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NASA NTRS2023

Interconnected Hydrologic Extreme Drivers and Impacts Depicted By Remote Sensing Data Assimilation

In a changing climate, the likelihood of hydrologic extremes has been increasing as climate change can impact both means and extremes4 of hydrologic cycle processes, potentially resulting in an increased frequency of floods in some regions and decreases in others. In a warming world, the physical processes that affect hydrologic response, such as rain-on snow runoff events, are also changing, such that the seasonality of streamflow has been shifting. The geography of rain-on-snow runoff events is predicted to move from low to high elevations. In addition to floods, there is also potential for an increase in dry extremes in a warming world with increased drought frequency and occurrences in many parts of the world. The increased frequency of drought and heatwave events is expected to have consequences such as escalating crop failures in future projection scenarios1. Thus, the consensus of literature shows that climate change is increasing the magnitude and frequency of extreme hydrologic events, and the human influence in many of these events is substantial.

Timothy M. Lahmers↗

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 NTRS2023

Changing Intensity of Hydroclimatic Extreme Events Revealed by GRACE and GRACE-FO

Distortion of the water cycle, particularly of its extremes (droughts and pluvials), will be among the most conspicuous consequences of climate change. We applied a novel approach with terrestrial water storage observations from the GRACE and GRACE-FO satellites to delineate and characterize 1,056 extreme events during 2002-2021. Dwarfing all other events was an ongoing pluvial that began in 2019 and engulfed central Africa. Total intensity of extreme events was strongly correlated with global mean temperature, more so than with the El Nino Southern Oscillation or other climate indicators, suggesting that continued warming of the planet will cause more frequent, more severe, longer, and/or larger droughts and pluvials. In three regions, including a vast swath extending from southern Europe to southwestern China, the ratio of wet to dry extreme events decreased substantially over the study period, while the opposite was true in two regions, including sub-Saharan Africa from 5°N to 20°N.

Climate change↗

NASA NTRS2023

Flood Impacts on Net Ecosystem Exchange in the Midwestern and Southern United States in 2019

Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year-to-year increase in atmospheric CO(2) through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding is not well understood. To improve upon this knowledge, the impact of the 2019 anomalously wet conditions over the Midwest and Southern US on CO(2) vegetation fluxes is examined in the context of 2017–2018 when such precipitation anomalies were not observed. CO(2) is simulated with NASA's Global Earth Observing System (GEOS) combined with the Low-order Flux Inversion, where fluxes of CO(2) are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power as well as with a bias correction based on insitu observations. Net ecosystem exchange CO(2) tracers are separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO(2) observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15%–25% reduction of annual net carbon uptake in 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non-crop vegetation, annual net carbon uptake is enhanced in 2019 by about 10%–20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

Nikolay Balashov↗

NASA NTRS2022

Using Soil Moisture Information to Better Understand and Predict Wildfire Danger: A Review of Recent Developments and Outstanding Questions

Soil moisture conditions are represented in fire danger rating systems mainly through simple drought indices based on meteorological variables, even though better sources of soil moisture information are increasingly available. This review summarises a growing body of evidence indicating that greater use of in situ, remotely sensed, and modelled soil moisture information in fire danger rating systems could lead to better estimates of dynamic live and dead herbaceous fuel loads, more accurate live and dead fuel moisture predictions, earlier warning of wildfire danger, and better forecasts of wildfire occurrence and size. Potential uses of soil moisture information in existing wildfire danger rating systems include (1) as a supplement or replacement for drought indices, (2) for live and (3) dead fuel moisture modelling, (4) for estimating herbaceous fuel curing, and (5) for estimating fuel loads. We identify key remaining research questions and note the logistical challenge of convincing wildfire professionals of the importance of soil moisture compared with more familiar wildfire danger metrics. While obstacles remain, the path forward is clear. Soil moisture information can and should be used to improve fire danger rating systems and contribute to more effective fire management for the protection of communities and ecosystems worldwide.

fuel properties↗

NASA NTRS2023

Exploring Natural and Social Drivers of Forest Degradation in Post-Soviet Georgia

