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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Assessment of the impact of reactor residence time distribution on non-equilibrium product selectivity of polypropylene pyrolysis using reactive molecular dynamics simulations

Mass residence time distribution (RTD) is considered to be an important factor controlling the product selectivity in the pyrolysis of biomass and plastic wastes along with the pyrolysis chemistry. However, due to the complex pyrolysis chemistry of biomass and plastic waste, the coupling between the reaction chemistry, RTD, and product selectivity is challenging to understand. Here, we introduce a reaction molecular dynamics-based method to examine pyrolysis chemistry and species timescales to assess the impact of RTD on product selectivity and yield. To validate this method, reactive molecular dynamics simulations were conducted for polypropylene pyrolysis and its non-equilibrium product selectivity using 6 different RTDs. We find that the RTD and the reaction chemistry control the peak non-equilibrium product concentrations. The peak monomer (C 3 H 6 ) concentration during pyrolysis can be increased by up to 25 % by using a narrow RTD in the case of polypropylene pyrolysis. We also find that product selectivity is strongly affected by the average residence time and RTD. This coupling between the reaction chemistry, RTD, and product selectivity highlights the need to understand detailed reaction chemistry to control RTD and optimize non-equilibrium product selectivity during polymer and biomass pyrolysis. The present method provides a new way to design RTD for reactors to reach maximized product selectivity of plastic waste and biomass.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Design and analysis of actively-cooled, edge-transport diagnostic for long-pulsed operation in WEST

Next step fusion devices that will operate in steady-state will require complex plasma-facing components (PFCs) that can survive the harsh environment over long timescales not common in current devices. This will require robust plasma facing surfaces that are integrated with active cooling systems. In a collaboration between CEA and ORNL, a plasma-interacting diagnostic is being designed for the W Environment in Steady-state Tokamak (WEST) in Cadarache, France which requires plasma-facing protection like those needed for steady-state PFCs. This integrated diagnostic studies edge transport and impurity migration within WEST, and is planned to include imbedded temperature Langmuir probe sensors, as well as removeable sample slots for ex-situ surface analysis of plasma-material interactions. The entire assembly is expected to move into the plasma edge for periods up to 1000 s having an energy removeable capability of ∼6 kW and seeing a peak heat flux of more than 7 MW/m2. The assembly compliments WEST high fluence campaigns that plan for multiple 1000 s pulses. Within these specifications, the assembly will require a refractory metal plasma facing surface and integral cooling in order to function within the limited space allotted for such diagnostics. Because of the limited space and linear actuator needs, additive manufacturing of the high heat flux working end of the assembly is being considered which could allow for precision cooling-channels and lighter weight designs. Conceptual design along with simulation and analysis results will be presented for this complex diagnostic with novel PFCs.

Lumsdaine, Arnold↗
DOE OSTI2025

Experimental determination of hydrogen isotopic equilibrium in the system H 2 O (l) -H 2(g) from 3 to 90 °C

Molecular hydrogen (H 2 ) is found in a variety of settings on and in the Earth from low-temperature sediments to hydrothermal vents, and is actively being considered as an energy resource for the transition to a green energy future. The hydrogen isotopic composition of H 2 , given as D/H ratios or δD, varies in nature by hundreds of per mil from ∼−800 ‰ in hydrothermal and sedimentary systems to ∼+450 ‰ in the stratosphere. This range reflects a variety of processes, including kinetic isotope effects associated with formation and destruction and equilibration with water, the latter proceeding at fast (order year) timescales at low temperatures (<100 °C). At isotopic equilibrium, the D/H fractionation factor between liquid water and hydrogen ( D α H2O(l)-H2(g) ) is a function of temperature and can thus be used as a geothermometer for H 2 formation or re-equilibration temperatures. Multiple studies have produced theoretical calculations for hydrogen isotopic equilibrium between H 2 and water vapor. However, only three published experimental calibrations used in geochemistry exist for the H 2 O-H 2 system: two between 51 and 742 °C for H 2 O (g) -H 2(g) (Suess, 1949, Cerrai et al., 1954), and one in the H 2 O (l) -H 2(g) system for temperatures <100 °C (Rolston et al., 1976). Despite these calibrations existing, there is uncertainty on their accuracy at low temperatures (<100 °C; e.g., Horibe and Craig, 1995).

