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DOE OSTI2022

Variational quantum algorithm for estimating the quantum Fisher information

The quantum Fisher information (QFI) quantifies the ultimate precision of estimating a parameter from a quantum state and can be regarded as a reliability measure of a quantum system as a quantum sensor. However, estimation of the QFI for a mixed state is in general a computationally demanding task. In this paper we present a variational quantum algorithm called variational quantum Fisher information estimation (VQFIE) to address this task. By estimating lower and upper bounds on the QFI, based on bounding the fidelity, VQFIE outputs a range in which the actual QFI lies. This result can then be used to variationally prepare the state that maximizes the QFI, for the application of quantum sensing. In contrast to previous approaches, VQFIE does not require knowledge of the explicit form of the sensor dynamics. We simulate the algorithm for a magnetometry setup and demonstrate the tightening of our bounds as the state purity increases. For this example, we compare our bounds with literature bounds and show that our bounds are tighter.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Long-run Marginal CO2 Emission Rates Workbooks for 2020 Standard Scenarios Cambium Data

This dataset has been superseded by a new set of workbooks that can be found here: https://data.nlr.gov/submissions/183 These workbooks contain modeled estimates of long-run marginal CO2 emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed. In addition to year-over-year data, the Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand. Three future scenarios are provided in separate workbooks: A Mid-case (i.e., business-as-usual), and two scenarios with relatively higher or lower renewable energy costs. For more details on these scenarios, see the Standard Scenarios 2020 Report ( https://www.nlr.gov/docs/fy21osti/77442.pdf ). For more data underlying each scenario, see the Standard Scenarios 2020 project (Cambium data) at https://cambium.nlr.gov/ . This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2020 ( https://www.nlr.gov/docs/fy21osti/78239.pdf ). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nlr.gov/analysis/cambium.html .

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale

Abstract Decarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from produced water (PW) sourced from the Marcellus Shale in Pennsylvania (PA). Statewide, Marcellus Shale PW has substantial extractable lithium, however, concentrations, production volumes and extraction efficiencies vary between the northeast and southwest operating zones. Annual estimates suggest statewide lithium mass yields of approximately 1160 (95% CI 1140–1180) metric tons (mt) per year. Production decline curve analysis on PW volumes reveal cumulative volumetric disparities between the northeast (median = 2.89 X 10 7 L/10-year) and southwest (median = 5.56 × 10 7 L/10-year) regions of the state, influencing lithium yield estimates of individual wells in southwest [2.90 (95% CI 2.80–2.99) mt/10-year] and northeast [1.96 (CI 1.86–2.07) mt/10-year] PA. Moreover, Mg/Li mass ratios vary regionally, where NE PA are low Mg/Li fluids, having a median Mg/Li mass ratio of 5.39 (IQR, 2.66–7.26) and SW PA PW is higher with a median Mg/Li mass ratio of 17.8 (IQR, 14.3–20.7). These estimates indicate substantial lithium yields from Marcellus PW, though regional variability in chemistry and production may impact recovery efficiencies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

First-Principles Approach for Assessing Cold Electron Injection Efficiency of Dye-Sensitized Solar Cell: Elucidation of Mechanism of Charge Injection and Recombination

The adequacy of the inclusion of spacer units in the metal-free D-π–A organic dyes pertaining to the augmentation of dye-sensitized solar cell (DSSC) efficiency has been examined through the excited-state simulations of the charge injection and recombination processes at the dye–semiconductor interface. Within the framework of the time-dependent density functional theory, the proposed computational studies focus on the precise evaluation of pivotal factors controlling the rates of photoinduced charge-transfer and energy-transfer processes, including electronic coupling, reorganization energy, and threshold energy barrier in the semiclassical Marcus formalism. The estimation of the fluorescent state appears to be the crucial step while explaining the ultrafast electron injection process and the charge recombination at the Marcus inverted region, as revealed by the obtained results. The retardation of charge recombination is facilitated by the insertion of a thiophene moiety between the π-bridge and the acceptor units. The estimated cold electron injection efficiencies deploying the Onsager–Braun theory, that rely on the computations of cold electron injection lifetime and cold electron lifetime, show a linear correlation with the experimental photovoltaic parameters of the DSSC comprising short-circuit current density, open-circuit voltage, and power conversion efficiency. The outcomes of the present investigation establish a basis for unraveling the mechanism of intricate dynamical processes upon photoexcitation of the sensitizers, as well as devising plausible routes for functional DSSC materials.

