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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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13 records

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NASA NTRS1981

A simulation study of the recession coefficient for antecedent precipitation index

The antecedent precipitation index (API) is a useful indicator of soil moisture conditions for watershed runoff calculations and recent attempts to correlate this index with spaceborne microwave observations have been fairly successful. It is shown that the prognostic equation for soil moisture used in some of the atmospheric general circulation models together with Thornthwaite-Mather parameterization of actual evapotranspiration leads to API equations. The recession coefficient for API is found to depend on climatic factors through potential evapotranspiration and on soil texture through the field capacity and the permanent wilting point. Climatologial data for Wisconsin together with a recently developed model for global isolation are used to simulate the annual trend of the recession coefficient. Good quantitative agreement is shown with the observed trend at Fennimore and Colby watersheds in Wisconsin. It is suggested that API could be a unifying vocabulary for watershed and atmospheric general circulation modelars.

Choudhury, B. J.↗

NASA NTRS1975

The correlation of Skylab L-band brightness temperatures with antecedent precipitation

The S194 L-band radiometer flown on the Skylab mission measured terrestrial radiation at the microwave wavelength of 21.4 cm. The terrain emissivity at this wavelength is strongly dependent on the soil moisture content, which can be inferred from antecedent precipitation. For the Skylab data acquisition pass from the Oklahoma panhandle to southeastern Texas on 11 June 1973, the S194 brightness temperatures are highly correlated with antecedent precipitation from the preceding eleven day period, but very little correlation was apparent for the preceding five day period. The correlation coefficient between the averaged antecedent precipitation index values and the corresponding S194 brightness temperatures between 230 K and 270 K, the region of apparent response to soil moisture in the data, was -0.97. The equation of the linear least squares line fitted to the data was: API (cm) = 31.99 -0.114 TB (K), where API is the antecedent precipitation index and TB is the S194 brightness temperature.

Mcfarland, M. J.↗

NASA NTRS1975

The correlation of Skylab L-band brightness temperatures with antecedent precipitation

Mcfarland, M. J.↗

NASA NTRS1981

Dryland pasture and crop conditions as seen by HCMM

The antecedent precipitation index (API) was related to surface temperatures as measured from the NASA C-130 and HCMM thermal data. Significant results from the aircraft flight in May 1978, include: (1) canopy temperature were measured accurately remotely; (2) pasture surface temperatures were related to pasture and wheat soil moisture conditions; (3) no relationship was developed with that data set between wheat yield and thermal infrared data due to a lack of moisture stress during the measurement period; and (4) lake surface temperatures were useful in normalizing the thermal IR data. Results from HCMM also suggested a relationship between thermal IR data and antecedent precipitation index. While HCMM was adequate in detecting relative soil moisture differences, the overpass timing was infrequent and prevented detailed analysis of the API/thermal relationship.

Harlan, J. C.↗

DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗

NASA NTRS1981

The DMSP imager (SSMI): Unfinished plans

The basic theory relating microwave emissivity to soil and snow moisture is presented along with data from field and aircraft measurements to support the theory. Data from the ESMR on Nimbus-5 and the S-194 L Band radiometer on Skylab were compared with Antecedent Precipitation indices (APT) to show the sensitivity of spaceborne observations to soil moisture. Similarly, data from the ESMR and SMMR on the Nimbus spacecraft were compared with surface measurements of snow depth with good results.

