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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Shutdown Dose Rate Analysis Using the Multi-Step CADIS Method

The Multi-Step Consistent Adjoint Driven Importance Sampling (MS-CADIS) hybrid Monte Carlo (MC)/deterministic radiation transport method was proposed to speed up the shutdown dose rate (SDDR) neutron MC calculation using an importance function that represents the neutron importance to the final SDDR. This work applied the MS-CADIS method to the ITER SDDR benchmark problem. The MS-CADIS method was also used to calculate the SDDR uncertainty resulting from uncertainties in the MC neutron calculation and to determine the degree of undersampling in SDDR calculations because of the limited ability of the MC method to tally detailed spatial and energy distributions. The analysis that used the ITER benchmark problem compared the efficiency of the MS-CADIS method to the traditional approach of using global MC variance reduction techniques for speeding up SDDR neutron MC calculation. Compared to the standard Forward-Weighted-CADIS (FW-CADIS) method, the MS-CADIS method increased the efficiency of the SDDR neutron MC calculation by 69%. The MS-CADIS method also increased the fraction of nonzero scoring mesh tally elements in the space-energy regions of high importance to the final SDDR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Computational Evaluation of Neutron Multiplicity Measurements of Polyethylene-Reflected Plutonium Metal

Simulations of neutron multiplicity measurements of a highly multiplicative plutonium sphere measured with a moderated array of 3 He proportional counters have consistently overpredicted the mean and variance of the measured multiplicity distribution. In contrast, identical experiments using a 252 Cf source have been accurately simulated. This paper outlines a sensitivity analysis of several key parameters that could account for the overprediction in the simulation of the plutonium sphere. Parameters that were analyzed include source-detector distance, detector dead time, variations in density and volume of the plutonium, and the value of v for 239 Pu-induced fission. Of these parameters, the only factor that accounted for the overprediction within reasonable bounds was a change in the value of the 239 Pu $\bar{v}$ . The sensitivity analysis showed that a small change (1.14% reduction) in the value of $\bar{v}$ dramatically improved the simulated results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Achievable Accuracy and Precision of Timing with Dynasen Piezo Electric Pins

Piezo electric pins from Dynasen are often used in the shock and detonation community for triggering or timing purposes. The purpose of this memorandum is to document factors that affect the accuracy and precision that can be reasonably expected from using the pins. Three different pin types were used and repetition of ablative laser pulses were used to measure the time variation both for the same pin and similar pins from a lot. It was established that the standard deviation for a common shock between all pins was 190 ps and that the maximum variance between any two similar pins was 960 ps. Thus it is clear that uncertainty in the pin location, cable lengths and electrical signal analysis are likely to affect accuracy and precision from piezo pin-based measurements far more than inherent pin-to-pin variation. However, these measurements also demonstrate that with suffcient experimental care, these off-the-shelf piezo pins are capable of nanosecond accuracy and precision.

42 ENGINEERING↗
DOE OSTI2013

A GENERALIZED STOCHASTIC COLLOCATION APPROACH TO CONSTRAINED OPTIMIZATION FOR RANDOM DATA IDENTIFICATION PROBLEMS

We present a scalable, parallel mechanism for stochastic identification/control for problems constrained by partial differential equations with random input data. Several identification objectives will be discussed that either minimize the expectation of a tracking cost functional or minimize the difference of desired statistical quantities in the appropriate $L^p$ norm, and the distributed parameters/control can both deterministic or stochastic. Given an objective we prove the existence of an optimal solution, establish the validity of the Lagrange multiplier rule and obtain a stochastic optimality system of equations. The modeling process may describe the solution in terms of high dimensional spaces, particularly in the case when the input data (coefficients, forcing terms, boundary conditions, geometry, etc) are affected by a large amount of uncertainty. For higher accuracy, the computer simulation must increase the number of random variables (dimensions), and expend more effort approximating the quantity of interest in each individual dimension. Hence, we introduce a novel stochastic parameter identification algorithm that integrates an adjoint-based deterministic algorithm with the sparse grid stochastic collocation FEM approach. This allows for decoupled, moderately high dimensional, parameterized computations of the stochastic optimality system, where at each collocation point, deterministic analysis and techniques can be utilized. The advantage of our approach is that it allows for the optimal identification of statistical moments (mean value, variance, covariance, etc.) or even the whole probability distribution of the input random fields, given the probability distribution of some responses of the system (quantities of physical interest). Our rigorously derived error estimates, for the fully discrete problems, will be described and used to compare the efficiency of the method with several other techniques. Numerical examples illustrate the theoretical results and demonstrate the distinctions between the various stochastic identification objectives.