08 HYDROGEN↗
DOE OSTI2024

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Modeling and Experimental Validation of a Direct-Contact Counter-Flow Fluidized Bed Heat Exchanger for Thermal Energy Storage (TES) Applications

Particle-based thermal energy storage (TES) systems are an emerging energy storage technology. The technological advances have reduced costs, making TES more competitive and reliable in the marketplace but an efficient and reliable operation is heavily dependent on coherent heat transfer between air to particles or vice versa. The particle-based TES technologies provide an intermediate system that can store energy for short (0-10 h), long (10-200 h) and seasonal (> 200 h) timescales. The TES systems store energy by converting electricity to thermal energy; electricity can be directly sourced intermittent generation technologies and/or the grid, helping manage peak loads and other mismatches in supply and demand. The overall efficiency of the TES system depends on the performance of system components (particle storage silos and particle transfer mechanism etc.). The particle heat exchanger is one of the key system components that affects the system efficiency. The pressurized fluidized bed heat exchanger (PFB HX) performance is challenging to predict due to the chaotic behavior of particle and fluid interaction. This research presents a computational study of a novel direct-contact, counter-flow and air-to-particles PFB HX, that contributes in advancing the particle-based long-duration TES technologies. For the current analysis an unsteady Eulerian-Eulerian CFD model was developed and validated against experiments performed at the National Laboratory of the Rockies for two particle sizes (600 ..mu..m and 825 ..mu..m ). Following validation, parametric simulations were conducted to evaluate the effects of interphase drag models (Syamlal-O'Brien and Gidaspow), particle size, bed height and the influence of a frictional-viscosity term on hydrodynamics and heat transfer between the air & particles. The key findings from the analysis are: (1) for the studied operating window Syamlal-O'Brien provides superior agreement with measured gas temperatures (errors generally < 10%) while Gidaspow shows large deviations for the coarse particle case; (2) model predictions are most sensitive in the lower 0.2 m above the air distributor where bubble initiation and local mixing dominate interphase heat transfer; (3) representation of the distributor (number of inlet ports) materially affects predicted local mixing and temperature stratification; and (4) the Eulerian-Eulerian framework reproduces bulk thermal trends but shows regime dependent limitations for coarse particles, motivating mesoscale informed closures for scale-up analysis for future studies. These results provide validated guidance for drag selection and distributor design in particle-based thermal energy storage applications. Collectively, the validated model and parametric results quantify key drivers of PHB-HX performance and provide practical guidance for design and optimization. The results provide confidence in the model predictability and provide a step forward to improve on heat exchange performance. The demonstrated performance and modeling approach support the deployment and further development of this novel PHB-HX concept for robust, particle-based long-duration thermal energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2022

Unifying Energy-Management Problems for Inverter-Based Power Networks

This paper outlines a suite of energy-management problems for inverter-based power networks from the vantage point of optimal control and (non)linear optimization. The problems are categorized based on timescales dictated by the network dynamics, and organized methodologically based on the problem complexity. A growing body of literature has addressed problems in this domain, albeit, with poorly motivated assumptions and behavioral models that obscure precise device behavior. With a combination of circuit- and control-theoretic lenses, we establish appropriate dynamic models for the networked resources, illustrate how common engineering assumptions arise, uncover how problems are linked, and postulate open challenges.

