and future global changes. However, root trait dynamics are poorly understood, especially in tropical ecosystems. We analyzed existing research on tropical root responses to key global change drivers: warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO 2 , and fires. Based on tree species- and community-level literature, we obtained 266 root trait observations from 93 studies across 24 tropical countries. We found differences in the proportion of root responsiveness to global change among different global change drivers but not among root categories. In particular, we observed that tropical root systems responded to warming and eCO 2 by increasing root biomass in species-scale studies. Drought increased the root: shoot ratio with no change in root biomass, indicating a decline in aboveground biomass. Despite N deposition being the most studied global change driver, it had some of the most variable effects on root characteristics, with few predictable responses. Episodic disturbances such as cyclones, fires, and flooding consistently resulted in a change in root trait expressions, with cyclones and fires increasing root production, potentially due to shifts in plant community and nutrient inputs, while flooding changed plant regulatory metabolisms due to low oxygen conditions. The data available to date clearly show that tropical forest root characteristics and dynamics are responding to global change, although in ways that are not always predictable. This synthesis indicates the need for replicated studies across root characteristics at species and community scales under different global change factors.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1979

The LACIE crop years: An assessment of the crop conditions experienced in the three years of LACIE

Meteorological data and LANDSAT spectral data from growing regions in the U.S. Great Plains, the U.S.S.R., and Canada were used to assess growing conditions and to document where anomalies such as drought, floods, and freezes were impacting the crop yield and appearance of spring and winter wheat. In the United States, the weekly rainfall and temperature data were used to estimate soil moisture, which was then related to crop needs by a crop moisture index. The transformation of LANDSAT digital data into a green index number provided a procedure whereby data from a LACIE segment could be classified as drought affected or not. The growing seasons encountered in each LACIE country during the three phases are described.

Hill, J. D.↗

NASA NTRS2017

Mapping Threats to Agriculture in East Africa: Performance of MODIS Derived LST for Frost Identification in Kenya's Tea Plantations

Increased prevalence of weather related hazards in eastern Africa including drought, floods, hail and frost threatening agricultural productivity. Kenya is heavily dependent on agriculture for economic growth (FAO 2013); (1) Agriculture contributed 23.5% and 21.5% of GDP in 2009 and 2010 respectively, (2) Employment to half a million households of smallholders and 150,000 on large tea estates. Tea growing in Kenya depends on stability of the weather; (1) Weather is unpredictable, (2) Frost has contributed 30% of tea leaf losses, (3) Drought has contributed 14-30%, (4) The losses are experienced between January and march - frost and dry season.

remote sensing↗

DOE OSTI2022

Multi-Level Impacts of Extreme Climate-Related Events on Food Supply Chain: Idaho Potato's Case Study

Background: The world is experiencing frequent extreme weather events like droughts, floods, snowstorms, and shifting of seasons due to climate change. Increasing frequencies and severity of these extreme weather events threaten food security because agriculture is extremely vulnerable to climate change. Higher temperature reduces overall yields of the desirable crops but proliferates weeds and harmful pests. Current data shows that every year U.S. farmers spend more than 11 billion USD to fight weeds and this number is expected to rise in the coming years due to climate change. Due to droughts, the water required for irrigation is becoming scarcer. Since many areas are becoming climatically unusable for crop production, the amount of farmland area is shrinking rapidly. In addition, changes in precipitation patterns are causing crop failures and negatively impacting expected yields. In this study, we investigated how different extreme climate events impact different stages of a food supply chain. Approach: As a case study, we chose Idaho’s potato supply chain (SC). Idaho is the top potato producing state in the U.S. and contributes around 30% of the nation’s potato production. Using the agent-based modeling (ABM) approach, we developed a multi-echelon potato supply chain computer simulation model with six types of agents – seed producers, farmers, shippers, processors, retailers, and logistics companies. In addition to the baseline scenario, we developed two additional climate-related disruption events – drought and snowstorm. We investigated how each of these disruption events impacts different stakeholders across the supply chain in terms of revenue and some other key performance metrics such as lead time, food waste, number of delayed orders, and order fulfillment rate. Results: Extreme climate events not only impact the growers but there is also cascading ripple effects downstream of the supply chain. The methodology developed in this study could be applied to other food and agricultural SC to understand the vulnerabilities of the different links. The findings would potentially help to adopt new policies for the well-being of the overall SC.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2008