14 SOLAR ENERGY↗

DOE OSTI2020

Analyzing Wildland Fire Smoke Emissions Data Using Compositional Data Techniques

By conservation of mass, the mass of wildland fuel that is pyrolyzed and combusted must equal the mass of smoke emissions, residual char and ash. For a given set of conditions, these amounts are fixed. This places a constraint on smoke emissions data which violates statistical assumptions for many of the methods currently used to analyze these data such as linear regression, analysis of variance, and t-tests. These data are inherently multivariate and non-negative parts of a whole. This paper introduces the field of compositional data analysis to the emissions community and provides examples of appropriate statistical treatment of emissions data. It is shown that modified combustion efficiency should not be used as a predictor variable for other smoke emissions because it is not an independent variable. An alternative method based on compositional linear trends to estimate trace gas composition using CO and CO2 is presented. The data used in this paper resulted from projects the DOD/DOE/EPA Strategic 586 Environmental Research and Development Program projects RC-1648 and 1649. The senior 587 author appreciates the guidance and R scripts provided by Prof. Girty at San Diego State 588 University to estimate linear trends by perturbation. J. P.-A. was supported by the Spanish 589 Ministry of Science, Innovation and Universities under the project CODAMET (RTI2018-590 095518-B-C21, 2019-2021). The data used in this study have been previously published and are 591 available in the original publications. DRW conceived the initial manuscript (70 percent) and 592 performed the bulk of the data analysis. JPA provided statistical guidance and compositional data 593 expertise and contributed 20 percent of the manuscript. TJJ and HJ were extensively involved in 594 the study that provided the data. TJJ provide smoke emissions expertise and HJ provided 595 combustion expertise. The authors declare that they have no conflict of interest. The use of trade 596 or firm names in this publication is for reader information and does not imply endorsement by 597 the U.S. Department of Agriculture of any product or service.

simplex, compositional data analysis, balance, log↗

DOE OSTI2022

The eROSITA Final Equatorial-Depth Survey (eFEDS)

In 2019, the eROSITA telescope on board the Russian-German satellite Spectrum-Roentgen-Gamma (SRG) began to perform a deep all-sky X-ray survey with the aim of identifying ~100 000 clusters and groups over the course of four years. As part of its performance verification phase, a ~140 deg 2 survey, called eROSITA Final Equatorial-Depth Survey (eFEDS), was performed. With a depth typical of the all-sky survey after four years, it allows tests of tools and methods as well as improved predictions for the all-sky survey. As part of this effort, a catalog of 542 X-ray selected galaxy group and cluster candidates was compiled. Here, in this paper, we present the optical follow-up, with the aim of providing redshifts and cluster confirmation for the full sample. Furthermore, we aim to provide additional information on the dynamical state, richness, and optical center of the clusters. Finally, we aim to evaluate the impact of optical cluster confirmation on the purity and completeness of the X-ray selected sample. We used optical imaging data from the Hyper Suprime-Cam Subaru Strategic Program and from the Legacy Survey to identify optical counterparts to the X-ray detected cluster candidates. We make use of the multi-component matched filter cluster confirmation tool (MCMF), as well as of the optical cluster finder CAMIRA to derive cluster redshifts and richnesses. MCMF provided the probabilities with which an optical structure would be a chance superposition with the X-ray candidate. These probabilities were used to identify the best optical counterpart as well as to confirm an X-ray candidate as a cluster. The impact of this confirmation process on catalog purity and completeness was estimated using optical to X-ray scaling relations as well as simulations. The resulting catalog was furthermore matched with public group and cluster catalogs. Optical estimators of the cluster dynamical state were constructed based on density maps of the red-sequence galaxies at the cluster redshift. By providing redshift estimates for all 542 candidates, we construct an optically confirmed sample of 477 clusters and groups with a residual contamination of 6%. Of these, 470 (98.5%) are confirmed using MCMF, and 7 systems are added through cross-matching with spectroscopic group catalogs. Using observable-to-observable scaling and the applied confirmation threshold, we predict that 8 ± 2 real systems have been excluded with the MCMF cut required to build this low-contamination sample. This number agrees well with the 7 systems found through cross-matching that were not confirmed with MCMF. The predicted redshift and mass distribution of this catalog agree well with simulations. Thus, we expect that these 477 systems include >99% of all true clusters in the candidate list. Using an MCMF-independent method, we confirm that the catalog contamination of the confirmed subsample is 6 ± 3%. Application of the same method to the full candidate list yields 17 ± 3%, consistent with estimates coming from the fraction of confirmed systems of ~17% and with expectations from simulations of ~20%. We also present a sample of merging cluster candidates based on the derived estimators of the cluster dynamical state.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Global kinetic modeling of NH 3 -SCR with two sites of NH 3 storage on Cu-SSZ-13