(non)linear optimization↗
DOE OSTI2023

A unified non-equilibrium phase change model for injection flow modeling

The homogenous relaxation model (HRM) is one of the most widely used models to describe the liquid- gas phase transition. However, in its original formulation, it is unable to handle multispecies vapor-liquid equilibrium (VLE), which limits its applicability to single-component fluids. In this work, a unified non-equilibrium phase change model that considers the VLE of multicomponent mixtures is proposed building upon the HRM's structure. A time factor is introduced to mimic the effect of different phase change timescales due to different mechanisms, e.g., cavitation, flash-boiling, and evaporation. Here to assess the model's performance, computational fluid dynamics simulations of the internal and near-nozzle injection flow of the Engine Combustion Network's Spray G injector were performed using the nine-component PACE-20 fuel with both the unified model and the original HRM. The predicted fuel density in the near-nozzle region matched well with X-ray tomography measurements. The simulation results indicated that, whereas the HRM failed to capture the vaporization due to convective mixing between the fuel and ambient gas, the unified model performed well in predicting the mixing-driven vaporization and the corresponding evaporative cooling. Further comparisons using the nine-component fuel formula and a single-component fuel surrogate demonstrated the unified model's ability to predict preferential vaporization, which affects the predictions of local mixture composition and rate of vaporization. Finally, it is shown that the unified model is capable of representing multiple phase change mechanisms, and the relaxation time factor plays an important role in determining the degree of phase change due to the different mechanisms.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

15 years of spin-echo spectroscopy at SNS-NSE: Looking back and looking forward

High-resolution neutron spin echo (NSE) spectroscopy offers unique insights into the mobility of molecular (sub)structures excited by thermal fluctuations (i.e., Brownian motion) of “soft matter” as polymers in solution and in the melt, in biological matter (e.g., protein motions), membranes, and glasses. The ability to tag substructures using H, D contrast variation to resolve the relevant timescales of dynamics on selected molecular items in liquids, soft matter, and melts is a significant advantage of NSE. Also slow magnetic fluctuations on molecular length scales in the range of nanoseconds, e.g., in spin glasses or topological spin structures, can be accessed. This paper reviews the highlights and peculiarities of the SNS-NSE, based at the pulsed neutron source SNS, during its first 1.5 decades of operation. An outlook and perspectives of research in the domain of high-resolution spectroscopy is given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Neutron irradiation-enhanced grain growth in tungsten and tungsten alloys

Herein, to understand the microstructural stability of candidate plasma-facing materials under fusion-relevant environments, neutron irradiation of W and W-3%Re alloys with and without K and La dopants was performed in the mixed-spectrum High Flux Isotope Reactor at nominal temperatures of ~850 °C and ~1100 °C to calculated doses between 0.42 and 0.47 dpa. To the best of our knowledge, this study presents the first experimental evidence of radiation-enhanced recrystallization in W and undoped W–Re alloys at ~850 °C, conditions where thermal annealing does not cause any grain growth in a similar timescale. Potassium- or lanthanum-doped tungsten alloys showed more resistance to radiation-enhanced grain growth. We explain the acceleration of grain growth by analyzing the self-diffusion constant under atomic displacement environments. The microstructural observations of the studied W variants suggest that La doping is more effective than K doping for mitigating recrystallization. This study also found that radiation-enhanced recrystallization is an important consideration when designing and applying W to plasma-facing components in future nuclear fusion reactors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Molecular simulation of lignin-related aromatic compound permeation through gram-negative bacterial outer membranes

Lignin, an abundant aromatic heteropolymer in secondary plant cell walls, is the single largest source of renewable aromatics in the biosphere. Leveraging this resource for renewable bioproducts through targeted microbial action depends on lignin fragment uptake by microbial hosts and subsequent enzymatic action to obtain the desired product. Recent computational work has emphasized that bacterial inner membranes are permeable to many aromatic compounds expected from lignin depolymerization processes. In this study, we expand on these findings through simulations for 42 lignin-related compounds across a gram-negative bacterial outer membrane model. Unbiased simulation trajectories indicate that spontaneous crossing for the full outer membrane is relatively rare at molecular simulation timescales, primarily due to preferential membrane partitioning and slow diffusion within the lipopolysaccharide layer within the outer membrane. Membrane partitioning and permeability coefficients were determined through replica exchange umbrella sampling simulations to overcome sampling limitations. We find that the glycosylated lipopolysaccharides found in the outer membrane increase the permeation barrier to many lignin-related compounds, particularly the most hydrophobic compounds. However, the effect is relatively modest; at industrially relevant concentrations, uncharged lignin-related compounds will readily diffuse across the outer membrane without the need for specific porins. Together, our results provide insight into the permeability of the bacterial outer membrane for assessing lignin fragment uptake and the future production of renewable bioproducts.