The NASA Energy and Water Cycle Extreme (NEWSE) Integration Project

Skillful predictions of water and energy cycle extremes (flood and drought) are elusive. To better understand the mechanisms responsible for water and energy extremes, and to make decisive progress in predicting these extremes, the collaborative NASA Energy and Water cycle Extremes (NEWSE) Integration Project, is studying these extremes in the U.S. Southern Great Plains (SGP) during 2006-2007, including their relationships with continental and global scale processes, and assessment of their predictability on multiple space and time scales. It is our hypothesis that an integrative analysis of observed extremes which reflects the current understanding of the role of SST and soil moisture variability influences on atmospheric heating and forcing of planetary waves, incorporating recently available global and regional hydro- meteorological datasets (i.e., precipitation, water vapor, clouds, etc.) in conjunction with advances in data assimilation, can lead to new insights into the factors that lead to persistent drought and flooding. We will show initial results of this project, whose goals are to provide an improved definition, attribution and prediction on sub-seasonal to interannual time scales, improved understanding of the mechanisms of decadal drought and its predictability, including the impacts of SST variability and deep soil moisture variability, and improved monitoring/attributions, with transition to applications; a bridging of the gap between hydrological forecasts and stakeholders (utilization of probabilistic forecasts, education, forecast interpretation for different sectors, assessment of uncertainties for different sectors, etc.).

House, P. R.↗

DOE OSTI2020

Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry

Increasing population and resource-intensive lifestyles are driving enhanced demands for clean water, food, and energy. In parallel, land-use change, climate change, and perturbations—including drought, floods, fires, and early snowmelt—are significantly reshaping interactions within watersheds throughout the world. While watersheds are the Earth's key functional unit for assessing and managing water resources, hydrological processes in watersheds also mediate biogeochemical interactions that support terrestrial life on Earth (Kaushal, Gold, Bernal, & Tank, 2018; National Research Council, 2012). Although society is dependent upon clean water availability, tractable prediction of watershed hydrobiogeochemical behavior, including watershed response to perturbations, remains a challenge. Central to the challenge are complex, multiscale interactions between plants, microorganisms, organic matter, minerals, dissolved constituents, and migrating fluids, which occur within and across bedrock-to-canopy compartments and along extensive lateral gradients of a watershed. Several recent community reports have synthesized formidable challenges associated with watershed science and technology (AGU, 2018; Blöschl et al., 2019). Here, we discuss emerging technologies and collaboration modes that are critical for developing generalizable insights about and predictive understanding of complex watershed hydrobiogeochemical behavior, which are important for underpinning optimized natural resource management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Projected changes in extreme rainfall and temperature events and possible implications for Cameroon's socio-economic sectors

Extreme events like flooding, droughts and heatwave are among the factors causing huge socio-economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country's policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX-CORE) initiative, at ~25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon's extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Cli mate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days' is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature-based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high-mitigated low-emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio-economic consequences are likely to include food insecurity, heat-related illness, population impoverishment, price rises and market instability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. In this work, we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross-validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root-mean-square error of 0.034 m 3 /m 3 . It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Commentary: Our “heatshed” moment is now

We are living through a “heatshed” moment, a critical turning point in which heat must be rapidly addressed to protect health and livelihoods worldwide. The year 2024 was the hottest on record since global records began in 1850, and the past 10 years were the hottest decade in recorded history. Although extreme heat is the top weather-related killer in the United States, affecting livelihoods and economies, cities and nations dedicate more attention and resources to other climate hazards, such as hurricanes, floods, droughts, and wildfires. In this commentary, we argue that heat resilience must be rapidly advanced through heat mitigation, heat management, and heat governance efforts to ensure thriving communities in the face of an overheating world.

Extreme heat↗

DOE OSTI2024

Tropical root traits in response to global changes from 1984 to 2023

This dataset is a compilation of tropical root traits data in response to different global changes in tropical sites, considering 23.50N and S as latitudinal boundaries. The global changes considered are warming, drought, flooding, cyclones, nitrogen addition, CO2 fertilization, and fire. This dataset contains 266 root trait observations from 93 studies across 24 tropical countries. The full citation from where the data was taken from is provided in the dataset, as well as the global change, the ecosystem type, location, coordinates, the root traits measured, and the direction of their response after the global change. Additional information such as the duration of the experiment, the intensity of the global change, the soil layers from where the roots were collected, the root orders, and the type of experiment are also shown. We obtained this dataset by performing a systematic literature review on Web of Science using standardized keywords in English, Spanish, and Portuguese (Yaffar, Lugli et al. in press).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Autonomous reinforcement learning agents for improving predictions and observations of extreme climate events

Primary Focus Area: This proposal addresses focus area 2, “Predictive modeling through the use of AI techniques.” Science Challenge: Extreme climate events associated with severe weather, coastal and inland flooding, droughts, heat waves and wildfires are expected to increase in frequency and severity in the future. Due to the complexity and chaotic behavior of the climate system, accurately predicting and observing extreme climate events requires a tremendous amount of human intervention to run predictive climate simulations and deploy measurement systems. Extreme events often unfold very quickly, leaving little time to iterate on simulations or re-position instruments. Through reinforcement learning, autonomous AI agents can be designed to make real-time decisions to characterize extreme climate events more efficiently through adaptive models and targeted observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Multi-scale Multi-physics Scientific Machine Learning for Water Cycle Extreme Events Identification, Labelling, Representation, and Characterization

