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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An attached microalgae platform for recycling phosphorus through biologically mediated fertilizer formation and biomass cultivation

Nutrient management is a global challenge for protecting water bodies from eutrophication and for retaining and sustainably recycling phosphorus within the biosphere. This challenge is especially important for water resource recovery facilities (WRRFs) in jurisdictions that limit nutrient loads in plant effluent. A microalgae-based biofilm platform has been designed, constructed, and tested to remove phosphorus and nitrogen from anaerobic digester (AD) effluent filtrate through cultivating biomass and inducing the precipitation of a mineral called struvite (NH₄MgPO₄ · 6H₂O) using a rotating algae biofilm reactor (RABR). RABRs function by rotating a growth substratum through nutrient rich water and then into the atmosphere, immersing microalgae in both sunlight and water. Photosynthesis is utilized in RABR operation to enhance struvite formation by increasing the pH value within the biofilm through the uptake of carbon dioxide from solution. Measurements of pH trended higher with depth through the biofilm when exposed to light confirming the function of photosynthesis in increasing pH and, as a consequence, struvite formation. In addition, reducing RPM allows more time for water to evaporate through exposure of the biofilm to the atmosphere and provides a management option to exceed struvite solubility product. Struvite precipitation was predicted based on chemical analysis and MINTEQ modeling of ADE, was confirmed using Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy, and quantified by determining ash content. Increase in pH within the biofilm was confirmed as photosynthetic photon flux density increased. While struvite precipitation and removal from wastewater is conventionally accomplished through physicochemical methods, this research is the first report of struvite enhanced formation through algae biofilm-based photosynthesis processes.

09 BIOMASS FUELS↗
DOE OSTI2022

Assessing the large-scale drivers of precipitation in the northeastern United States via linear orthogonal decomposition

This study examines the linear orthogonal modes associated with monthly precipitation in the northeastern United States, from CESM1 LENS (35 ensemble members, 1979–2005) and two reanalysis datasets (ERA5, 1950–2018 and NOAA-CIRES-DOE 20CRv3, 1950–2015). Calendar months are aggregated together, and any linear trends in data are removed. Using region-averaged precipitation anomaly time series and monthly anomalies for several global 2D atmospheric fields, a linear orthogonal decomposition method is implemented to iteratively extract time series (based on field and geographic location) of absolute maximum correlation. Linear modes associated with this method are then projected onto the full set of 2D fields to provide physical insight into the mechanisms involved in generating precipitation. In this region, the first mode is associated with vapor transport from the Atlantic seaboard, the second mode is characterized by westward vapor transport associated with extratropical cyclones, and the third mode captures vapor transport from the Gulf of Mexico during the fall and winter. However, the third mode is less robust in the spring and summer. Results are generally consistent across the datasets, and applying multiple linear regression with the linear modes to predict the precipitation anomalies produces R-squared values of around 0.54–0.65 for CESM1 LENS, and around 0.58–0.88 for reanalysis, with the lowest values generally in the spring and late summer. The influence of low-frequency climate variability on the modes is considered for CESM1 LENS, and the modes in late winter can be predicted with some success via a combination of several, prominent large-scale teleconnection patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Increased photosynthesis during spring drought in energy-limited ecosystems

Abstract Drought is often thought to reduce ecosystem photosynthesis. However, theory suggests there is potential for increased photosynthesis during meteorological drought, especially in energy-limited ecosystems. Here, we examine the response of photosynthesis (gross primary productivity, GPP) to meteorological drought across the water-energy limitation spectrum. We find a consistent increase in eddy covariance GPP during spring drought in energy-limited ecosystems (83% of the energy-limited sites). Half of spring GPP sensitivity to precipitation was predicted solely from the wetness index (R 2 = 0.47, p < 0.001), with weaker relationships in summer and fall. Our results suggest GPP increases during spring drought for 55% of vegetated Northern Hemisphere lands ( >30° N). We then compare these results to terrestrial biosphere model outputs and remote sensing products. In contrast to trends detected in eddy covariance data, model mean GPP always declined under spring precipitation deficits after controlling for air temperature and light availability. While remote sensing products captured the observed negative spring GPP sensitivity in energy-limited ecosystems, terrestrial biosphere models proved insufficiently sensitive to spring precipitation deficits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Forecasting Remote Atmospheric Responses to Decadal Kuroshio Stability Transitions

