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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vapor kinetic energy for the detection and understanding of atmospheric rivers

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and moving eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along densely populated coastal regions. Many recent studies about ARs focused on the statistical analyses of ARs, but a process-level understanding of ARs remains elusive. Here we show that ARs are streams of air with enhanced vapor kinetic energy (VKE) and derive a governing equation for Integrated VKE to understand what contributes to the evolution of ARs. We find that ARs grow mainly because of potential energy conversion to kinetic energy, decay largely owing to condensation and turbulence, and the eastward movement is primarily due to horizontal advection of VKE. Our VKE framework complements the integrated vapor transport framework, which is popular for identifying ARs but lacks a prognostic equation for understanding the physical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping

Schistosomiasis is a neglected tropical disease (NTD) found throughout tropical and subtropical Africa. In Madagascar, the condition is widespread and endemic in 74% of all administrative districts in the country. Despite the significant burden of the disease, high-resolution risk maps have yet to be produced to guide national control programs. This study used an ecological niche modeling (ENM) and precision mapping approach to estimate environmental suitability and disease transmission risk. The results show that suitability for schistosomiasis is widespread and covers 264,781 km 2 (102,232 sq miles). Covariates of significance to the model were the accessibility to cities, distance to water, enhanced vegetation index (EVI), annual mean temperature, land surface temperature (LST), clay content, and annual precipitation. Disease transmission risk is greatest in the central highlands, tropical east coast, arid-southwest, and northwest. An estimated 14.9 million people could be at risk of schistosomiasis; 11.4 million reside in rural areas, while 3.5 million are in urban areas. This study provides valuable insight into the geography of schistosomiasis in Madagascar and its potential risk to human populations. Because of the focal nature of the disease, these maps can inform national surveillance programs while improving understanding of areas in need of medical interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Impacts of Hurricane Disturbance on Water Quality across the Aquatic Continuum of a Blackwater River to Estuary Complex

Hurricanes cause landscape-scale disturbances that affect biogeochemical cycling and water quality in coastal ecosystems. During Hurricane Irma’s passage through northern Florida, water movements driven by wind velocities up to 105 km h-1 caused a salinity peak in an estuary/blackwater river complex. Water quality was monitored across the 15 km site to detect the magnitude and duration of disturbance. Saline water intruded 15 km inland into a freshwater portion of the river that peaked at a salinity of 2 psu. Due to the volume of precipitation from the hurricane, significant runoff of freshwater and dissolved organic matter (DOM) caused a decrease in salinity, dissolved oxygen (DO), and Chlorophyll-a concentrations while increasing turbidity and fluorescent dissolved organic matter (fDOM). The disturbance caused rapid changes observed by in-situ water quality monitors over a 3-week period, but some effects persisted for longer periods as shown by 3-month weekly water sampling. This disturbance caused shifts in DOM loading, altered salinity dynamics, and reshaped landscapes due to wind and wave surge both in upland marsh and downstream estuary. Hurricane disturbance temporarily and abruptly alters the aquatic continuum, and observations of system response can help us understand the mechanisms associated with ecosystem resilience and recovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

New findings and current controversies in the reaction of ruthenium red and ammonium cerium(iv) nitrate: focus on the precipitated compound

Finding the true catalyst has always been a challenging step toward the exploration, understanding, and design of catalytic systems for water-oxidation reaction. Many homogeneous and heterogeneous transition metal complexes have been reported that can undergo a complete transformation to the true catalysts during water-oxidation reaction. Furthermore, the transformation of the previously-explored ruthenium red ([(NH 3 ) 5 RuORu(NH 3 ) 4 ORu(NH 3 ) 5 ]Cl 6 ) catalyst in the presence of ammonium cerium(IV) nitrate is reported. For the first time, it is reported that a heterogeneous nano-sized ruthenium–cerium compound is formed by the electrostatic interaction of [(NH 3 ) 5 RuORu(NH 3 ) 4 ORu(NH 3 ) 5 ], 6+/7+ nitrate ions, and the products of the reduction of ammonium cerium(IV) nitrate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Desert-Urban SysTem IntegratEd AtmospherIc Monsoon (DUSTIEAIM) in the Southwestern United States