Webster, Clayton G↗
DOE OSTI2016

Uncertainty Quantification and Sensitivity Analysis Applications to Fuel Performance Modeling

Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. The rodlet is representative of a BWR fuel rod. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlight the importance of quantifying the uncertainty and sensitivity in fuel performance modeling predictions and the need for additional research into improving the material models that are currently available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Covariance of CMB anomalies

Several unexpected features are observed at large angular scales in the cosmic microwave background (CMB) temperature anisotropy measurements by both WMAP and Planck. These include the lack of both variance and correlation, alignment of the lowest multipole moments with one another, hemispherical power asymmetry, and an odd-to-even parity excess. In this work, we study the statistics of eight representative large-angle CMB features in order to evaluate their covariance in the standard Λ CDM model. We do so using two sets of simulated CMB temperature maps; an ensemble of 100 000 simple Gaussian simulations, and 1000 Full Focal Plane (FFP) simulations provided by the Planck collaboration. In measuring feature probabilities, we pay particular attention to analysis choices, making sure that we can reproduce previous results in the literature, and explain differences where appropriate. The covariance structure we find is consistent with expectations given that many of the features studied are functions of the angular power spectrum. Notably, we find significant differences in the covariance entries associated with the quadrupole-octopole alignments derived from the Gaussian and FFP simulations. We additionally perform a principal component analysis to quantitatively gauge what combinations of features capture the most information about how simulation measurements vary, and to provide an alternative assessment of the ways in which the real sky is anomalous. Here, the first four principal components explain about 90% of the simulations’ variance, with the first two roughly quantifying the lack of large-angle correlations, and the next two quantifying the phase-dependent anomalies (multipole alignments and power asymmetry). Though the results of this analysis are fairly unsurprising, its comprehensive approach serves to tie together a number of previous results, and will therefore provide context for future studies of large-angle anomalies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Analysis of land-based CSEM data for CO2 monitoring at Bell Creek, MT

Monitoring subsurface fluid migration is critical to any CO2 injection operation like enhanced oil recovery or carbon storage. The controlled-source electromagnetic (CSEM) method provides a promising low-cost, non-invasive alternative to conventional monitoring methods. To investigate the use of CSEM for monitoring CO2 injection, we are conducting a time-lapse charged well casing CSEM survey at the Bell Creek oil field. There, CO2 is injected into the reservoir for enhanced oil recovery, which also results in incidental carbon storage. From October 2017 to October 2018 we completed three field campaigns, and we plan to complete one more in July 2019. We have analyzed the signal strength and variance of the time-lapse CSEM data. Difference data signal strength well exceeds the standard deviation of the data, and it agrees with estimates of expected signal strength derived from numerical modeling. This analysis indicates that our survey can detect the change in conductivity within the reservoir due to fluid movement.

time-lapse, land CSEM, monitoring, processing↗
DOE OSTI2023

Analysis of the MUSIC 3 He Multiplicity Data

A measurement campaign called the Measurement of Uranium Subcritical and Critical (MUSiC) was performed on a range of configurations of highly-enriched uranium (HEU) from December 2020 through April of 2021. While part of the focus was to measure reactor kinetics parameters on delayed supercritical systems, an additional focus was performing neutron noise measurements on subcritical configurations from deeply subcritical to nearly delayed critical. Multiple detector systems were used to perform these measurements, such as a 3 He multiplicity detector called the Neutron Multiplicity Array Detector (NoMAD) and a liquid scintillator system called the Rossi-α Measurement Rapid Organic Discriminating Detector (RAM-RODD). Also included were a scintillator system from the University of Michigan and a set of small 3He tubes that have previously been used to measure Rossi-α values on near-critical systems. The focus of this paper will be a comparison of prospective analysis methods for the NoMAD measurements. Previous subcritical measurements at the National Criticality Experiments Research Center (NCERC) submitted to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) used the Hage-Cifarelli formalism of the Feynman Variance-to-Mean method. This relies on the time correlations of neutron detections to infer the spontaneous fission rate and neutron multiplication of a system through binning the time tagged detections and analyzing resulting histograms of the numbers of counts. However, there are other neutron noise methods that rely on similar processes, such as the Hansen-Dowdy formalism which uses a slightly different methodology to extract multiplication from the neutron multiplicity counting moments. Comparisons can be made between these experimental results and those obtained through simulations to validate or identify deficiencies in analysis, detection methods, or the underlying nuclear data. Different time gating strategies and their effects on count rate uncertainties are also investigated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗
DOE OSTI2015

Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. First, we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding. The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the Rössler system and find that periodic dynamics translate to ring structures whereas chaotic time series translate to band or tube-like structures—thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore, we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length, and network diameter are highly sensitive to the interior crisis captured in this particular data set.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