A comprehensive selective catalytic reduction (SCR) model is developed to detail the global kinetic reactions in the catalyst which is involved in two type of sites for high- and low-temperature NH 3 adsorption/desorption, as well as a low temperature H 2 O storage. The model accounts for the formation of ammonium nitrate in fast SCR and the decomposition formation of ammonium nitrate in the absence of NO, and addresses N 2 O relative to NO and NO 2 reactions with NH 3 . The model has been validated against commercial Cu-SSZ-13 measurements of NH 3 inventory, NH 3 oxidation, NO oxidation, standard SCR, fast SCR and NO 2 SCR at both comprehensive steady-state and transient conditions. In addition, the application of the SCR model for emissions control over a transient drive cycle at cold start was demonstrated based on diesel oxidation catalyst (DOC) and SCR aftertreatment system for a passenger car. In conclusion, the results show that the SCR model can provide the detailed emissions estimations for steady-state and transient conditions, can assist with understanding the impact of the SCR reaction mechanism, and can assist with the design and optimization of SCR catalysts to develop innovative technologies for co-optimization of engine fuel economy and emissions control technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Fast-forwarding quantum simulation with real-time quantum Krylov subspace algorithms

Quantum subspace diagonalization (QSD) algorithms have emerged as a competitive family of algorithms that avoid many of the optimization pitfalls associated with parameterized quantum circuit algorithms. While the vast majority of the QSD algorithms have focused on solving the eigenpair problem for ground, excited-state, and thermal observable estimation, there has been a lot less work in considering QSD algorithms for the problem of quantum dynamical simulation. In this work, we propose several quantum Krylov fast-forwarding (QKFF) algorithms capable of predicting long-time dynamics well beyond the coherence time of current quantum hardware. Our algorithms use real-time evolved Krylov basis states prepared on the quantum computer and a multi-reference subspace method to ensure convergence towards high-fidelity, long-time dynamics. In particular, we show that the proposed multi-reference methodology provides a systematic way of trading off circuit depth with classical post-processing complexity. Further, we also demonstrate the efficacy of our approach through numerical implementations for several quantum chemistry problems including the calculation of the auto-correlation and dipole moment correlation functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

The Role of Interfacial Thermal Resistance in Li-Ion Battery Thermal Management: Preprint

Temperature critically affects the performance, life and safety of lithium-ion batteries. Therefore, it is essential to understand heat generation and dissipation within individual battery cells and battery packs to plan a proper thermal management strategy. One of the key challenges is that interfacial heat transfer of a battery unit is difficult to quantify. The steady-state absolute method and the transient laser-flash-diffusivity method were employed to measure heat conductivities of battery layer stacks and individual battery layer separately. Results show flash diffusivity method gives higher thermal conductivity at both cross-plane and in-plane directions. The difference is primarily caused by interfacial thermal resistance so that it can be estimated by steady-state and transient measurements. To investigate the effects of interfacial thermal transport beyond individual cell level, a multiphysics battery model is used. The model is built upon a multi-scale multi-domain modeling framework for battery packs that accounts for the interplay across multiple physical phenomena. Benefits of a battery module using thermal management materials are quantified through numerical experiments. During a thermal runaway event, it is found interfacial thermal resistance can mitigate thermal runaway in a battery module by significantly reducing heat transfer between cells.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Long-Run Marginal CO 2 e Emission Rates for End-Use Electricity Consumption in the State of Washington [Slides]