Phoenix, Arizona, is the fifth-largest city in the United States with about 1.6 million residents. It is also considered to be the hottest major city in the country, having set a record of 31 consecutive days with temperatures above 110 F (43 C) in summer 2023. Phoenix depends on the Colorado River for over a third of its water supply, but there are questions about how much water will eventually be available to serve the rapidly growing metropolitan area, especially in such extreme heat. Current earth system models struggle to accurately capture precipitation in the Southwestern United States, especially in relation to the summertime North American Monsoon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Waste to worth: A high-temperature water-gas shift magnetite catalyst with encapsulated core-shell structure from coal fly ash

In this paper, we demonstrate the synthesis of magnetite-based, encapsulated catalyst with a core-shell structure from coal fly ash, an industrial solid waste, and its application to industrially important high temperature water-gas shift reaction (HT-WGSR). The catalyst precursor was first synthesized through a leaching-precipitation-hydrothermal process, leading to the formation of a nanosheet-like hematite precursor consisting of Fe 3+ as primary element and minor cations including Al 3+ , Mg 2+ and other cations (e.g. Ca 2+ and Ti 4+ ). During the WGSR, the nanosheets broke into smaller particles with a core of magnetite with a diameter of ~20 nm, whereas the inherent impurity elements were activated and migrated to the outer surface forming a shell with a thickness of ~4 nm. Due to the protection of shell, the as-synthesized catalyst exhibited improved activity, thermal stability and long-term durability for HT-WGSR at 400 °C and 450 °C. Compared to the pure hematite, this catalyst exhibited much higher activity and stability, i.e., increased CO conversion and decreased deactivation rate constant, by a factor of four and three, respectively. Additionally, through a bottom-up reverse synthesis, we have confirmed that Al 3+ functioned as a textual promoter enhancing the interfacial area of the catalyst. On the other hand, the presence of minor impurity elements, in particular 2.58 wt% Mg 2+ and 0.18 wt% Ti 4+ , were found to increase the basicity, and promote the adsorption rate of CO, the redox of catalysts, and the alteration of the reaction mechanism from regenerative to associative with the formation of a strong intermediate formate. Finally, this study not only opens a new direction on the valorization of solid waste into high-value catalysts, promoting the sustainability of solid waste management, but also provides an alternative, cost-effective and scalable approach for the design and fine-tuning of encapsulated catalysts.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Process Modeling of Aerosol‐Cloud Interaction in Summertime Precipitating Shallow Cumulus Over the Western North Atlantic

Abstract Process modeling of Aerosol‐cloud interaction (ACI) is essential to bridging gaps between observational analysis and climate modeling of aerosol effects in the Earth system and eventually reducing climate projection uncertainties. In this study, we examine ACI in summertime precipitating shallow cumuli observed during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). Aerosols and precipitating shallow cumuli were extensively observed with in‐situ and remote‐sensing instruments during two research flight cases on 02 June and 07 June, respectively, during the ACTIVATE summer 2021 deployment phase. We perform observational analysis and large‐eddy simulation (LES) of aerosol effect on precipitating cumulus in these two cases. Given the measured aerosol size distributions and meteorological conditions, LES is able to reproduce the observed cloud properties by aircraft such as liquid water content (LWC), cloud droplet number concentration ( N c ) and effective radius r eff . However, it produces smaller liquid water path (LWP) and larger N c compared to the satellite retrievals. Both 02 and 07 June cases are over warm waters of the Gulf Stream and have a cloud top height over 3 km, but the 07 June case is more polluted and has larger LWC. We find that the N a ‐induced LWP adjustment is dominated by precipitation feedback for the 2 June precipitating case and there is no clear entrainment feedback in both cases. An increase of cloud fraction due to a decrease of aerosol number concentration is also shown in the simulations for the 02 June case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Planning green infrastructure placement based on projected precipitation data

Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. Here, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AmeriFlux US-UR8 Yellow Jacket - UCRB

This is the AmeriFlux version of the carbon flux data for the site US-UR8 Yellow Jacket - UCRB. Site Description - This site is located in Yellow Jacket, Colorado, on a moderate slope. The area is irrigated using a center pivot system, with alfalfa being the dominant vegetation. The region experiences a temperate climate, characterized by cool to mild temperatures throughout the year and substantial annual precipitation, creating favorable conditions for agricultural activities.

