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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Mapping ecoregions under climate change: a case study from the biological ‘crossroads’ of three continents, Turkey

Context: Besides climate change vulnerability, most ecosystems are under threat from a history of improper land-use and conservation policies, yet there is little existing long-term ecological research infrastructure in Turkey. In regions with no ecological networks across large landscapes, ecoregion concept offers opportunities for characterizing the landscape under changing climate.Objectives: Aim is to develop contemporary and future quantitative ecoregions for Turkey based on climate model outputs, to identify climate change-sensitive areas of biodiversity and conservation significance, and to provide a framework for a comprehensive ecological observatory network design.Methods: Using Multivariate Spatio-Temporal Clustering and climate data contemporary and projected future distributions of Turkey’s ecoregions are delineated at several division levels.Results: Turkey’s contemporary ecoregions generally show a northward shift by the end of this century and the lengthening of the growing season across the country, especially eastward and northward. The increase in growing season length, along with the shift in precipitation seasonality and increasing growing season precipitation, shape future conditions within the climate change-sensitive areas. Apart from transboundary ecological and socioeconomic significance, these potentially vulnerable ecosystems also constitute the majority of Turkey’s biodiversity hotspots.Conclusions: Our study marks the first ‘ecoregionalization’ study for Turkey based on both contemporary and future climate scenarios. For countries like Turkey, where large-scale ecological networks have not been established, using such quantitative methodology for delineation of optimal ecoclimatic regions, and for mapping environments at risk from climate change provides an invaluable perspective for conservation planning strategies, and a framework for a comprehensive ecological observatory network design.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Snowmelt runoff modeling in simulation and forecasting modes with the Martinec-Mango model

The Martinec-Rango snowmelt runoff model was applied to two watersheds in the Rio Grande basin, Colorado-the South Fork Rio Grande, a drainage encompassing 216 sq mi without reservoirs or diversions and the Rio Grande above Del Norte, a drainage encompassing 1,320 sq mi without major reservoirs. The model was successfully applied to both watersheds when run in a simulation mode for the period 1973-79. This period included both high and low runoff seasons. Central to the adaptation of the model to run in a forecast mode was the need to develop a technique to forecast the shape of the snow cover depletion curves between satellite data points. Four separate approaches were investigated-simple linear estimation, multiple regression, parabolic exponential, and type curve. Only the parabolic exponential and type curve methods were run on the South Fork and Rio Grande watersheds for the 1980 runoff season using satellite snow cover updates when available. Although reasonable forecasts were obtained in certain situations, neither method seemed ready for truly operational forecasts, possibly due to a large amount of estimated climatic data for one or two primary base stations during the 1980 season.

Shafer, B.↗
DOE OSTI2025

The System for Classification of Low-Pressure Systems (SyCLoPS): An All-In-One Objective Framework for Large-Scale Data Sets

We propose the first unified objective framework (SyCLoPS) for detecting and classifying all types of low-pressure systems (LPSs) in a given data set. We use the state-of-the-art automated feature tracking software TempestExtremes (TE) to detect and track LPS features globally in ERA5 and compute 16 parameters from commonly found atmospheric variables for classification. A Python classifier is implemented to classify all LPSs at once. The framework assigns 16 different labels (classes) to each LPS data point and designates four different types of high-impact LPS tracks, including tracks of tropical cyclone (TC), monsoonal system, subtropical storm and polar low. The classification process involves disentangling high-altitude and drier LPSs, differentiating tropical and non-tropical LPSs using novel criteria, and optimizing for the detection of the four types of high-impact LPS. A comparison of our labels with those in the International Best Track Archive for Climate Stewardship (IBTrACS) revealed an overall accuracy of 95% in distinguishing between tropical systems, extratropical cyclones, and disturbances. SyCLoPS produces a better TC detection skill compared to the previous algorithms, highlighted by an approximately 6% reduction in the false alarm rate compared to the previous TE algorithm. The vertical cross section composite of the four types of high-impact LPS we detect each shows distinct structural characteristics. Finally, we demonstrate that SyCLoPS is valuable for investigating various aspects of LPSs in climate data, such as the evolution of a single LPS track, patterns of LPS frequencies, and precipitation or wind influence associated with a particular LPS class.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Field‐scale analysis of miscanthus production indicates climate change may increase the opportunity for water quality improvement in a key Iowa watershed

