Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Statistical Testing Approach”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Validation of the DESI DR2 measurements of baryon acoustic oscillations from galaxies and quasars

The Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) galaxy and quasar clustering data represents a significant expansion of data from Data Release 1 (DR1), providing improved statistical precision in baryon acoustic oscillation (BAO) constraints across multiple tracers, including bright galaxies, luminous red galaxies, emission line galaxies, and quasars. In this paper, we validate the BAO analysis of DR2. We present the results of robustness tests on the blinded DR2 data and, after unblinding, consistency checks on the unblinded DR2 data. All results are compared with those obtained from a suite of mock catalogs that replicate the selection and clustering properties of the DR2 sample. We confirm the consistency of DR2 BAO measurements with DR1 while achieving a reduction in statistical uncertainties due to the increased survey volume and completeness. The combined BAO precision, including both statistical and systematic errors, improves from ∼0.52% in DR1 to 0.30% in DR2—a factor of 1.7 gain. We assess the impact of analysis choices, including different data vectors (correlation function vs power spectrum), modeling approaches and systematics treatments, and an assumption of the Gaussian likelihood, finding that our BAO constraints are stable across these variations and assumptions with a few minor refinements to the baseline setup of the DR1 BAO analysis. We summarize a series of pre-unblinding tests that confirmed the readiness of our analysis pipeline, the final systematic errors, and the DR2 BAO analysis baseline. The successful completion of these tests led to the unblinding of the DR2 BAO measurements, ultimately leading to the DESI DR2 cosmological analysis, with their implications for the expansion history of the Universe and the nature of dark energy presented in the DESI key paper (companion paper).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Methods for Bayesian power spectrum inference with galaxy surveys

We derive and implement a full Bayesian large scale structure inference method aiming at precision recovery of the cosmological power spectrum from galaxy redshift surveys. Our approach improves upon previous Bayesian methods by performing a joint inference of the three-dimensional density field, the cosmological power spectrum, luminosity dependent galaxy biases, and corresponding normalizations. We account for all joint and correlated uncertainties between all inferred quantities. Classes of galaxies with different biases are treated as separate subsamples. This method therefore also allows the combined analysis of more than one galaxy survey. In particular, it solves the problem of inferring the power spectrum from galaxy surveys with non-trivial survey geometries by exploring the joint posterior distribution with efficient implementations of multiple block Markov chain and Hybrid Monte Carlo methods. Our Markov sampler achieves high statistical efficiency in low signal-to-noise regimes by using a deterministic reversible jump algorithm. This approach reduces the correlation length of the sampler by several orders of magnitude, turning the otherwise numerically unfeasible problem of joint parameter exploration into a numerically manageable task. We test our method on an artificial mock galaxy survey, emulating characteristic features of the Sloan Digital Sky Survey data release 7, such as its survey geometry and luminosity-dependent biases. These tests demonstrate the numerical feasibility of our large scale Bayesian inference frame work when the parameter space has millions of dimensions. This method reveals and correctly treats the anti-correlation between bias amplitudes and power spectrum, which are not taken into account in current approaches to power spectrum estimation, a 20% effect across large ranges in k space. In addition, this method results in constrained realizations of density fields obtained without assuming the power spectrum or bias parameters in advance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

STATISTICAL ANALYSIS OF INTERFEROMETRIC MEASUREMENTS OF AXIS RATIOS FOR CLASSICAL Be STARS

This work presents a novel method to estimate the effective opening angle of CBe star disks from projected axis ratio measurements, obtained by interferometry using a statistical approach. A Monte Carlo scheme was used to generate a large set of theoretical axis ratios from disk models using different distributions of disk densities and opening angles. These theoretical samples were then compared to observational samples, using a two-sample Kolmogorov-Smirnov test, to determine which theoretical distribution best reproduces the observations. The results suggest that the observed ratio distributions in the K, H, and N band can best be explained by the presence of thin disks, with opening half-angles of the order of 0.°15-4.°0. Results for measurements over the Hα line point toward slightly thicker disks, 3.°7-14°, which is consistent with a flaring disk predicted by the viscous disk model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey quasar sample: testing observational systematics on the Baryon Acoustic Oscillation measurement