On the unsteady Darcy–Forchheimer–Brinkman equation in local and nonlocal tumor growth models

A mathematical analysis of local and nonlocal phase-field models of tumor growth is presented that includes time-dependent Darcy–Forchheimer–Brinkman models of convective velocity fields and models of long-range cell interactions. A complete existence analysis is provided. In addition, a parameter-sensitivity analysis is described that quantifies the sensitivity of key quantities of interest to changes in parameter values. Two sensitivity analyses are examined; one employing statistical variances of model outputs and another employing the notion of active subspaces based on existing observational data. Remarkably, the two approaches yield very similar conclusions on sensitivity for certain quantities of interest. The work concludes with the presentation of numerical approximations of solutions of the governing equations and results of numerical experiments on tumor growth produced using finite element discretizations of the full tumor model for representative cases.

Mathematics↗
DOE OSTI2017

Modified GMDH-NN algorithm and its application for global sensitivity analysis

Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages. - Highlights: • The GMDH-NN is improved to construct the explicit polynomial model of optimal complexity by self-organization. • The paper aims at combining improved GMDH-NN with HDMR expansions and using it to compute Sobol' indices directly. • The method can be applied in uniform, normal and exponential distribution by using suitable orthogonal polynomials. • Engineering examples, e.g., electronic circuit models can be solved by the presented method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Performance Analysis of the Expected Track Length Estimator

This work investigates whether performance of the MCNP code is improved by the implementation of the expected track length estimator. The expected track length estimator is known to decrease variance when compared to the track length estimator in many classes of Monte Carlo particle transport simulations. However, the computational cost of computing an exponent for every particle collision within a tallied cell has previously been considered prohibitively expensive. Therefore, the goal of this work is to determine whether the variance improvements seen by the expected track length estimator in most cases makes up for the increase in transport time when implemented in the MCNP code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

MJO in Different Orbital Regimes: Role of the Mean State in the MJO’s Amplitude during Boreal Winter

Abstract The Madden–Julian oscillation (MJO) exhibits pronounced seasonality, with one of the key unanswered questions being the following: what controls the maximum in MJO precipitation variance in the Southern Hemisphere during boreal winter? In this study, we examine a set of global climate model simulations in which the eccentricity and precession of Earth’s orbit are altered to change the boreal winter mean state in an attempt to reveal the processes that are responsible for the MJO’s amplitude in the boreal winter. In response to the forced insolation changes, the north–south asymmetry in sea surface temperature is amplified in boreal fall, which intensifies the Hadley circulation in boreal winter. The stronger Hadley circulation yields higher mean precipitation and stronger mean lower-tropospheric westerlies in the southern part of the Indo-Pacific warm pool. The MJO precipitation variability increases significantly where the mean precipitation and lower-tropospheric westerlies strengthen. In the column-integrated moisture budget of the simulated MJO, only surface latent heat flux feedback shows a trend that is consistent with the MJO’s amplitude, suggesting an important role for the surface latent heat flux feedback in the MJO’s amplitude during the boreal winter. An analysis of the moisture–precipitation relationship in the simulations shows that the increase in the mean precipitation lowers the convective moisture adjustment time scale, leading to the increase in precipitation variance. Our results suggest that the mean-state precipitation plays a critical role in the maintenance mechanism of the MJO.

Meteorology & Atmospheric Sciences↗
DOE OSTI2016

Uncertainty quantification and sensitivity analysis applications to fuel performance modeling

Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions. Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation. Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem. The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway. The rodlet is representative of a BWR fuel rod. The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling). Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerline temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlighted the importance of the thermal conductivity of the fuel, the thermal conductivity of the cladding, the gap thickness, the fuel thermal expansion, and the system power factor as influential parameters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

DETECTION OF PLASMA FLUCTUATIONS IN WHITE-LIGHT IMAGES OF THE OUTER SOLAR CORONA: INVESTIGATION OF THE SPATIAL AND TEMPORAL EVOLUTION