Based on observational estimates and global ocean eddy-resolving coupled retrospective initialized predictions, we show that Kuroshio Extension variability affects rainfall variability along the west coast of North America. We show that the teleconnection between the current undulations and downstream rainfall can lead to improved subseasonal to seasonal predictions of precipitation over California, and we demonstrate that capturing these teleconnections requires coupled systems with sufficient ocean resolution (i.e., eddy-resolving), especially over time scales longer than one season. The improved forecast skill is diagnosed in terms of 35 years of retrospective initialized ensemble forecasts with an ocean eddy-resolving and an ocean eddy-parameterized coupled model. Not only does the ocean eddy-resolving model show sensitivity to Kuroshio Extension variability in terms of western North America precipitation, but the ocean eddy-resolving forecasts also show improved forecast skill compared to the ocean eddy-parameterized model. Additionally, the ocean eddy-parameterized coupled model shows no sensitivity to Kuroshio Extension variability. We also find near-decadal variability associated with a progression of a lower-tropospheric height dipole around the North Pacific and how these height anomalies lead to wind-driven Rossby waves that affect the eddy activity in the Kuroshio Extension with a time lag on the order of four years. This decadal-scale variability (~10 years) opens the possibility of multiyear predictability of western North American rainfall.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The global decline in the sensitivity of vegetation productivity to precipitation from 2001 to 2018

The sensitivity of vegetation productivity to precipitation (S ppt ) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of S ppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of S ppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of S ppt with different satellite products: S ppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest S ppt , and evergreen needle-leaf forest and wetland showed the lowest. Temporally, S ppt showed a generally declining trend over the past two decades (p < .05), yet with clear spatial heterogeneities. The decline in S ppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, S ppt showed an increasing trend. Biome-wise, most ecosystem types exhibited significant decrease in S ppt , while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non-significant changes in S ppt . Our finding of the overall decline in S ppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed S ppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting the potential for mineral scale precipitation in unconventional reservoirs due to fluid-rock and fluid mixing geochemical reactions

Mineral precipitation within hydraulically fractured shale may affect fluid flow pathways and impact longterm hydrocarbon production. The ability to predict geochemical reactions that lead to problematic mineral precipitation will lead to active reservoir management strategies to improve overall production. Using the Marcellus Shale as a case study, a combination of laboratory experiments and reaction path modeling was applied to determine which reactions are likely to occur upon introduction of hydraulic fracturing fluid into the shale reservoir. Experimental results indicate that contact between fracturing fluid and shale will result in dissolution of primary minerals (quartz, feldspars, kaolinite, chlorite, pyrite) and secondary mineral precipitation over time periods of less than one week. Precipitation of barite, Fe-oxides, feldspars, amorphous silica and clay is likely to occur within the reservoir during shut in and early flowback due to mixing between fracturing fluid and reservoir brine as based on modeling saturation indices using experimental fluid data. Reaction path modeling of the experimental scenarios corroborates the specific dissolution and precipitation reactions observed experimentally. Comparison of the results to injected and produced waters from a Marcellus Shale well pad in Greene County, PA, USA, shows that the mineral reactions occur during the hydraulic fracturing, shut in, and early flowback periods. The results presented in this paper demonstrate the value in applying experimental approaches to identify mineral precipitation/dissolution reactions that may significantly impact reservoir performance. The good agreement between geochemical models and experimental results provides confidence that numerical models can be applied to screen the potential fluid-mineral and fluid-mixing reactions in unconventional reservoirs that result in undesired mineral scale precipitation.

58 GEOSCIENCES↗
DOE OSTI2024

Quantifying the Role of EMIC Wave Scattering During the 27 February 2014 Storm by RAM–SCB Simulations