ABSTRACT Baryon Acoustic Oscillations (BAO) are considered to be a very robust standard ruler against various systematics. This premise has been tested against observational systematics, but not to the level required for the next generation of galaxy surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid. In this paper, we investigate the effect of observational systematics on the BAO measurement of the final sample of quasars from the extended Baryon Oscillation Spectroscopic Survey Data Release 16 in order to prepare and hone a similar analysis for upcoming surveys. We employ catalogues with various treatments of imaging systematic effects using linear and neural network-based non-linear approaches and consider how the BAO measurement changes. We also test how the variations to the BAO fitting model respond to the observational systematics. As expected, we confirm that the BAO measurements obtained from the DR16 quasar sample are robust against imaging systematics well within the statistical error, while reporting slightly modified constraints that shift the line-of-sight BAO signal by less than 1.1 per cent. We use realistic simulations with similar redshift and angular distributions as the DR16 sample to conduct statistical tests for validating the pipeline, quantifying the significance of differences, and estimating the expected bias on the BAO scale in future high-precision data sets. Although we find a marginal impact for the eBOSS QSO data, the work presented here is of vital importance for constraining the nature of dark energy with the BAO feature in the new era of big data cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Emergent magnetic ordering and topological frustration in quasicrystal artificial spin ices

Artificial spin ices (ASIs) are nanoscale geometrically engineered arrays of magnetic bars or islands that display magnetic frustration and provide a fertile system for modeling and testing many complex statistical mechanics theories and emergent phenomena. Aperiodic ASIs display spatially varying magnetic frustration as a result of the aperiodicity. We have explored the magnetization reversal of quasicrystalline ASIs (QC-ASIs) patterned on P2 and P3 Penrose tilings, using a statistical graph-theory approach that enables quantitative comparison between the two tilings. Our approach offers insight into the QC-ASIs. We show that the P2 tiling leads to enhanced topological frustration compared to the P3 tiling, and we elucidate the emergence of local antiferromagnetic ordering as well as disordered states in these ferromagnetic aperiodic lattices. Furthermore our method enables quantitative exploration of frustration behavior on any periodic or aperiodic lattice, thereby opening pathways to study collective behavior in various topologically frustrated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Geometric Tail Approximation for Reliability and Survivability

A common problem in developing high-reliability systems is estimating the reliability for a population of components that cannot be 100% tested. The radiation survivability of a population of components is often estimated by testing a very small sample to some multiple of the required specification level, known as an overtest. Given a successful test with a sufficient overtest margin, the population of components is assumed to have the required survivability or radiation reliability. However, no mathematical justification for such claims has been crafted without making aggressive assumptions regarding the statistics of the unknown distribution. Here we illustrate a new approach that leverages geometric bounding arguments founded on relatively modest distribution assumptions to produce conservative estimates of component reliability.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Improving the Parameterization of Cloud and Rain Microphysics in E3SM using Novel Observationally-Constrained Bayesian Approach (Final Technical Report)

In this project, we sought to develop new cloud and rain microphysics frameworks within the Energy Exascale Earth System Model (E3SM). This work encompassed two primary avenues of research: 1) Further development of a Bayesian-based scheme called BOSS (Bayesian Observationally-constrained Statistical-physical Scheme) to represent cloud and rain microphysics, testing it in realistic high-resolution cloud models, and implementing it in E3SM; 2) Development of a methodology utilizing machine learning to enable computationally tractable use of tractable use of Markov chain Monte Carlo sampling for Bayesian parameter estimation in Earth system and cloud models. In this project, we adapted the BOSS microphysics scheme, originally formulated for rain-only, to include all liquid-phase microphysical processes for cloud and rain, in particular the processes that mediate between these two categories, for example the conversion from cloud to rain through collision and coalescence of drops. We constrained the scheme via comparison and testing against a detailed model that explicitly represents the evolution of cloud and rain particles, called a bin microphysics scheme.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Development of Scalable Coastal and Offshore Macroalgal Farming