Neale, C. U. [Daugherty Water for Food Global Inst↗
DOE OSTI2020

Characterizing Pore-Scale Geochemical Alterations in Eagle Ford and Barnett Shale from Exposure to Hydraulic Fracturing Fluid and CO 2 /H 2 O

As demand increases for an affordable energy source that is tied to an environmental obligation to reduce greenhouse gas emissions and water usage, there is a growing consideration in shale production utilizing processes such as 1) enhancing hydrocarbon recovery via carbon dioxide (CO 2 ) flooding, 2) using CO 2 as a fracturing agent to minimize water use, and 3) storing CO 2 in depleted shale formations to mitigate emissions to the atmosphere. Understanding the geochemical reactions and alterations that occur as shale is exposed to fluids and CO 2 is necessary to develop and optimize each of these processes for field applications. While the majority of shale formations are stimulated using traditional fracturing fluid, some may be fractured using CO 2 or other non-traditional means. We examine the effect fracturing fluid has on shale and how it behaves with secondary exposure to dry CO 2 or CO 2 -saturated water using in situ Fourier Transform infrared spectroscopy (FTIR), feature relocation scanning electron microscopy (SEM), and surface area and pore size analysis using volumetric gas sorption. These techniques were performed on Eagle Ford and Barnett shale samples that were exposed to fracturing fluid and unexposed (as received). Shales that have been exposed to traditional fracturing fluid experienced two reaction fronts. The first reaction front was formed during exposure to the fracturing fluid (pH of ~1.4). A secondary reaction front was formed as a result of CO 2 -saturated fluid exposure in the form of carbonic acid (pH ~5.6). These two different reaction mechanisms drove multiple dissolution and precipitation cycles which altered petrophysical properties of the shale and could lead to a significant impact on flow pathways. FTIR showed that equilibration of carbonate dissolution and precipitation cycles could take as long as 35 days. Samples exposed to fracturing fluid showed significantly less carbonate reactivity compared to those exposed to water. Pore size analysis results indicate exposure to fracturing fluid blocked small nanopores (10-15 nm) reducing BET surface area and total pore volume. SEM results show barite precipitated heavily during exposure to fracturing fluid. It appeared that carbonic acid was able to extract sulfur from organic matter to form gypsum evaporites. The mineralogical (barite precipitation and calcite dissolution/precipitation) and pore-scale alterations observed in these samples may lead to enhancement of flow pathways for injected CO 2 or produced hydrocarbons.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2021

Molecular thermodynamics for scaling prediction: Case of membrane distillation

Membrane distillation (MD) for water treatment is significantly impaired by the scaling of dissolved minerals. The type and content of minerals generally measured as total dissolved solids (TDS) in hypersaline brines not only reduce the MD flux but also control the scaling behavior on the membrane surface. The scaling-induced pore blockage further reduces water flux and eventually leads to membrane wetting. The scaling problem is even more pronounced in the treatment of produced water (PW) as it contains 3–7 times higher TDS concentrations, compared to seawater. Theoretically, the necessary conditions for a salt to precipitate can be traced from its solubility product constant and activity of the constituents within the solution. Therefore, a comprehensive thermodynamic model is necessary to represent the electrolyte behavior and to predict the precipitation of different salts in a complex solution like PW. We pursued electrolyte Nonrandom Two Liquid Theory (eNRTL), a state-of-the-art electrolyte model, to address the PW fluid phase equilibria. With a fully parameterized eNRTL model, we predicted salt precipitations in two different PW samples and compared the results against the experimental findings. Furthermore, here we forecast the precipitation of salts in response to the change in PW concentration and temperature in the MD operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds

The profile of the liquid water content (LWC) in clouds provides fundamental information for understanding the internal structure of clouds, their radiative effects, propensity to precipitate, and degree of entrainment and mixing with the surrounding environment. In principle, differential absorption techniques based on coincident dual-frequency radar reflectivity observations have the potential to provide the LWC profile. Previous differential frequency radar reflectivity (DFR) efforts were challenged by the fact that the measurable differential attenuation for small quantities of LWC is usually comparable to the system measurement error. This typically renders the retrieval impractical, as the uncertainty can become many times greater than the retrieved value itself. Theoretically, this drawback can be mitigated following two interconnected approaches: (1) increasing the frequency separation between the dual-frequency radar system to measure greater differential attenuation and (2) increasing the radar operating frequency to reduce the instrument measurement random error. Our recently developed 239 GHz radar was deployed during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) along with a variety of collocated remote sensing and in situ instruments. We have combined Ka-band (35 GHz) and G-band (239 GHz) observations to retrieve the LWC from more than 100 vertical profiles of shallow clouds with typical amounts of LWC smaller than 1 g m -3 . We theoretically and experimentally demonstrate that the Ka-band and G-band pair of frequencies offers at least a 65 % relative improvement in the LWC retrieval sensitivity compared to previous works reported in the literature using lower-frequency radars. This new technique provides a missing capability to determine the LWC in the challenging low liquid water path (LWP) range (< 200 g m -2 ) and suggests a way forward to characterize microphysical and dynamical processes more precisely in shallow clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Where Are White Roofs More Effective in Cooling the Surface?

Solar reflective roofs (also called white or cool roofs) are a common heat mitigation strategy. By reflecting more sunlight than regular roofs, cool roofs experience lower surface temperatures and produce less heating of the surrounding air. The effectiveness of cool roofs, quantified by the surface temperature difference between cool roofs and regular roofs ??????, is known to vary spatially. A common perception is that ?????? is controlled by solar radiation that reaches the roof surface. Here we use an Earth System Model and a surface energy balance model to show that the spatial variability of ??????, when normalized by the albedo difference between cool roofs and regular roofs (????), is in fact more controlled by an energy distribution factor that encodes the efficiencies of surface energy balance components in dissipating heat. Our results suggest that painting the roof white is more effective in reducing the roof surface temperature when the roof has less water holding capacity and smaller thermal admittance and is located in places with more solar radiation, less precipitation, and lower wind speed.

Wang, Linying↗
DOE OSTI2026

Comparing Synoptic Pattern Evolution for Flash‐Flood‐Producing and Non‐Flash‐Flood‐Producing Mesoscale Convective Systems in the United States

Understanding how the short-term evolution of synoptic weather patterns influence Mesoscale Convective Systems (MCSs) is essential, as these systems are responsible for over half of central U.S. flash floods, leading to substantial socioeconomic and water resource management impacts. This study analyzes long-term MCS data, flash flood reports, and atmospheric reanalyses from 2007 to 2017 using a machine learning clustering algorithm to examine how the synoptic weather patterns evolve prior to MCS initiation. While the clusters reflect seasonal and regional differences in MCS occurrence, they do not consistently distinguish between MCSs that do and do not produce flash floods. Systems in the southern Great Plains are more flood-prone when a synoptic-scale forcing, located near the system, drives strong water vapor transport from the nearby moisture source. More generally under different synoptic weather patterns, a broader precipitating area is the most dominant factor governing MCS flash flood potential.

atmospheric dynamics↗
DOE OSTI2026

Subseasonal Forecasting and MJO Teleconnections in Machine Learning Weather Prediction Models

Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting

Peings, Yannick↗
DOE OSTI2020

Deeper snow increases the net soil organic carbon accrual rate in moist acidic tussock tundra: 210Pb evidence from Arctic Alaska

The net change in the carbon inventory of arctic tundra remains uncertain as global warming leads to shifts in arctic water and carbon cycles. To better understand the response of arctic tundra carbon to changes in winter precipitation amount, we investigated soil depth profiles of carbon concentration and radionuclide activities ( 7 Be, 137 Cs, 210 Pb, and 241 Am) in the active layer of a twenty-two-year winter snow depth manipulation experiment in moist acidic tussock tundra at Toolik Lake, Alaska. Depth correlations of cumulative carbon dry mass (g cm -2 ) vs. unsupported 210 Pb activity (mBq g -1 ) were examined using a modified constant rate of supply (CRS) model. Results were best fit by two-slope CRS models indicating an apparent step temporal increase in the accumulation rate of soil organic carbon. Most of the best-fit model chronologies indicated that the increase in carbon accumulation rate apparently began and persisted after snow fence construction in 1994. The inhomogeneous nature of permafrost soils and their relatively low net carbon accumulation rates make it challenging to establish robust chronologic records. Nonetheless, the data obtained in this study support a decadal-scale increase in net soil organic carbon accumulation rate in the active layer of arctic moist acidic tussock tundra under conditions of increased winter precipitation.

54 ENVIRONMENTAL SCIENCES↗