This analysis contains an estimate of the long-run marginal emission rate for the electric sector in the state of Washington. The long-run marginal emission rate is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. The metric explicitly takes into account both the underlying evolution of the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which also identifies a marginal emission rate but treats the grid assets as fixed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Confocal Raman Microscopy as an In Situ Probe of Volume Change in Hydration-Sensitive Polymer Membranes

An approach is described for measuring hydration-induced volume change within ion conductive polymer membranes and thin films by adapting the technique of confocal Raman microscopy. With careful consideration of factors that affect excitation and scattering within the confocal probe volume region, material swelling and deswelling were estimated from the intensities of polymer matrix spectral features. A high numerical aperture oil-immersion objective was used to achieve tight focusing within samples and the efficient collection of Raman scattered light. The approach is demonstrated for a fluorinated cation-exchange ionomer (Nafion) and the hydrocarbon anion-exchange ionomer Sustainion. Samples were monitored while under a nitrogen atmosphere that was cycled between dry and humid (∼50% relative humidity, RH) states. The volume changes estimated from the Raman spectra were in close agreement with those derived from conventional measurements. The reported work advances understanding needed to adapt confocal Raman microscopy for quantitative in situ and operando studies of ionomers within electrochemical devices, such as polymer electrolyte membrane fuel cells and electrolyzers, and lays a foundation for broader applications in the study of hydration-induced polymer swelling and the associated molecular level water and polymer framework structural changes.

Absorption↗

DOE OSTI2025

Universality of Rényi Entropy in Conformal Field Theory

We use the thermal effective theory to prove that, for the vacuum state in any conformal field theory in 𝑑 dimensions, the 𝑛th Rényi entropy 𝑆$^{(𝑛)}_{𝐴}$ behaves as 𝑆$^{(𝑛)}_{𝐴}$ = [𝑓⁡/(2⁢𝜋⁢𝑛) 𝑑−1 ]⁢[Area⁡(∂𝐴)/(𝑑−2)⁢𝜀 𝑑−2 ]⁢(1+𝑂⁡(𝑛)) in the 𝑛 → 0 limit when the boundary of the entanglement domain 𝐴 is spherical with the UV cutoff 𝜀. The theory dependence is encapsulated in the cosmological constant 𝑓 in the thermal effective action. Using this result, we estimate the density of states for large eigenvalues of the modular Hamiltonian for the domain 𝐴. In two dimensions, we can use the hot spot idea, which describes the effective action in the high-temperature limit when the temperature is position-dependent, to derive more powerful formulas valid for arbitrary positive 𝑛. We discuss the difference between two and higher dimensions and clarify the applicability of the hot spot idea. We also use the thermal effective theory to derive an analog of the Cardy formula for boundary operators in higher dimensions.

Conformal field theory↗

DOE OSTI2022

CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms

Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum Algorithms (VQAs). VQAs rely upon the iterative optimization of a parameterized unitary circuit (ansatz) with respect to an objective function. Since quantum machines are noisy and expensive resources, it is imperative to classically choose the VQA ansatz initial parameters to be as close to optimal as possible to improve VQA accuracy and accelerate their convergence on today’s devices. This work tackles the problem of finding a good ansatz initialization, by proposing CAFQA, a Clifford Ansatz For Quantum Accuracy. The CAFQA ansatz is a hardware-efficient circuit built with only Clifford gates. In this ansatz, the parameters for the tunable gates are chosen by searching efficiently through the Clifford parameter space via classical simulation. The resulting initial states always equal or outperform traditional classical initialization (e.g., Hartree-Fock), and enable high-accuracy VQA estimations. CAFQA is well-suited to classical computation because: a) Clifford-only quantum circuits can be exactly simulated classically in polynomial time, and b) the discrete Clifford space is searched efficiently via Bayesian Optimization. For the Variational Quantum Eigensolver (VQE) task of molecular ground state energy estimation (up to 18 qubits), CAFQA’s Clifford Ansatz achieves a mean accuracy of nearly 99% and recovers as much as 99.99% of the molecular correlation energy that is lost in Hartree-Fock initialization. CAFQA achieves mean accuracy improvements of 6.4x and 56.8x, over the state-of-the-art, on different metrics. Here, the scalability of the approach allows for preliminary ground state energy estimation of the challenging chromium dimer (Cr2) molecule. With CAFQA’s high-accuracy initialization, the convergence of VQAs is shown to accelerate by 2.5x, even for small molecules. Furthermore, preliminary exploration of allowing a limited number of non-Clifford (T) gates in the CAFQA framework, shows that as much as 99.9% of the correlation energy can be recovered at bond lengths for which Clifford-only CAFQA accuracy is relatively limited, while remaining classically simulable.