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

Smectite phase separation is driven by hydration-mediated interfacial charge

Smectite clay minerals have an outsize impact on the response of clay-rich media to common stimuli, such as hydration and ion exchange, motivating extensive effort to understand behaviors resulting from these processes such as swelling and exfoliation. Smectites are common and historic systems for investigating colloidal and interfacial phenomena, with two swelling regimes commonly identified across myriad clays: osmotic swelling at high water activity and crystalline swelling at low water activity. However, no current swelling model seamlessly spans the full ranges of water, salt and clay content encountered in natural or engineered settings. Here, we show that structures previously rationalized as either osmotic or crystalline coexist as a rich array of distinct colloidal phases that differ by water content, layer stacking thickness, and curvature. We present an analytical model for intermolecular potentials among water, salt and clay in both mono- and divalent electrolytes that predicts swelling pressures across high and low water activities. Our results indicate that all clay swelling is osmotic swelling, but that the osmotic pressure of charged mineral interfaces becomes attractive and dominates that of the electrolyte at high clay activities. Global energy minima are often not reached on experimental timescales due to many local energy minima that promote long-lived intermediate states with vast differences in clay, ion, and water mobilities, leading to hyperdiffusive layer dynamics driven by variable hydration-mediated interfacial charge.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Predicting rare events using neural networks and short-trajectory data

Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or measurement needed to resolve the elemental dynamics, accurate prediction from direct observations becomes challenging. In such cases a more effective approach is to cast statistics of interest as solutions to Feynman-Kac equations (partial differential equations). Here, we develop an approach to solve Feynman-Kac equations by training neural networks on short-trajectory data. Our approach is based on a Markov approximation but otherwise avoids assumptions about the underlying model and dynamics. This makes it applicable to treating complex computational models and observational data. Additionally, we illustrate the advantages of our method using a low-dimensional model that facilitates visualization, and this analysis motivates an adaptive sampling strategy that allows on-the-fly identification of and addition of data to regions important for predicting the statistics of interest. Finally, we demonstrate that we can compute accurate statistics for a 75-dimensional model of sudden stratospheric warming. This system provides a stringent test bed for our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Implicit-explicit Runge-Kutta for radiation hydrodynamics I: Gray diffusion

Radiation hydrodynamics are a challenging multiscale and multiphysics set of equations. To capture the relevant physics of interest, one typically must time step on the hydrodynamics timescale, making explicit integration the obvious choice. On the other hand, the coupled radiation equations have a scaling such that implicit integration is effectively necessary in non-relativistic regimes. A first-order Lie-Trotter-like operator split is the most common time integration scheme used in practice, alternating between an explicit hydrodynamics step and an implicit radiation solve and energy deposition step. However, such a scheme is limited to first-order accuracy, and nonlinear coupling between the radiation and hydrodynamics equations makes a more general additive partitioning of the equations non-trivial. Here, we develop a new formulation and partitioning of radiation hydrodynamics with gray diffusion that allows us to apply (linearly) implicit-explicit Runge-Kutta time integration schemes. In conclusion, we prove conservation of total energy in the new framework, and demonstrate 2nd-order convergence in time on multiple radiative shock problems, achieving error 3–5 orders of magnitude smaller than the first-order Lie-Trotter operator split at the hydrodynamic CFL, even when Lie-Trotter applies a 3rd-order TVD Runge-Kutta scheme to the hydrodynamics equations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Moment-based adaptive time integration for thermal radiation transport