The Caucasus Mountains harbor high concentrations of endemic species and provide an abundance of ecosystem services yet are significantly understudied compared to other ecosystems in Eurasia. In the country of Georgia, at the heart of the Caucasus region, forest degradation has been the largest land change process over the last thirty years. The prevailing narrative is that legal and illegal cutting of trees for fuelwood is primarily responsible for this process. Yet, since independence from the Soviet Union in 1991, the country has undergone rapid socioeconomic and institutional changes which have not been explored as drivers of forest change. We combine newly available land-cover change estimates, Georgian statistical data, and historical institutional change data to examine socioeconomic drivers of forest degradation. Our analysis controls for concurrent changes in climate that would affect degradation and examines variation at the regional (state) level from 2011 to 2019, as well as at the national level from 1987 to 2019. We find that higher winter temperature and drought are associated with higher degradation at the regional scale, while major institutional changes and drought are associated with higher forest degradation at the national level. Access to natural gas, the major energy alternative to fuelwood, had no significant association with degradation. Our results challenge the narrative that poverty and a lack of alternative energy infrastructure drive forest degradation and suggest that government policies banning household fuelwood cutting, including the new Forest Code of 2020, may not reduce forest degradation. Given these results, improved data on wood harvesting and more research on the commercial drivers of degradation and their links to economic and political reforms is needed to better inform forest policy in the region, especially given ongoing risks from climate change.

Institutions↗

NASA NTRS2023

Modeling Global Indices for Estimating Non-Photosynthetic Vegetation Cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

Index optimization↗

NASA NTRSDate not supplied

Comprehensive Analysis of Thirty Years of Land Change in Georgia: Forest Degradation, Land-Use Patterns, and Drivers

In this talk, we present results from a study, funded by the NASA Land-Cover and Land-Use Change program, of thirty years of environmental change in the country of Georgia. The country, a former Soviet Union Republic, has experienced more political, economic and social change in the last 30 years than most countries. But the environmental implications of these extreme events remain largely unknown – the region is understudied, no national forest inventory has been completed in 20 years, and previous research in the area conducted by the authors were not successful. The latter statement is because of forest degradation. After the collapse of the Soviet Union, much of the forestry infrastructure broke down and people resorted to chopping down individual trees. The result was in many parts of the country a reduction in forest biomass but not to the point of a change in land cover. This type of forest degradation, which was either gradual over decades or abrupt, could not be readily identified using the methods and data at our disposal at the time. In the recent study, we had full access to the Landsat archive in combination with computing power. By using monitoring methods that are based on time series analysis, we were able to capture and identify the gradual processes driven by small scale activities that are so characteristic of the post-Soviet Georgian landscape. We developed an approach (“CCDC-SMA”) that combines time series analysis and spectral mixture analysis running on Google Earth Engine for monitoring abrupt and gradual forest degradation. By using this approach, we found that forest degradation was significantly larger than the area estimate of deforestation; 3,541 ± 556 km2 (11% of the forest area in 1987) compared to 158 ± 98 km2 from 1987 to 2020. The prevailing narrative is that legal and illegal cutting of trees for fuelwood is primarily responsible for this process. Yet, since independence from the Soviet Union in 1991, the country has undergone rapid socioeconomic and institutional changes which have not been explored as drivers of forest change. To further our understanding of the underlying causes, we combined forest disturbance estimates, Georgian statistical data, and historical institutional change data to examine socioeconomic drivers of forest degradation. We found that higher winter temperature and drought were associated with higher degradation at the regional scale, while major institutional changes and drought were associated with higher forest degradation at the national level. Access to natural gas, the major energy alternative to fuelwood, had no significant association with degradation. Our results challenge the narrative that poverty and a lack of alternative energy infrastructure drive forest degradation and suggest that government policies banning household fuelwood cutting, including the new Forest Code of 2020, may not reduce forest degradation. Given these results, improved data on wood harvesting and more research on the commercial drivers of degradation and their links to economic and political reforms is needed to better inform forest policy in the region, especially given ongoing risks from climate change.