bayesian optimization↗

DOE OSTI2023

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.

42 ENGINEERING↗

DOE OSTI2024

The Dynamic Assimilation Technique measures photosynthetic CO 2 response curves with similar fidelity to steady-state approaches in half the time

The net CO 2 assimilation (A) response to intercellular CO 2 concentration (C i ) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/C i protocols rely on steady-state measurements and take 15–40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO 2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO 2 protocols in steady-state measurements, and tested whether different CO 2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO 2 protocols, with slight differences in Rubisco carboxylation limitation. The A/C i curves were not affected by the direction of the change of CO 2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (V cmax ), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Combined Force-Frequency Sampling for Simulation of Systems Having Rugged Free Energy Landscapes.

An adaptive, machine learning-based sampling method is presented for simulation of systems having rugged, multidimensional free energy landscapes. The method's main strength resides in its ability to learn both from the frequency of visits to distinct states and the generalized force estimates that arise in a system as it evolves in phase space. This is accomplished by introducing a self-integrating artificial neural network, which generates an estimate of the free energy directly from its derivatives. The usefulness of the proposed combined approach is examined in the context of two concrete examples, namely, an alanine dipeptide molecule in water and a polymer diffusing through a narrow pore. This new method is found to be robust, faster, and more accurate than approaches that rely only on frequency-based or generalized force-based estimations. After combining the proposed approach with overfill protection and support for sparse data storage and training, the method is shown to be more effective than comparable, previously available techniques and capable of scaling efficiently to larger numbers of collective variables.

Sevgen, Emre↗

DOE OSTI2026

Kinetics vs Thermodynamics: Engineering Photoredox Reactivity from an Upper Excited State of Fe II

Ultrafast deactivation of metal-to-ligand charge-transfer (MLCT) states into low-lying metal-centered states has long limited the utility of first-row transition-metal complexes in a broad range of applications, including photoredox catalysis. Here we bypass such limitations using a Fe II -pyridinium carbene complex to enable electron transfer reactivity directly from a higher-lying MLCT state. Time-resolved spectroscopic data revealed that the higher-energy MLCT manifold relaxes to lower-lying metal-centered states with a time constant of ca. 3 ps. The nature of the metal-centered (MC) excited state as a 3 MC (i.e., S = 1) was inferred from variable-temperature transient absorption studies. These experiments also revealed that the ground-state recovery process occurs at or very near the “Marcus barrierless regime”, thereby allowing for an estimate of its excited-state redox potential. The detailed picture of the energetics of this compound that emerged enabled fine-tuning of competing thermodynamic and kinetic pathways to effect electron transfer from the higher-energy MLCT excited state prior to relaxation to the ligand-field manifold. We believe these results open an unexplored landscape for the use of earth-abundant first-row transition-metal-based chromophores for applications in light-to-chemical energy conversion.

charge transfer↗

DOE OSTI2021

Estimating and Forecasting Time-Varying Groundwater Recharge in Fractured Rock: A State-Space Formulation with Preferential and Diffuse Flow to the Water Table

Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real-time to estimate time-varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base-flow recession, unsaturated infiltration models, or Water-Table Fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real-time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics-based State-Space (SS) model of one-dimensional infiltration to estimate recharge, which includes preferential and diffuse-flow to the water table. The model can take advantage of real-time data for water-table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water-table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily versus sub-daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.

groundwater, Recharge, infiltration, recursive est↗