Abstract The Raccoon River Basin is the primary source for drinking water in Iowa's largest city and plays a major role in the Mississippi River Basin's high nutrient exports. Future climate change may have major impacts on the biological, physiological, and agronomic processes imposing a threat to ecosystem services. Efforts to reduce nitrogen (N) loads within this basin have included local litigation and the implementation of the Iowa Nutrient Reduction Strategy, which suggest incorporating bioenergy crops (i.e., miscanthus) within the current corn–soybean landscape to reach a 41% reduction in nitrate loads. This study focuses on simulating N export for historical and future land use scenarios by using an agroecosystem model (Agro‐IBIS) and a hydrology model (THMB) at the 500‐m resolution, similar to the scale of agricultural fields. Model simulations are driven by CMIP5 climate data for historical, mid‐century, and late‐century under the RCP 4.5 and 8.5 warming projections. Using recent crop profit analyses for the state of Iowa, profitability maps were generated and nitrogen leaching thresholds were used to determine where miscanthus should replace corn–soybean area to maximize reductions in N pollution. Our results show that miscanthus inclusion on low profit and high N leaching areas can result in a 4% reduction of N loss under current climate conditions and may reduce N loss by 21%–26% under future climate conditions, implying that water quality has the potential continue to improve under future climate conditions when strategically implemented conservation practices are included in future farm management plans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AI for Extreme Volcanic Climate Forcing and Feedback Forecasting in the 21st century

Our paradigm-shifting framework will apply machine learning and perform physical analysis of contemporary volcanoes to develop sound forecasts of extreme eruptions in the 21stcentury and their abrupt drying and cooling impacts on the warming climate. We will also bridge climate data driven regression models and earth system model output to trace teleconnections that exacerbate regional impacts such as Arctic amplification and western US droughts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Prediction of Seasonal Climate-induced Variations in Global Food Production

Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.

variations↗
NASA NTRSDate not supplied

WIS and WIGOS Metadata as the Foundation for a Sustainable Framework for Global Greenhouse Gas Watch Data Exchange

Metadata (data about data) is a critical component of data discovery, description, evaluation, documentation, and preservation. Developing and propagating metadata standards has been a longstanding area of activity in WMO and beyond. The WIS2 and WIGOS metadata models are being actively developed and maintained by dedicated task teams, established under the WMO Expert Team on Metadata. The metadata representations and vocabularies are governed by well-established processes within WMO. These standards are being used in a number of metadata/data exchange activities (e.g., WMO Information System 2.0 (WIS2), WIGOS (WMDR), Climate Data Management Systems (CMDS), etc.). It should also be noted that the application of the WIS2 and WIGOS standards fully support the WMO Unified Data Policy and open data policy as well as greatly enhance the value of observations by fostering data F.A.I.R.ness. Furthermore, the WMO metadata standards can serve as the foundation for a framework that will facilitate metadata mapping between the existing schemas used in well-established data centres, e.g., WMO WDCGG (World Data Centre for Greenhouse Gases) and NOAA ObsPack (Observation Package Data Products) and to automate metadata exchange between data centres as well as with WMO. These activities will play a central role in integrating measurements sponsored by various member countries and organizations to provide a more comprehensive characterization of the temporal and spatial distribution of the greenhouse gases. At the same time, this metadata exchange can lead to member countries and partner organizations improving their current metadata collection process for data discoverability, interoperability, and (re)usability. This presentation will describe metadata activities in the context of WIS2 and WIGOS and how they apply to GGGW data integration via metadata mapping and exchange.