Electromagnetic Ion Cyclotron (EMIC) wave scattering has been proved to be responsible for the fast loss of both radiation belt (RB) electrons and ring current (RC) protons. However, its role in the concurrent dropout of these two co–located populations remains to be quantified. In this work, we study the effect of EMIC wave scattering on both populations during the 27 February 2014 storm by employing the global physics–based RAM–SCB model. Throughout this storm event, MeV RB electrons and 100s keV RC protons experienced simultaneous dropout following the occurrence of intense EMIC waves. By implementing data–driven initial and boundary conditions, we perform simulations for both populations through the interplay with EMIC waves and compare them against Van Allen Probes observations. Notably, the results indicate that by including EMIC wave scattering loss, especially by the He–band EMIC waves, the model aligns closely with data for both populations. Additionally, we investigate the simulated pitch angle distributions (PADs) for both populations. Including EMIC wave scattering in our model predicts a 90° peaked PAD for electrons with stronger losses at lower pitch angles, while protons exhibit an isotropic PAD with enhanced losses at pitch angles above 40°. Furthermore, our model predicts considerable precipitation of both particle populations, predominantly confined to the afternoon to midnight sector (12 hr < MLT < 24 hr) during the storm's main phase, corresponding closely with the presence of EMIC waves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Declining groundwater storage expected to amplify mountain streamflow reductions in a warmer world

Abstract Groundwater interactions with mountain streams are often simplified in model projections, potentially leading to inaccurate estimates of streamflow response to climate change. Here, using a high-resolution, integrated hydrological model extending 400 m into the subsurface, we find groundwater an important and stable source of historical streamflow in a mountainous watershed of the Colorado River. In a warmer climate, increased forest water use is predicted to reduce groundwater recharge resulting in groundwater storage loss. Losses are expected to be most severe during dry years and cannot recover to historical levels even during simulated wet periods. Groundwater depletion substantially reduces annual streamflow with intermittent conditions predicted when precipitation is low. Expanding results across the region suggests groundwater declines will be highest in the Colorado Headwater and Gunnison basins. Our research highlights the tight coupling of vegetation and groundwater dynamics and that excluding explicit groundwater response to warming may underestimate future reductions in mountain streamflow.

Carroll, Rosemary W. H. (ORCID:0000000293028074)↗
DOE OSTI2024

A mixture of grass–legume cover crop species may ameliorate water stress in a changing climate

Climate change models predict increasing precipitation variability in the mid-latitude regions of Earth, generating a need to reduce the negative impacts of these changes on crop production. Despite considerable research on how cover crops support agriculture in a changing climate, understanding is limited of how climate change influences the growth of cover crops. We investigated the early development of two common cover crop species—crimson clover (Trifolium incarnatum) and rye (Secale cereale)—and hypothesized that growing them in the mixture would ameliorate stress from drought or waterlogging. This hypothesis was tested in a 25-day greenhouse experiment, where the two factors (species number and water stress) were fully crossed in randomized blocks, and plant responses were quantified through survival, growth rate, biomass production and root morphology. Water stress negatively influenced the early growth of these two species in contrasting ways: crimson clover was susceptible to drought while rye performed poorly under waterlogging. Per-plant biomass in rye was always greater in mixture than in monoculture, while per-plant biomass of crimson clover was greater in mixture under drought. Both species grew longer roots in mixture than in monoculture under drought, and total biomass of mixtures did not differ significantly from the more-productive monoculture (rye) in any water condition. In the face of increasingly variable precipitation, growing crimson clover and rye together has potential to ameliorate water stress, a possibility that should be further investigated in field experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling

We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities. A hybrid framework incorporating traditional ESM modeling, inference methods, and AI techniques could make better use of both measured and computed information as well as computational resources by targeting inference tasks at program priorities, such as the predictability of precipitation extremes. This runtime pathway to closing the simulation/analysis-data/model improvement loop will streamline the traditional offline pathway to model improvement, which is based on domain science expertise, while suggesting guidance for further observations and measurements.

58 GEOSCIENCES↗
DOE OSTI2020

Modeling regional precipitation over the Indus River basin of Pakistan using statistical downscaling

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. Furthermore, we also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Additionally, such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High-Resolution Global Nonhydrostatic Model