The US DOE/ARPA-E MARINER program funded a four-year project to determine an optimal way to grow kelps in large, nearshore and offshore arrays for the eventual purpose of biofuel production with the goal of keeping the cost below $\$$80 USD per dry metric ton of kelp. This project specifically looked at how Saccharina latissima can be grown in the Gulf of Alaska to reach that goal. There were three major aspects of the research: 1) optimizing nursery production and seeding lines for outplanting, 2) designing an economical, modular outplanting structure and 3) developing methods to efficiently harvest the product. Farm designs were based on catenary structures and the use of spreader bars with variable spacing of grow lines and line types. The spacing of the grow lines makes a difference in the yield. Grow line spacing of ≥1.5m showed about a 50% increase in kg/m. There was no statistical difference in the growth of Saccharina latissima whether in the middle or the outside of the array, but the line type and perhaps line thickness can make a difference in yield. Sagging caused by the weight of the mature fronds resulted in lower growth at depth. Various harvesting approaches for mature kelps were tested by collaborating farmers. One promising innovation is the use of large bags for holding the freshly harvested fronds. Although the weight of the fronds on the growlines causes the lines to sink, the bags packed with the harvested fronds float, allowing for easy loading to the transfer vessel. Another advance in harvesting is a specially built harvest vessel, the Harvest Buddy, allowing a more mechanized and faster way to harvest. A significant aspect of this project was the TTO/T2M. Several different outreach activities were performed by the Alaska Fisheries Development Foundation and GreenWave. A techno-economic assessment (TEA) using our data has pointed to solutions to reach the $\$$80 goal. A second phase of this project involved the co-culture of two different species of kelps. Interest in farming kelps has grown beyond using kelp for food, feed or biofuels. There is considerable interest in generating biomass from seaweed for use in bioplastics and other products that would substitute for petroleum-derived products. For these uses to be viable, large amounts of biomass are needed. Very large kelp farms can be expensive to build and maintain, leading to the need to optimize the biomass per unit area. Although close spacing of growlines can lead to poor growth, a viable approach may be to grow two species of kelps together: one that hangs down and one that is buoyant, growing up. This system would increase the spacing in three dimensions. In Alaska, Saccharina latissima is commonly grown hanging down from longlines. One of the buoyant Alaskan kelps is Nereocystis luetkeana. Because there are commercial uses for wild-harvested Nereocystis in Alaska, we undertook a preliminary trial in Kodiak, Alaska that grew both Saccharina and Nereocystis in the same longline array. Closely spaced lines were seeded the first week of February 2023 and set at 3m below the surface. The arrays were harvested in late June 2023. Despite having 45% fewer grow-lines, the total yield of the Nereocystis on the combined arrays was statistically similar to the Nereocystis only arrays. Total yields were greatest on the combined arrays, followed by the Nereocystis only and Saccharina only arrays. These results may have significance for large scale macroalgal production.

09 BIOMASS FUELS↗
DOE OSTI2016

Molecular dynamics simulations of hydrogen diffusion in aluminum

In this study, hydrogen diffusion impacts the performance of solid-state hydrogen storage materials and contributes to the embrittlement of structural materials under hydrogen-containing environments. In atomistic simulations, the diffusion energy barriers are usually calculated using molecular statics simulations where a nudged elastic band method is used to constrain a path connecting the two end points of an atomic jump. This approach requires prior knowledge of the “end points”. For alloy and defective systems, the number of possible atomic jumps with respect to local atomic configurations is tremendous. Even when these jumps can be exhaustively studied, it is still unclear how they can be combined to give an overall diffusion behavior seen in experiments. Here we describe the use of molecular dynamics simulations to determine the overall diffusion energy barrier from the Arrhenius equation. This method does not require information about atomic jumps, and it has additional advantages, such as the ability to incorporate finite temperature effects and to determine the pre-exponential factor. As a test case for a generic method, we focus on hydrogen diffusion in bulk aluminum. We find that the challenge of this method is the statistical variation of the results. However, highly converged energy barriers can be achieved by an appropriate set of temperatures, output time intervals (for tracking hydrogen positions), and a long total simulation time. Our results help elucidate the inconsistencies of the experimental diffusion data published in the literature. The robust approach developed here may also open up future molecular dynamics simulations to rapidly study diffusion properties of complex material systems in multidimensional spaces involving composition and defects.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2019