This work focuses on the first results from the identification and characterization of periodic plasma density fluctuations in the outer corona, observed in STEREO-A COR1 white-light image time series. A two-dimensional reconstruction of the spatial distribution and temporal evolution of the coronal fluctuation power has been performed over the whole plane of the sky, from 1.4 to 4.0 R{sub Sun }. The adopted diagnostic tool is based on wavelet transforms. This technique, with respect to the standard Fourier analysis, has the advantage of localizing non-persistent fluctuating features and exploring variations of the relating wavelet power in both space and time. The map of the variance of the coronal brightness clearly outlines intermittent spatially coherent fluctuating features, localized along, or adjacent to, the strongest magnetic field lines. In most cases, they do not correspond to the visible coronal structures in the brightness maps. The results obtained provide a scenario in which the solar corona shows quasi-periodic, non-stationary density variations characterized by a wide range of temporal and spatial scales and strongly confined by the magnetic field topology. In addition, structures fluctuating with larger power are larger in size and evolve more slowly. The characteristic periodicities of the fluctuations are comparable to their lifetimes. This suggests that plasma fluctuations lasting only one or two wave periods and initially characterized by a single dominant periodicity either rapidly decay into a turbulent mixed flow via nonlinear interactions with other plasma modes, or they are damped by thermal conduction. The periodic non-stationary coronal fluctuations outlined by the closed field lines at low and mid latitudes might be associated with the existence of slow standing magneto-acoustic waves excited by the convective supergranular motion. The fluctuating ray-like structures observed along open field lines appear to be linked either to the intermittent nature of the processes underlying the generation of magnetic reconnection in the polar regions or to the oscillatory transverse displacements of the coronal ray itself.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Prostate-Specific Antigen at 4 to 5 Years After Low-Dose-Rate Prostate Brachytherapy Is a Strong Predictor of Disease-Free Survival

Purpose: To determine (1) the prognostic utility of prostate-specific antigen (PSA) concentration at 45 to 60 months (48mPSA) after low-dose-rate prostate brachytherapy (LDR-PB); (2) the predictors of 48mPSA; and (3) the prognostic utility of directional trends between PSA levels at 24, 36, and 48 months after LDR-PB. Methods and Materials: Between 1998 and 2008, 2223 patients with low- and intermediate-risk prostate cancer received LDR-PB monotherapy. A cohort of 1434 of these patients was identified with a documented 48mPSA and no evidence of disease relapse prior to the 48mPSA. In addition, a subset of this cohort (n=585) was identified with ≥72 months of follow-up and documented PSA values at both 24 and 36 months after implantation. Results: Median follow-up time was 76 months. Eight-year Kaplan-Meier disease-free survival (DFS) rates were 100% vs 73.4% for patients with 48mPSA ≤0.2 vs those with >0.2 ng/mL; 99.1% versus 53.8% for a 48mPSA threshold of ≤0.4 versus >0.4 ng/mL, respectively; and 97.3% versus 0% for a threshold of ≤1.0 versus >1.0 ng/mL, respectively. On multivariate analysis, the only factor predictive of DFS was 48mPSA (P<.0001). On subset analysis (n=585), 29 patients had a PSA rise (defined as >0.2 ng/mL) between 24 and 36 months, 24 patients had a rise between 36 and 48 months, and 11 patients had rises over both intervals. Failure rates in these patients were 52%, 79%, and 100%, respectively. On multivariate analysis, initial PSA, androgen deprivation therapy, and dose to 90% of the prostate significantly correlated with 48mPSA but together accounted for only ∼5% of its total variance. Conclusions: The 48mPSA after LDR-PB is highly predictive of long-term DFS. Patients with 48mPSA ≤0.4 ng/mL had a <1% risk of disease relapse at 8 years, whereas all patients with 48mPSA >1.0 ng/mL relapsed. Consecutive PSA rises of >0.2 ng/mL from 24 to 36 months and from 36 to 48 months were also highly predictive of subsequent failure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds

Atmospheric rivers (ARs) can significantly modulate surface hydrological processes through the extreme precipitation they produce. Previously, studies have investigated the impact of ARs on individual hydrological processes, but a comprehensive evaluation of ARs’ impact on surface hydrology is lacking. This study used a high-resolution regional climate simulation to quantify the impact of ARs on surface hydrological processes across the western U.S. watersheds. The model performance is evaluated through extensive comparison against observations. Our analysis indicates that ARs produce heavy precipitation but suppress evapotranspiration. Snowpack ablates more during ARs, which is mainly driven by warmer air temperature and increased downward longwave radiation. At the 0oC to 10oC temperature range, ARs increase the probability of snow ablation from 0.33 to 0.57. Soil moisture responds similarly to AR and non-AR precipitation events. The runoff-to-precipitation ratio almost doubles due to modulation by heavy precipitation in the southwestern watersheds or intensification of snow ablation in the northwestern watersheds during AR events compared to non-AR events. From analysis of the relationship between the hydrological responses and different meteorological factors, precipitation, temperature and radiation were identified as the key drivers that distinguish the hydrologic responses between AR and non-AR events. Lastly, analysis of ARs and total runoff at annual scale and April 1st snowpack and winter precipitation shows that ARs explain 30% to 60% of the interannual variance and sharpen the seasonality of water resources availability in the west coast mountain watersheds.

54 ENVIRONMENTAL SCIENCES↗