Savage, R. C.↗

DOE OSTI2026

Storylines for the 1997 New Year’s Flood: The role of watershed antecedent conditions and future warming in shaping discharge in the Truckee River watershed

The 1997 New Year’s flood was among the most devastating floods in the Truckee River watershed located in western Nevada. This event resulted from complex interactions of flood drivers, such as extreme precipitation, wet antecedent watershed conditions, warm temperatures and rapid snowmelt. We leveraged simulated forcings from the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) and a process-based hydrological model to recreate the 1997 New Year’s flood for the Truckee River watershed across four climate warming levels ranging from the current temperatures to + 4° C. For each scenario, we conducted ensemble simulations with the same forcing but with 100 different seasonal watershed antecedent conditions, which were randomly sampled from long-term hydrological simulations. The results show that the 1997 New Year’s flood can be reproduced or exceeded consistently only when the antecedent watershed conditions are wet, specifically when streamflows are above the 75th percentile of the climatological value. There is negligible change in ensemble mean peakflows for Truckee River near Reno; however, there are increases of 18% and 14% under the warming levels of + 3° C and + 4° C, respectively. The increases in peakflows under future climate warming are attributed to wetter antecedent watershed conditions and enhanced snowmelt. Furthermore, the largest increases in peakflows occur at small, high-elevation headwater basins along the Sierra Nevada crest. This study highlights that changes in extreme flood events will result from the complex interplay of multiple flood drivers. It also demonstrates the potential of storyline approaches to analyze future realizations of these extreme events under different climate scenarios.

Climate change impact study↗

DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗

DOE OSTI2024

Soil Moisture‐Cloud‐Precipitation Feedback in the Lower Atmosphere From Functional Decomposition of Satellite Observations

Abstract The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

From tides to seasons: How cyclic tidal drivers and plant physiology interact to affect carbon cycling at the terrestrial-estuarine boundary (Final technical report)

Coastal ecosystems are among the most biologically and biogeochemically active and diverse systems on Earth. Because they act as important linkages between terrestrial ecosystems and the open ocean, their incorporation in Earth system models (ESMs) is critical to predict coastal and global responses to environmental changes. However, they vary greatly in the magnitude of tides and the volume and timing of freshwater input from land, making it challenging to model the major biogeochemical reactions that control productivity and greenhouse gas emissions across coastal terrestrial aquatic interfaces (TAIs). Our overall objective was to improve mechanistic process understanding and modeling of tidal wetland hydro-biogeochemistry in coastal TAIs. We established a new flux tower site (Ameriflux US-PLo) in the oligohaline part of the Parker River to continuously monitor ecosystem-scale carbon fluxes under temporally varying salinity conditions. The site is co-located with long-term monitoring plots of the Plum Island Ecosystems LTER project. We installed wells and redox sensors in the marsh interior and creek bank, established biomass monitoring plots and deployed novel optode sensors in both locations. We used this data to parameterize plant-mediated transport in PFLOTRAN and tested the impact of soil heterogeneity on porewater constituents and gas fluxes. We collected observations of root oxygen release with a novel planar optode system in the field. Flux data collected during the measurement period encompasses a large variation in salinity ranging from drought to record precipitation years. We developed a method to extract functional relationships from the flux data using artificial neural networks, identifying salinity thresholds for CH 4 fluxes. Finally, we are using the coupled ELM-PFLOTRAN model to test the impact of antecedent hydrological conditions on the salinity-CH 4 flux relationship. This grant contributed to the professional development of one postdoc, three research assistants and one graduate student. The sensor data has been shared with external collaborators.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Disentangling atmospheric, hydrological, and coupling uncertainties in compound flood modeling within a coupled Earth system model