Impacts of climate are usually felt through extreme events such as droughts, floods, thunderstorms, windstorms, wildfires, and so on, that are intimately tied to the water cycle. Predicting the frequency and severity of extreme events under climate change remains a significant challenge; meanwhile, the mechanisms and impacts of these extremes are far from well understood. There are several major science challenges: (1) Lack of labelled extreme events data and missing standards in defining extremes; (2) Computational demand of high-resolution ensemble climate modeling; (3) Modeling the multiscale multi-physics hierarchical structure of compound extremes; (4) Lack of understanding of mechanisms of extreme events; (5) Large uncertainty in extreme events impacts on infrastructure; (6) Subjective assessment of weather-related risk from seasonal to multi-decadal time scales and lack of metrics for risk assessment and mitigation control.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Optimizing DOE Opportunities to Research Land–Atmosphere Interactions in the U.S. Southeast (Workshop Report)

The southeastern United States (Southeast), with its complex and varied environments, is an area of tremendous economic, ecological, and societal importance to the country. The region is characterized by heterogeneous landscapes (i.e., geology and soil type) and a long history of human land use coupled with a warm temperature regime and high precipitation. As a result, soil erosion and deposition are pronounced, vegetation recovery is rapid, and human modification is extensive across the region. To better understand land–atmosphere interactions in this important and complex region, research communities supported by the U.S. Department of Energy’s Biological and Environmental Research (BER) program identified the Southeast as a priority region of interest. In fall 2024, the third Atmospheric Radiation Measurement Mobile Facility (AMF3), one of three mobile monitoring facilities designed to collect atmospheric and climate data from undersampled regions around the world, will begin operations in northwestern Alabama’s Bankhead National Forest (BNF). The AMF3-BNF 5-year deployment, from 2024–2029, will monitor the effects of feedbacks among aerosols, clouds, and precipitation on plant physiology and canopy-scale fluxes. It will also focus on scale aggregation to resolve the role of local forcing on larger-scale processes. To enable broader AMF3 involvement by the science community, the BER Environmental System Science (ESS) program organized the Southeast Land– Atmosphere Research Opportunities (SELARO) workshop in August 2023. The purpose was to identify gaps in scientific understanding of terrestrial processes in the Southeast (defined as states bounded by the Gulf of Mexico to the south, the Atlantic Ocean to east, the Mississippi River to the west, and extending through Tennessee and North Carolina to the north) and explore opportunities to use the AMF3-BNF deployment to coordinate and leverage research efforts across the region. Many parts of the Southeast have experienced repeated anthropogenic forcings. Farming, hunting, burning, and settlement of the region by Indigenous Peoples first shaped the distribution of plant communities, which in turn influenced European colonization patterns. Timber harvesting was common during the expansion of European settlements, and production forestry continues today. Agricultural production was extensive and then waned through the 20th century, creating a period of afforestation following agricultural abandonment. Today, many formerly agricultural landscapes are undergoing rapid urbanization and suburbanization. Overlying these patterns of anthropogenic land use are frequent disturbances from hurricanes, tornadoes, wildfires, drought, flooding, ice storms, and the occasional blizzard. An additional characteristic of the Southeast is its overall landscape complexity. Unlike the western United States, where broad expanses may share similar characteristics, Southeast topography, drainage patterns, vegetation, and development patterns vary widely across relatively small spatial scales (<1 km). This is due to the region’s underlying geology and soil development, species biodiversity patterns, and land ownership and use coupled with strong forces of erosion, weathering, and rapid plant growth in the warm, wet climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Editorial: Advances in observations and modeling of snow, forest-snow processes and snow hydrology

Seasonal snowpack plays a pivotal role in the regional water cycle. In Western mountainous regions, where precipitation predominantly occurs as snow during winter and is limited in the summer months when water demand peaks, snowpack serves as a critical water source essential for sustaining water supply, ecosystems, agriculture, and industries. Additionally, snowpack dynamically influences extreme events, such as floods, droughts, and wildfires, through intricate land-atmosphere feedback. Nevertheless, significant knowledge gaps persist within this field due to data limitations and the complexity of the physics involved in modeling snow and snow hydrology (i.e., snow acts as a critical upstream hydrological process). Here, this research topic presents a collection of manuscripts that address some of these important research gaps.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Reactive transport modeling for supporting climate resilience at groundwater contamination sites