Here, in this paper we develop a framework for moment-based adaptive time integration of deterministic multifrequency thermal radiation transpot (TRT). We generalize our recent semi-implicit-explicit (IMEX) integration framework for gray TRT to multifrequency TRT, and also introduce a semi-implicit variation that facilitates higher-order integration of TRT, where each stage is implicit in all components except opacities. To appeal to the broad literature on adaptivity with Runge–Kutta methods, we derive new embedded methods for four asymptotic preserving IMEX Runge–Kutta schemes we have found to be robust in our previous work on TRT and radiation hydrodynamics. We then use a moment-based high-order-low-order representation of the transport equations. Due to the high dimensionality, memory is always a concern in simulating TRT. We form error estimates and adaptivity in time purely based on temperature and radiation energy, for a trivial overhead in computational cost and memory usage compared with the base second order integrators. We then test the adaptivity in time on the tophat and Larsen problem, demonstrating the ability of the adaptive algorithm to naturally vary the timestep across 4–5 orders of magnitude, ranging from the dynamical timescales of the streaming regime to the thick diffusion limit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Sample environment effects on synchrotron-measured temperature profiles in an approximant of optical floating zone crystal growth

Even though the growth of crystals using optical floating zone furnaces has had an immense scientific impact, the implementation of this method remains more of an art than a science due to the difficulty of obtaining quantitative information about the sample thermal profile during crystal growth. Building on recent work demonstrating that in situ synchrotron studies can be used to map sample rod temperatures during heating, investigations were carried out to better understand how the sample environment affects the sample temperature profile. Through a combination of experimental studies and modeling efforts, it is shown that the environment in the furnace can strongly influence the sample temperature at the lamp focus, the steepness of the vertical temperature gradient, and the timescale required for sample heating and cooling - effects which can combine to produce a strong history-dependence to sample temperature profiles. It is demonstrated that the furnace effects can be effectively captured in thermal models, allowing both the steady-state and time-dependent behavior of the sample to be accurately reproduced with predictive models, and providing a launching point for improved furnace designs that can more readily deliver desired thermal profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterizing major and trace element compositions in fallout melt glass from a near-surface nuclear test

We report the chemical and isotopic compositions of fallout melt glasses from nuclear tests contain a range of information constraining the physical conditions within the fireball and the mechanisms of fallout formation but historic studies tended to exclude the behavior of stable major and trace elements. Here, we present a large study specifically focused on major and trace element relationships within a population of macroscale fallout samples from a single event. We interpret these data to better constrain how fallout melt glass formation in near surface environments is influenced by that environment and demonstrate how major and trace element abundances can provide useful insights into chemical processes within the fireball. Data confirm that the uranium in the fallout glass population derives from two isotopically distinct endmembers: isotopically enriched uranium (presumably from the weapon), and natural composition uranium that may be a combination of anthropogenic and environmental materials from within the blast zone. The similarity between major and trace element concentrations in fallout and corresponding local soils from the event site confirm the local soils as the most probable source of entrained material into the fireball and the source of carrier material into which the bomb vapor was incorporated. The lack of correlation between major and trace element abundances with size indicates that volatility driven processes, such as condensation from the fireball, do not control the composition of macroscale fallout melt glass. Although the fallout has major and trace element chemical characteristics broadly similar to those of the local, associated soils, some systematic differences are observed between the two populations. Fallout melt glass is depleted in volatile elements such as K, Na, Tl and Pb, consistent with heating to temperatures above ~1000 °C for 3-10 s. This is supported by the results of laser heating experiments performed on rhyolitic soil at temperatures (1600-2200 °C) and timescales (1-120 s) that are broadly relevant to fallout formation conditions. Relative enrichments of metals such as Cu and Co do not correlate with the abundance of uranium, suggesting that fallout also records input of near field anthropogenic materials. Our observations suggest that major chemical features can be related to processing in the fireball and used to inform the thermal-chemical evolution of the system. Ultimately, these data are consistent with a fallout formation mechanism that involves rapid melting of surface materials to form carrier material melts with minor incorporation of bomb vapor and a degree of volumetric volatile loss due to heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Risk assessment of wellbore leakage during underground hydrogen storage