Pontus Olofsson↗

NASA NTRS2024

Deducing Land-Atmosphere Coupling Regimes from SMAP Soil Moisture

In recent years, there has been a growing recognition of the significance of Land-Atmosphere (L-A) interactions and feedback mechanisms in understanding and predicting Earth’s water and energy cycles. Soil moisture plays a critical role in mediating the strength of L-A interactions and is important for understanding the complex and governing processes across this interface. This study aims to identify the significance of soil moisture in identifying L-A coupling strength within the Convective Triggering Potential (CTP) and Humidity Index (HI) framework. To address this, a consistent and reliable dataset of atmospheric profiles is created by merging CTP and HI using Triple Collocation (TC) with three reanalysis datasets. The merged CTP and HI product demonstrates enhanced performance globally as compared to the individual datasets when validated with radiosonde and satellite observations. This merged product of CTP and HI is then used to compare the L-A coupling strength based on Soil Moisture Active Passive Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) over two decades (2003-2022) where L-A coupling strength is defined as the persistence probability within the dry and wet coupling regimes. Results indicate that the persistency-based coupling strength is related to the ability of soil moisture to predict future atmospheric humidity and dry vs. wet coupling state. The coupling strength in SMAPL4 is consistently stronger than in SMAPL3 and is likely due to its reliance on a land surface model and reduced susceptibility to random noise. The difference in coupling strength based on the same CTP-HI underscores the importance of soil moisture data in estimating coupling strength within the CTP-HI framework. These findings lay the groundwork for understanding the role of L-A interactions and drought evolution due to soil moisture variations, by providing insight into the quantification of coupling strength and its role in drought monitoring and forecast efforts.

Land-atmosphere coupling↗

NASA NTRSDate not supplied

Urban Expansion and Climate Change: Investigating the Impact of Future Scenarios on Child Health

Cities in the poorest countries of the world are rapidly urbanizing. In fact, some of the fastest growing cities on the planet are found in sub-Saharan Africa. Many people benefit from economic, educational, and health opportunities that exist in cities and, as a result, city-dwellers often face less risk of poverty and disease in the context of climate change than their rural-dwelling counterparts. However, rapid urbanization, resulting from a large and constant influx of migrants from rural areas, can put pressure on existing social, economic, and health systems. For poor cities, they face a challenge to support existing residents while also expanding to support the needs of migrants, many of whom are deeply impoverished. Additionally, in a climate change, linked to heat waves, floods, droughts, and other related events, can add stress to complex urban food, health, hygiene, and housing systems. In this project we investigate urban expansion, climate change, and health in four major African cities – Ouagadougou, Addis Ababa, Nairobi, and Kigali. We consider different scenarios of urban expansion using historic and contemporary maps of urban extent, combined with climate (temperature, rainfall, and vegetation) and land cover combined with spatially referenced health information from the Demographic and Health Surveys (DHS) to investigate the relationships between urban dwelling, climate change and health. Temperature and rainfall scenarios are developed under different urban land use futures (expanding agricultural areas versus reducing agricultural areas) to examine the ways that individual health outcomes related to malnutrition vary under different potential future conditions. Preliminary results of the research highlight the importance of temperature for health, in particular, and suggest that while urban conditions related to urban infrastructure (e.g., educational attainment, electricity access, and improved hygiene) cannot reduce health risks to counter the impacts of high temperatures and reduced agricultural land. As cities in poor countries urbanize, city-dwellers face unique risks when droughts, heat waves, and other extreme weather events occur. In this project we explore urban land use conditions, climate conditions and health using future scenarios. Specifically we evaluate health outcomes in the future while considering different land use practices, urban expansion, temperature, and rainfall conditions to identify individual-level risk and protective factors. We compare results across four major cities in sub Saharan Africa.

Public Health↗

NASA NTRSDate not supplied

A Knowledge Graph Framework for Organizing Heterogeneous Datasets for Utilization in Classical and Quantum Computing: Current Challenges and Future Directions

"The escalating impact of climate change induced extreme weather events in urban, suburban, and rural environments demands a rethink of how we have been using the single event-based or use-case-based knowledge graph models. The lack of representation in interaction within environmental variables found in literature led to the development of a novel framework that reflects the true nature of the interconnectedness in our environment. We propose an Environmental Interaction Knowledge Graph (EIKG) framework. This general EIKG framework works as the basis for interconnected environmental events by knitting interrelated events such as hurricanes leading to storm surges, which lead to flood events that could cause mudslides, landslides, etc., The cascading nature of one event leading to another related event in the environment requires an adequate understanding of each event using contextual information before conducting any data-driven analytics. This vision paper showcases how the EIKG:floods, EIKG:wildfire EIKG:landslides, etc, can be derived from a base case framework of EIKG as those individual events are interconnected with some common denominator variables. As an example, the precipitation variable is used in the flood case study as well as in the wildfire case study, as excessive precipitation levels lead to floods, and lack of precipitation leads to droughts and wildfires. We identify the precipitation variable as a “common-denominator-variable” in extreme weather events that play a key role in modeling the environment leading to different extreme weather events based on the variability of that variable (varying values where low precipitation leads to drought, and high values lead to floods). We use the insights gained from EIKG to conduct classical and Quantum Machine Learning (QML) based data analysis on the research questions developed. Our preliminary study shows how the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC) are used along with the classical machine learning models to compare the model accuracies. Our study elaborates on how a quantitative analysis uses state-of-the-art machine learning techniques that include implementing both classical and quantum machine learning models and developing the knowledge graph. The EIKG is used to organize heterogeneous datasets and integrate the relations to case-specific extreme weather events such as floods. The study uses datasets such as county-to-country residential mobility data, socioeconomic datasets from the US Census Bureau, climate and weather-related Earth Observational data from NASA, and critical infrastructure data from the Homeland Infrastructure datasets."