Abstract. Climate resilience is an emerging issue at contaminated sites and hazardous waste sites, since projected climate shifts (e.g., increased/decreased precipitation) and extreme events (e.g., flooding, drought) could affect ongoing remediation or closure strategies. In this study, we develop a reactive transport model (Amanzi) for radionuclides (uranium, tritium, and others) and evaluate how different scenarios under climate change will influence the contaminant plume conditions and groundwater well concentrations. We demonstrate our approach using a two-dimensional (2D) reactive transport model for the Savannah River Site F-Area, including mineral reaction and sorption processes. Different recharge scenarios are considered by perturbing the infiltration rate from the base case as well as considering cap-failure and climate projection scenarios. We also evaluate the uranium and nitrate concentration ratios between scenarios and the base case to isolate the sorption effects with changing recharge rates. The modeling results indicate that the competing effects of dilution and remobilization significantly influence pH, thus changing the sorption of uranium. At the maximum concentration on the breakthrough curve, higher aqueous uranium concentration implies that sorption is reduced with lower pH due to remobilization. To better evaluate the climate change impacts in the future, we develop the workflow to include the downscaled CMIP5 (Coupled Model Intercomparison Project) climate projection data in the reactive transport model and evaluate how residual contamination evolves through 2100 under four climate Representative Concentration Pathway (RCP) scenarios. The integration of climate modeling data and hydrogeochemistry models enables us to quantify the climate change impacts, assess which impacts need to be planned for, and therefore assist climate resiliency efforts and help guide site management.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1977

Distress detection, location, and communications using advanced space technology

This paper briefly introduces a concept for low-cost, global, day-night, all-weather disaster warning and assistance. Evolving, advanced space technology with passive radio frequency reflectors in conjunction with an imaging synthetic aperture radar is employed to detect, identify, locate, and provide passive communication with earth users in distress. This concept evolved from a broad NASA research on new global search and rescue techniques. Appropriate airborne radar test results from this research are reviewed and related to potential disaster applications. The analysis indicates the approach has promise for disaster communications relative to floods, droughts, earthquakes, volcanic eruptions, and severe storms.

Sivertson, W. E., Jr.↗

NASA NTRS1977

Distress detection, location, and communications using advanced space technology

Sivertson, W. E., Jr.↗

NASA NTRS1998

A System Concept for the Advanced Post-TRMM Rainfall Profiling Radars

Atmospheric latent heating field is fundamental to all modes of atmospheric circulation and upper mixed layer circulations of the ocean. The key to understanding the atmospheric heating process is understanding how and where precipitation occurs. The principal atmospheric processes which link precipitation to atmospheric circulation include: (1) convective mass fluxes in the form of updrafts and downdrafts; (2) microphysical. nucleation and growth of hydrometeors; and (3) latent heating through dynamical controls on the gravitation-driven vertical mass flux of precipitation. It is well-known that surface and near-surface rainfall are two of the key forcing functions on a number of geophysical parameters at the surface-air interface. Over ocean, rainfall variation contributes to the redistribution of water salinity, sea surface temperature, fresh water supply, and marine biology and eco-system. Over land, rainfall plays a significant role in rainforest ecology and chemistry, land hydrology and surface runoff. Precipitation has also been closely linked to a number of atmospheric anomalies and natural hazards that occur at various time scales, including hurricanes, cyclones, tropical depressions, flash floods, droughts, and most noticeable of all, the El Ninos. From this point of view, the significance of global atmospheric precipitation has gone far beyond the science arena - it has a far-reaching impact on human's socio-economic well-being and sustenance. These and many other science applications require the knowledge of, in a global basis, the vertical rain structures, including vertical motion, rain intensity, differentiation of the precipitating hydrometeors' phase state, and the classification of mesoscale physical structure of the rain systems. The only direct means to obtain such information is the use of a spaceborne profiling radar. It is important to mention that the Tropical Rainfall Measuring Mission (TRMM) have made a great stride forward towards this ultimate goal. The Precipitation Radar (PR) aboard the TRMM satellite is the first ever spaceborne radar dedicated to three-dimensional, global precipitation measurements over the tropics and the subtropics, as well as the detailed synopsis of a wide range of tropical rain storm systems. In only twelve months since launch, the PR, together with other science instruments abroad the satellite have already provided unprecedented insights into the rainfall systems. It is anticipated the a lot more exciting and important rain observations would be made by TRMM throughout its mission duration. While TRMM has provided invaluable data to the user community, it is only the first step towards advancing our knowledge on rain processes and its contributions to climate variability. It is envisioned that a TRMM follow-on mission is needed in such a way to capitalize on the pioneering information provided by TRMM, and its instrument capability must be extended beyond TRMM in such a way to fully address the key science questions from microphysical to climatic time scale. In fact, a number of new and innovative mission concepts have recently put forth for this purpose. Almost all of these new concepts have suggested the utility of a more advanced, high-resolution, Doppler-enabled, vertical profiling radar that can provide multi-parameter observations of precipitation. In this paper, a system concept for a second- gene ration precipitation radar (PR-2) which addresses the above requirements will be described.

Im, Eastwood↗