The expansion of renewable energy sources would require large-scale energy storage options to overcome the intermittent nature of these sources. Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs offers a scalable and practical energy storage solution. These reservoirs are chosen for their availability and large capacity, but the unique properties of hydrogen raise concerns about potential leakage pathways, particularly through wellbores. In this study, we develop and apply, for the first time, reduced-order models (ROMs) specifically designed for efficient leakage risk prediction in UHS systems operating in depleted hydrocarbon reservoirs. Using 3,000 high-fidelity simulation scenarios, we examine the influence of 11 key parameters, including reservoir and aquifer depths, wellbore permeability and porosity, initial saturations of water, oil and gas fractions (hydrogen, light, intermediate, and heavy hydrocarbons), reservoir pressure multiplier, and the aquifer-to-reservoir volume ratio, to simulate leakage behavior over a 1,000-year timescale. We train ROMs using a two-step classification-regression approach, achieving R 2 values exceeding 99 % across all targets. These ROMs effectively capture the leakage evolution and identify critical controls of leakage, guiding the design of mitigation strategies. Results indicate that gas leakage occurs in about 27 % of scenarios as early as five years post-operation, reaching volumes of up to 106 ft3. Oil leakage is less frequent (~17 %) and typically begins decades later. Our findings also show that hydrogen often migrates first, owing to its smaller molecular size and higher buoyancy, followed by heavier hydrocarbons. Over time, these heavier components contribute significantly to the total leaked volume, reinforcing the need for targeted monitoring and remediation strategies. Our analysis highlights that deeper storage reservoirs, shallower aquifers, and low-permeability wellbores significantly reduce leakage risks. In conclusion, this work offers a robust framework for risk-informed UHS deployment, supporting energy security through reliable large-scale hydrogen storage while safeguarding environmental integrity.

08 HYDROGEN↗
DOE OSTI2022

The root-zone soil moisture spectrum in a mediterranean ecosystem

Storage of water within soil pores of the root zone introduce memory effects in the dynamics of soil moisture that are considerably longer than the integral timescale of many atmospheric processes. Thus, hydro-climatic states can be “sustained” through land-surface heat and water vapor fluxes primarily because they can “feed off” on this long-term soil moisture memory. Root-zone soil moisture memory is only but one feature characterizing the spectrum of soil moisture dynamics, which is analyzed here using a combination of long-term measurements and models. In particular, the spectrum of root-zone soil moisture content in a Mediterranean ecosystem is examined using 14-years of half-hourly measurements. A distinguishing hydro-climatic feature in such ecosystems is that sources (mainly rainfall) and sinks (mainly evapotranspiration) of soil moisture are roughly out of phase with each other. For over 4 decades of time scales and 7 decades of energy, the canonical shape of the measured soil moisture spectrum is shown to be approximately Lorentzian determined by the soil moisture variance and its memory but with two exceptions: the occurrences of a peak at diurnal-to-daily time scales and a weaker peak at near annual time scales. Model calculations and spectral analysis demonstrate that diurnal and seasonal variations in hydroclimate forcing responsible for variability in evapotranspiration had minor impact on the normalized shape of the soil moisture spectrum. However, their impact was captured by adjustments in the temporal variance. These findings indicate that precipitation and not evapotranspiration variability dominates the multi-scaling properties of soil moisture variability consistent with prior climate model simulations. Furthermore, the soil moisture memory inferred by the annual peak of soil moisture (340 d) is consistent with climate model simulations, while the memory evaluated from the loss function of a linearized mass balance approach leads to a smaller value (50 d), highlighting the effect of weak non-stationarity on soil moisture variability.

54 ENVIRONMENTAL SCIENCES↗