Gao Chen↗
NASA NTRSDate not supplied

Hailstorm Analyses and Detection Derived from Current and Historical Satellite Data and Convective Environmental Parameters

We seek to demonstrate the extent to which hailstorms can be detected using a combination of geostationary (GEO) visible and infrared metrics of storm intensity and convective environmental parameters from reanalysis. Hailstorm identifications from low-Earth-orbiting (LEO) passive microwave sensors and maximum expected size of hail (MESH) from ground-based radar serve as a proxy for hail events. Data are analyzed for two warm seasons over the contiguous United States (CONUS). A neural network (NN) is trained to predict hailstorm detection dependent on optimal multi-variate weighting of observed and modeled input. The NN results are then applied to a 15-year Meteosat Second Generation climatology over South Africa to depict where hailstorms are most likely to occur. We also explore the impact of GEO imager resolution on our ability to discriminate hailstorms by matching GOES-13 (4 km) and GOES-16 (2 km) storm intensity metrics against hail characteristics using LEO, MESH, and spotter reports over CONUS during 2017, when both satellites were simultaneously imaging. Such analysis allows us to assess the feasibility of assembling a severe storm climate data record back to GOES-8, in the mid-1990’s. Finer spatial resolution of GOES-16 better resolves the updraft characteristics and intensities that are inherently linked to hail formation; however, GOES-13 can be normalized to achieve comparable detection capability. When intelligently combined with model-derived convective environmental parameters, GEO-derived storm intensity metrics enable high-spatial resolution hail risk assessment at hourly intervals throughout the diurnal cycle anywhere around the world and an improved understanding of hailstorms in the climate system.

Kyle F Itterly↗
NASA NTRS2015

Contribution of Anthropogenic Warming to California Drought During 2012-2014

A suite of climate data sets and multiple representations of atmospheric moisture demand are used to calculate many estimates of the self-calibrated Palmer Drought Severity Index, a proxy for near-surface soil moisture, across California from 1901 to 2014 at high spatial resolution. Based on the ensemble of calculations, California drought conditions were record breaking in 2014, but probably not record breaking in 2012-2014, contrary to prior findings. Regionally, the 2012-2014 drought was record breaking in the agriculturally important southern Central Valley and highly populated coastal areas. Contributions of individual climate variables to recent drought are also examined, including the temperature component associated with anthropogenic warming. Precipitation is the primary driver of drought variability but anthropogenic warming is estimated to have accounted for 8-27 percent of the observed drought anomaly in 2012-2014 and 5-18 percent in 2014. Although natural variability dominates, anthropogenic warming has substantially increased the overall likelihood of extreme California droughts.

California↗
DOE OSTI2019

Measured moisture performance of sealed and insulated attics with permeable insulation in california homes

This study performed detailed thermal and moisture measurements in two homes in Fresno and Clovis, California with sealed attics insulated with glass fiber insulation to determine moisture performance in inland California climates. Data were recorded once every minute for more than a year in each home to allow detailed observation of time-varying moisture and thermal conditions. Moisture measurements were taken at multiple locations in each attic and included wood moisture, surface condensation and surface relative humidity. The results showed that there are strong solar-driven diurnal cycles in attic wood and air moisture content, as well as longer-term seasonal and annual variations. Mold growth was obdserved on the North roof deck in one of the two homes even in this sunny dry climate with attics built in compliance with California building codes that specifically address sealed and insulated attics with vapor-permeable insulation. We investigated moisture stratification in the attic and found that, although there is stratification during sunny daytime hours, this was not when wood moisture content, surface humidity or surface condensation are high. Therefore, other moisture transport mechanisms are acting to increase moisture levels near the roof peak. Finally, our measurements also showed that the attic with the highest measured moisture levels did not have any mold growth, while mold was visually observed in the attic whose measurements indicted little or no concern, indicating that the state-of-the-art in moisture measurement and mold growth prediction in building assemblies may be insufficient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Measured Moisture Performance of Sealed and Insulated Attics with Permeable Insulation In California Homes