Knowledge Graphs, Quantum Computing, Heterogenous ↗

NASA NTRS2026

Adaptation and Response in Drylands (ARID): A Dryland Research Agenda and Campaign Strategy

Dryland ecosystems cover 41% of Earth’s land surfaces, account for 44% of cultivated lands and 60% of food sources, and make large contributions to the global water and carbon cycles. However, these ecosystems are experiencing unprecedented extremes including heatwaves, floods, and droughts as well as hotter temperatures and often declining water resource availability. These ecosystems are some of the most challenging to monitor given their high temporal variability with rapid response to their environment as well as vast spatial variability with intermixing of different plant species and life forms amongst bare soil coverage. The Adaptation and Response in Drylands (ARID) campaign was selected by National Aeronautics and Space Administration (NASA) as a scoping study to develop a research agenda for a dryland field campaign. Here, we detail our ARID science research agenda and implementation plan that were developed based on an extensive community engagement effort in over 160 events with over a thousand scientists, land managers, and Tribal communities between 2023 and 2024. The selected science themes cover drought and climate variability, ecosystem structure, function, and biodiversity, carbon cycle interannual variability and trends, and social ecological systems (land management and adaptation). We then detail our remote sensing, modeling, and field-based strategies to capture high temporal and high spatial resolution processes. Finally, our implementation strategy is presented which includes focus area selections in a core intensive western U.S. domain and distributed international domains. This strategy includes our overarching guiding principles of using multi-temporal airborne acquisitions and super sites as well as enhancing land management in co-development with end-user partners. While originally developed for NASA, our ARID report creates a blueprint for any future dryland field campaign, at any scale, that can be implemented widely for foundational and applied science objectives.

drylands↗

DOE OSTI2015

Material and methods to increase plant growth and yield

The present invention relates to materials and methods for modulating growth rates, yield, and/or resistance to drought conditions in plants. In one embodiment, a method of the invention comprises increasing expression of an hc1 gene (or a homolog thereof that provides for substantially the same activity), or increasing expression or activity of the protein encoded by an hc1 gene thereof, in a plant, wherein expression of the hc1 gene or expression or activity of the protein encoded by an hc1 gene results in increased growth rate, yield, and/or drought resistance in the plant.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Using the Model Perennial Grass Brachypodium sylvaticum to Engineer Resistance to Multiple Abiotic Stresses

We are using the perennial model grass Brachypodium sylvaticum to identify combinations of transgenes that enhance tolerance to multiple, simultaneous abiotic stresses. The most successful transgene combinations will ultimately be used to create improved switchgrass (Panicum virgatum L.) cultivars. To further develop B. sylvaticum as a perennial model grass, and facilitate our planned transcriptional profiling, we are sequencing and annotating the genome. We have generated ~40x genome coverage using PacBio sequencing of the largest possible size selected libraries (18, 22, 25 kb). Our initial assembly using only long-read sequence contained 320 Mb of sequence with an N50 contig length of 315 kb and an N95 contig length of 40 kb. This assembly consists of 2,430 contigs, the largest of which was 1.6 Mb. The estimated genome size based on c-values is 340 Mb indicating that about 20 Mb of presumably repetitive DNA remains yet unassembled. Significantly, this assembly is far superior to an assembly created from paired-end short-read sequence, ~100x genome coverage. The short-read-only assembly contained only 226 Mb of sequence in 19k contigs. To aid the assembly of the scaffolds into chromosome-scale assemblies we produced an F2 mapping population and have genotyped 480 individuals using a genotype by sequence approach. One of the reasons for using B. sylvaticum as a model system is to determine if the transgenes adversely affect perenniality and winter hardiness. Toward this goal, we examined the freezing tolerance of wild type B. sylvaticum lines to determine the optimal conditions for testing the freezing tolerance of the transgenics. A survey of seven accessions noted significant natural variation in freezing tolerance. Seedling or adult Ain-1 plants, the line used for transformation, survived an 8 hour challenge down to -6 oC and 50% survived a challenge down to -9 oC. Thus, we will be able to easily determine if the transgenes compromise freezing tolerance. In the effort to develop biotechnological tools for perennial grass improvement, we have completed the transformation of B. sylvaticum with constructs containing 20 genes shown to be associated with enhanced abiotic stress tolerance in monocots. In addition, we have transformed plants with constructs containing a combination of genes (i.e. SARK::IPT- Ubi::HSR1::Ubi::NHX1) in order to simultaneously overexpress genes associated with drought + heat tolerance + salt tolerance. We generated single copy insert T1 lines for all constructs and the generation and bulking of homozygous T2 lines is well underway. In addition to our B. sylvaticum transgenics, we transformed B. distachyon with many of the same genes. Some of the transgenic B. distachyon plants subjected to a combined stress of both drought and salinity were able to produced higher yields than wild type plants. Our results indicate a great potential for the development of grasses with improved performance and yield in water-limited areas.