Current climate model resolution cannot accurately describe the complex topography over the Tibetan Plateau, which limits our understanding of past and future precipitation over this region. This study investigates the daily precipitation characteristics and its future changes over the Tibetan Plateau, especially for the southern slope of Tibetan Plateau (SSTP), by 14-km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) with explicitly calculated convection for historical and future 30-years period. By comparing with the satellite Global Precipitation Measurement (GPM), NICAM well reproduces the historical precipitation spatial pattern, seasonal cycle, and the extreme precipitation belt over SSTP, but overestimates precipitation amount by~35%. It is found that heavy precipitation probability decreases as elevation becomes higher while the light precipitation generally shows the opposite. For the precipitation changes during June to September from 1979–2008 to 2075–2104, NICAM predicts that mean precipitation will decrease over low-level SSTP but increase over high-level SSTP. Nonprecipitation and heavy precipitation probability will increase while light precipitation probability will decrease in the future over SSTP. The extreme precipitation probability and intensity will increase ~50%/°C and ~8%/°C over SSTP, and this increase is more obvious as elevation becomes higher. The robust increase of extreme precipitation along the SSTP topography is unique and has not been identified by the climate model simulations before. The strong meridional gradient of specific humidity over SSTP is found to be further enhanced under global warming, and this gradient enhancement is suggested to be responsible for the increase in the extreme precipitation over SSTP.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Warming decreases desert ecosystem functioning by altering biocrusts in drylands

Warming and precipitation fluctuations are changing desert ecosystems in global drylands. However, the effects of climate change on keystone species such as cryptogamic biocrust in drylands remain relatively under-investigated, even though biocrusts play a vital role in desert ecosystems. Here we conducted a long-term experiment (14 years) to simulate the responses of two main types of biocrusts to warming coupled with reduction in precipitation that was achieved by open-top chambers (OTCs) to simulate the predicted warming and precipitation decreasing under climate change scenario. We also conduct a structural analysis to evaluate the resulting changes in desert ecosystem functioning (carbon and nitrogen cycling). Neither warming and corresponding rainfall reduction treatments had a negative effect on lichen species richness, but both treatments reduced lichen cover and biomass. The negative effects of warming on moss-dominated crusts were much greater than those on lichen-dominated crusts. Although mosses and lichens had varying degree responses to warming, the loss of mosses and decreased lichen cover and biomass, as well as the shortening of the wet time, resulted in a reduction in carbon and nitrogen fixation, soil enzyme activity and water-holding capacity of biocrusts and topsoil. These impacts collectively change the water balance of drylands and weaken the hydrological and biogeochemical function of biocrusts. Synthesis and applications: Results from this long-term experiment suggest that the ecosystem C and N cycling and water balance of global drylands may be highly impacted by climate change, in part because of the response of biocrusts, which contribute an important implication for both dryland restoration and earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗
DOE OSTI2024

Meteorological Drivers of North American Monsoon Extreme Precipitation Events

Abstract In this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self‐organizing maps applied to the Climate Prediction Center global precipitation data set. This reveals distinct subregions, shaped by the inhomogeneous geographic features of the NAM area, with distinct extreme precipitation character and drivers. Next, defining EPEs as days when subregion‐mean precipitation exceeds the 95th percentile of rainy days, five synoptic features and one mesoscale feature are investigated as potential drivers of EPEs. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining unclassified. This analysis shows the dominant role of Gulf of California moisture surges, mesoscale convective systems and frontal systems in generating NAM extreme precipitation. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. The findings demonstrate that the co‐occurrence of multiple features does not necessarily enhance the EPE probability.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Global Precipitation Experiment—A New World Climate Research Programme Lighthouse Activity

The future state of the global water cycle and the prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation Experiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing, and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling, and experimental research and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here, we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2–3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multiscale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

Climate change↗
DOE OSTI2020

Seasonal grassland productivity forecast for the U.S. Great Plains using Grass–Cast

Every spring, ranchers in the drought-prone U.S. Great Plains face the same difficult challenge —trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass-Cast, to provide science-informed estimates of growing season above ground net primary production (ANPP). Grass-Cast uses over 30 yr of historical data including weather and the satellite-derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below-normal, near-normal, or above-normal amounts of grass biomass (lbs/ac). Grass-Cast also provides a view of rangeland productivity in the broader region, to assist in larger scale decision-making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass-Cast is updated approximately every two weeks from April through July. Each Grass-Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real-time 8-d NDVI can be used to supplement Grass-Cast in predicting cumulative growing season NDVI and ANPP starting in mid-April for the Southern Great Plains and mid-May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass-Cast along with the county-level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end-of growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass-Cast end-of-growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20-yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass-Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production.

54 ENVIRONMENTAL SCIENCES↗