This study performed detailed thermal and moisture measurements in two homes in Fresno and Clovis, California with sealed attics insulated with glass fiber insulation to determine moisture performance in inland California climates. Data were recorded once every minute for more than a year in each home to allow detailed observation of time-varying moisture and thermal conditions. Moisture measurements were taken at multiple locations in each attic and included wood moisture, surface condensation and surface relative humidity. The results showed that there are strong solar-driven diurnal cycles in attic wood and air moisture content, as well as longer-term seasonal and annual variations. Mold growth was obdserved on the North roof deck in one of the two homes even in this sunny dry climate with attics built in compliance with California building codes that specifically address sealed and insulated attics with vapor-permeable insulation. We investigated moisture stratification in the attic and found that, although there is stratification during sunny daytime hours, this was not when wood moisture content, surface humidity or surface condensation are high. Therefore, other moisture transport mechanisms are acting to increase moisture levels near the roof peak. Finally, our measurements also showed that the attic with the highest measured moisture levels did not have any mold growth, while mold was visually observed in the attic whose measurements indicted little or no concern, indicating that the state-of-the-art in moisture measurement and mold growth prediction in building assemblies may be insufficient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Artificial Soiling Replication of Field Losses on Commercial Photovoltaic Modules

Here, this study demonstrates the capabilities of an indoor artificial soiling approach developed to closely replicate the natural, cyclic soil accumulation processes in the field-dust suspension, deposition, and sedimentation/cementation-for a subtropical climate. In this work, a near-field environment is replicated in an artificial soiling cubic chamber through controlled regulation of humidity, temperature, dust type, and dust concentration, based on site-specific historical climate data. Two different models (MA and MB) of full-size commercial photovoltaic modules from a single manufacturer, installed side by side in the mid-Atlantic United States, were retrieved and subjected to artificial soiling experiments and various characterization measurements, including short-circuit current, colorimetry, reflectance, X-ray fluorescence, laser diffraction, and optical microscopy. Both in the field and in our improved field-representative artificial soiling tests, the MA model experienced roughly twice the soiling loss as the MB model. To closely replicate the field soiling losses for a site-specific climate, it is critical to include: 1) The use of field-collected dust with identical dust chemistry and particle distribution instead of standardized ISO 12103 Arizona Road dusts, 2) the use of only a small amount of field-collected dust inside the chamber during the deposition process (e.g., 0.15 g), and 3) the preconditioning of the surface coating for the partial/full dose of UV stress as experienced in the field during sunlight exposure and the abrasion as experienced in the field during regular module cleaning activities, if/as needed. The field-representative artificial soiling method developed here could potentially be adopted for rank ordering of various antisoiling coatings developed by researchers and industry stakeholders.

14 SOLAR ENERGY↗
NASA NTRS2018

An Initial Validation of Landsat 5 and 7 Derived Surface Water Temperature for U.S. Lakes, Reservoirs, and Estuaries

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34 C in lake pixels (is) greater than180 m from land, 4.89 C at the land-water boundary, and 1.11 C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

Schaeffer, Blake A.↗
DOE OSTI2024

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Trade can buffer climate-induced risks and volatilities in crop supply

Climate change is intensifying the frequency and severity of extreme events, posing challenges to food security. Corn, a staple crop for billions, is particularly vulnerable to heat stress, a primary driver of yield variability. While many studies have examined climate impact on average corn yields, little attention has been given to the climate impact on production volatility. This study investigates the future volatility and risks associated with global corn supply under climate change, evaluating the potential benefits of two key adaptation strategies: irrigation and market integration. A statistical model is employed to estimate corn yield response to heat stress and utilize NEX-GDDP-CMIP6 climate data to project future production volatility and risks of substantial yield losses. Three metrics are introduced to quantify these risks: Sigma (σ), the standard deviation of year-on-year yield change, which reflects overall yield volatility; Rho (ρ), the risk of substantial loss, defined as the probability of yield falling below a critical threshold; and Beta (β), a relative risk coefficient that captures the volatility of a region's corn production compared to the globally integrated market. The analysis reveals a concerning trend of increasing year-on-year yield volatility (σ) across most regions and climate models. This volatility increase is significant for key corn-producing regions like Brazil and the United States. While irrigated corn production exhibits a smaller rise in volatility, suggesting irrigation as a potential buffer against climate change impacts, it is not a sustainable option as it can cause groundwater depletion. On the other hand, global market integration reduces overall volatility and market risks significantly with less sustainability concerns. Furthermore, these findings highlight the importance of a multidimensional approach to adaptation in the food sector. While irrigation can benefit individual farmers, promoting global market integration offers a broader solution for fostering resilience and sustainability across the entire food system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2019