Gordon, Sean↗

DOE OSTI2015

Climate change and the future of natural disturbances in the central hardwood region

The spatial patterns and ecological processes of the southeastern upland hardwood forests have evolved to reflect past climatic conditions and natural disturbance regimes. Changes in climate can lead to disturbances that exceed their natural range of variation, and the impacts of these changes will depend on the vulnerability or resiliency of these ecosystems. Global Circulation Models generally project annual increases in temperature across the southeastern United States over the coming decades, but changes in precipitation are less consistent. Even more unclear is how climate change might affect future trends in the severity and frequency of natural disturbances, such as severe storms, fires, droughts, floods, and insect outbreaks. Here, we use a time-series satellite data record to map the spatial pattern and severity of broad classes of natural disturbances the southeast region. The data derived from this map allow analysis of regional-scale trends in natural and anthropogenic disturbances in the region over the last three decades. Throughout the region, between 5% and 25% of forest land is affected by some sort of disturbance each year since 1985. The time series reveals periodic droughts that themselves are widespread and of low severity but are associated with more localized, high-severity disturbances such as fire and insect outbreaks. The map also reveals extensive anthropogenic disturbance across the region in the form of forest conversion related to resource extraction and urban and residential development. We discuss how changes in climate and disturbance regimes might affect southeastern forests in the future via altering the exposure, sensitivity and adaptive capacity of these ecosystems. Changes in climate are highly likely to expose southeastern forests to more frequent and severe disturbances, but ultimately how vulnerable or resilient southeastern forests are to these changes will depend on their sensitivity and capacity to adapt to these novel conditions.

Dale, Virginia H↗

DOE OSTI2017

Materials and methods to increase plant growth and yield

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

To What Extent Will Decarbonization Deepen the Conversation Between Industry and the Grid?

Decarbonization - the transition away from un-mitigated fossil fuel combustion throughout the economy - requires big changes from both power and process systems. On the power system side, those changes are expected to include large increases in variable generation, e.g., from wind and solar, which has near-zero marginal costs and at large shares can produce infrequent but consequential energy droughts. On the process systems side, industries are investigating their options for direct and indirect electrification, the latter exemplified by replacing fossil fuel inputs with zero-carbon, energy-carrying chemicals like hydrogen and ammonia produced via electrochemical processes. The economic features of these changes within the larger context of power and process systems suggest that their realization could be accompanied by a paradigm shift in how industrial facilities interact with the grid. For example, the dominant type of demand participation in power markets could change from today's focus on load reductions at peak times to a new focus on shifting electricity use, enabled in part by large-scale product storage, to take advantage of renewable energy that would otherwise be curtailed and to avoid consumption during high-price energy droughts. This talk will describe these and other possible design and operational approaches from grid and industrial economic perspectives, culminating in an enumeration of open problems that lie at the interface of today and tomorrow's power and process systems.

co-design↗

DOE OSTI2015

The North American winter 'dipole' and extremes activity: A CMIP5 assessment

The 2013-2014 winter in North American brought intense drought in the West and severe cold in the East. The circulation anomalies were characterized as a dipole: an amplified upper-level ridge over the West Coast and a deepened trough over the central-eastern United States. A previous study using a single model has linked the dipole to the El Nino precursor and found that this link has strengthened in recent years. Here, 17 models from the Coupled Model Intercomparison Project Phase 5 are used to examine the dipole activity. As a result, most models capture the dipole and its association with El Nino precursor and project this association to strengthen.

54 ENVIRONMENTAL SCIENCES↗