Compound riverine and coastal flooding is usually driven by complex interactions among meteorological, hydrological, and ocean extremes. However, existing efforts to model this phenomenon often do not integrate hydrological processes across atmosphere–land–river–ocean systems, leading to substantial uncertainties that have not been fully examined. To bridge this gap, we leverage the new capabilities of the Energy Exascale Earth System Model (E3SM) that enable a multi-component framework that integrates coastally refined atmospheric, terrestrial, and oceanic components. We evaluate compound uncertainties arising from two-way land–river–ocean coupling in E3SM and track the cascading meteorological and hydrological uncertainties through ensemble simulations over the Delaware River basin and estuary during Hurricane Irene (2011). Our findings highlight the importance of two-way river–ocean coupling to compound flood modeling and demonstrate E3SM's capability in capturing compound flood extent near the coast, with a hit rate over 0.75. Our study shows the growing uncertainties that transition from atmospheric forcings to flood distribution and severity. Furthermore, an analysis based on artificial neural networks is used to assess the roles of hydrological drivers, such as infiltration and soil moisture, in the generation of compound flooding. The response of compound floods to tropical cyclones (TCs) is found to be susceptible to these often overlooked drivers. For instance, the flooded area could increase more than 2-fold (∼2.4) if Hurricane Irene were preceded by an extreme antecedent soil moisture condition (AMC). The results not only support the use of a multi-component framework for interactive flooding processes, but also underscore the necessity of broader definitions of compound flooding that encompass the simultaneous occurrence of intense precipitation, storm surge, and high AMC during TCs.

Feng, Dongyu [Pacific Northwest National Laborator↗

DOE OSTI2025

A Greening Future Elevates Flash Drought Risk in Northern Mid‐to‐High Latitudes

Flash droughts have become a growing concern, as they can emerge rapidly and increase the risk of crop failure. Although past studies have investigated the meteorological drivers and future changes of flash drought, why flash drought is more frequent over humid and vegetated regions remains underexplored. This study delves further into the mechanism by which vegetation regulates flash drought and its future change using observations from multiple data sets and large ensemble simulations from three Earth system models. On an interannual timescale, both observations and simulations show robust increases in flash drought frequency and a higher flash-to-sub-seasonal drought ratio during spring or antecedent conditions with dense vegetation, supporting the important role of vegetation in flash drought occurrence, especially in the northern mid-to-high latitudes. In the latter regions, the large ensemble simulations show robust increases in flash drought (e.g., 67% and 46% increases in Eastern U.S. and North Asia in 2050–2100 relative to 1950–2000 under the high emission scenario), where the growing season is lengthening. Although greening might suggest reduced drought stress, it drives precipitation-soil moisture-evapotranspiration decoupling by increasing evapotranspiration partitioning to transpiration. As transpiration can access deep soil water through the plant root system, its increased portion can weaken the constraints of concurrent precipitation on evapotranspiration, thus accelerating soil moisture depletion under high evaporative demand, driving a slow-to-rapid drought transition. How vegetation regulates flash drought by regulating surface moisture budget is supported by observations and simulations. Although warming supports early planting, agriculture may increasingly be threatened by surging flash drought risk.

Drought↗

DOE OSTI2025

Atmospheric River Frequency-Category Characteristics Shape U.S. West Coast Runoff

Abstractrunoff response to atmospheric rivers (ARs) over the U.S. West Coast. We focused on runoff time series variations impacted by AR characteristics (e.g., category and frequency) and land preconditions during Northern Hemisphere cool seasons in the period of 1940–2023. Results show that high-category ARs significantly increase local runoff with higher hourly precipitation rates leading to a greater incremental rate and peak runoff. Extreme runoff increases greatly with the AR category with an increase rate up to 12.5 times stronger than non-extreme runoff. Besides the AR category, land preconditions such as soil moisture and snowpack also play crucial roles in modulating runoff response. We found that runoff induced by weak-category ARs is more sensitive to land preconditions than high-category ARs, with high peak runoff occurring when soil is nearly saturated. Additionally, more than 50% of high-peak-runoff events in snow-covered grid cells are associated with rain-on-snow events particularly for the events associated with weaker ARs. Regression analysis reveals that AR precipitation and land preconditions jointly influence runoff, emphasizing the importance of including soil moisture and snowpack levels in AR impact assessments. The study also highlights the intensified runoff response to back-to-back ARs with short intervals, which may become more frequent with climate warming, posing increased flood risks via facilitating wet soil conditions. Our findings have significant implications for AR risk predictions and the development of prediction models for AR-induced runoff.

54 ENVIRONMENTAL SCIENCES↗