Homogenized Water Vapor Absorption Band Radiances From International Geostationary Satellites

In the past 20+ years, GEO Imagers with infrared 6.5‐μm bands have been observing the Earth's atmosphere, providing useful information of upper tropospheric moisture. Due to the instrumental differences and local viewing angles in GEO satellites, these observations are not consistent for generating climate data records (CDRs). In this study, a methodology has been developed to homogenize the 6.5‐μm radiances from the international GEO satellites, to generate a consistent CDR. Validations with Infrared Atmospheric Sounding Interferometer radiances from Metops for 2015–2017for seven GEO Imager sensors show that the GEO radiances are homogenized well with small standard deviation and biases of the differences (smaller for newer sensors), temporally stable radiometric accuracy, and weak angle dependency (even weaker for sensors with two water vapor bands). The homogenized 20+ years of consistent 6.5‐μm radiance CDR can be used to evaluate reanalysis and climate models, especially the diurnal variation of the model simulation.

Water Vapor↗
NASA NTRS2012

Use of MODIS Snow-Cover Maps for Detecting Snowmelt Trends in North America

Research has shown that the snow season in the Northern Hemisphere has been getting shorter in recent decades, consistent with documented global temperature increases. Specifically, the snow is melting earlier in the spring allowing for a longer growing season and associated land-cover changes. Here we focus on North America. Using the Moderate-Resolution Imaging Radiometer (MODIS) cloud-gap-filled standard snow-cover data product we can detect a trend toward earlier spring snowmelt in the approx 12 years since the MODIS launch. However, not all areas in North America show earlier spring snowmelt over the study period. We show examples of springtime snowmelt over North America, beginning in March 2000 and extending through the winter of 2012 for all of North America, and for various specific areas such as the Wind River Range in Wyoming and in the Catskill Mountains in New York. We also compare our approx 12-year trends with trends derived from the Rutgers Global Snow Lab snow cover climate-data record.

Hall, Dorothy K.↗
NASA NTRS2024

Bridgeport Urban Development: Leveraging NASA Earth Observations and Sociodemographic Data to Assess Urban Heat Vulnerability and Inform Cool Corridors in Bridgeport, Connecticut

Urban environments face hotter temperatures than suburban and rural areas due to higher concentrations of impervious surfaces, heat-retaining buildings, and lack of green space. Bridgeport, Connecticut, which was formerly a national manufacturing hub, is now the densest and most populous city in the state. Bridgeport experiences hotter temperatures, exposing its residents to more extreme temperatures than the surrounding affluent suburbs. Extreme heat affects the health of those exposed to it and intensifies energy demands. Understanding temperature differences is the first step in effectively directing mitigation efforts. Our partner, Groundwork Bridgeport, along with the Yale Urban Design Workshop, are planning a “cool corridors” project, implementing cooling infrastructure to combat urban heat. We used Landsat 8 Thermal Infrared Sensor and Landsat 9 Thermal Infrared Sensor-2 data to conduct a Land Surface Temperature analysis in Google Earth Engine for the county of Fairfield. A Principal Component Analysis was performed to identify indicators of social vulnerability in Bridgeport. We used the SOlar and LongWave Environmental Irradiance Geometry model to identify felt heat on the block level to inform where the partner should locate their cooling interventions to ensure they are most effective and equitable. We focused on the East Side of Bridgeport, which we found was 10 degrees hotter than other areas of Bridgeport and the neighboring town of Fairfield. We integrated our findings using Earth observations and additional sociodemographic and climate data into final communication products for our partners which will facilitate their selection of candidate locations for their Cool Corridors project.

Silas Kirsch↗