The comparison of semiempirical and ab initio molecular modeling methods in activity and property evaluation of selected antimicrobial sulfonamides

A group of sulfonamides exhibiting antimicrobial activity was analyzed with the application of quantitative structure–activity/property relationships method. The purpose of this study was to show the common and differentiating characteristics of the analyzed chemical structures alike physicochemically as well as pharmacologically based on the quantum-chemical calculations both in vacuo and in the aquatic environment together with their microbiological activity and chromatographic retention data. The semiempirical and ab initio level of in silico molecular modeling was performed for calculations of statistically significant molecular descriptors to compare obtained results. The relationship between the structure and biological activity and physicochemical parameters data was able to class and describe analyzed molecules and the applied chemometric approaches (principal component analysis, factor analysis, and multiple regression analysis) revealed the influential features of the tested structures responsible for the antimicrobial activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Baryon acoustic oscillations in the Ly α forest of BOSS DR11 quasars

We report a detection of the baryon acousticoscillation (BAO) feature in the flux-correlation function of the Lyα forest of high-redshift quasars with a statistical significance of five standard deviations. The study uses 137,562 quasars in the redshift range 2.1 ≤ z ≤ 3.5 from the data release 11 (DR11) of the Baryon Oscillation Spectroscopic Survey (BOSS) of SDSS-III. This sample contains three times the number of quasars used in previous studies. The measured position of the BAO peak determines the angular distance, D A (z = 2.34) and expansion rate, H(z = 2.34), both on a scale set by the sound horizon at the drag epoch, r d . We find D A /r d = 11.28 ± 0.65(1σ)$+2.8\atop{-1.2}$(2σ) and D H /r d = 9.18 ± 0.28(1σ) ± 0.6(2σ) where D H = c/H. The optimal combination, ~D$0.7\atop{H}$ D$0.3\atop{A}/r d is determined with a precision of ~2%. For the value r d = 147.4 Mpc, consistent with the cosmic microwave background power spectrum measured by Planck, we find D A (z = 2.34) = 1662 ± 96(1σ) Mpc and H(z = 2.34) = 222 ± 7(1σ) km s -1 Mpc -1 . Tests with mock catalogs and variations of our analysis procedure have revealed no systematic uncertainties comparable to our statistical errors. Our results agree with the previously reported BAO measurement at the same redshift using the quasar-Lyα forest cross-correlation. The autocorrelation and cross-correlation approaches are complementary because of the quite different impact of redshift-space distortion on the two measurements. The combined constraints from the two correlation functions imply values of D A /r d that are 7% lower and 7% higher for D H /r d than the predictions of a flat ΛCDM cosmological model with the best-fit Planck parameters. With our estimated statistical errors, the significance of this discrepancy is ≈2.5σ.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Electric field stimulated growth of Zn whiskers

We have investigated the impact of strong (∼10{sup 4} V/cm) electric fields on the development of Zn whiskers. The original samples, with considerable whisker infestation were cut from Zn-coated steel floors and then exposed to electric fields stresses for 10-20 hours at room temperature. We used various electric field sources, from charges accumulated in samples irradiated by: (1) the electron beam of a scanning electron microscope (SEM), (2) the electron beam of a medical linear accelerator, and (3) the ion beam of a linear accelerator; we also used (4) the electric field produced by a Van der Graaf generator. In all cases, the exposed samples exhibited a considerable (tens of percent) increase in whiskers concentration compared to the control sample. The acceleration factor defined as the ratio of the measured whisker growth rate over that in zero field, was estimated to approach several hundred. The statistics of lengths of e-beam induced whiskers was found to follow the log-normal distribution known previously for metal whiskers. The observed accelerated whisker growth is attributed to electrostatic effects. These results offer promise for establishing whisker-related accelerated life testing protocols.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Clutter Identification based on sparse recovery with dynamically changing dictionary sizes for cognitive radar

Existing radar algorithms assume stationary statistical characteristics for environment/clutter. In practical scenarios, the statistical characteristics of the clutter can dynamically change depending on where the radar is operating. Non-stationarity in the statistical characteristics of the clutter may negatively affect the radar performance. Cognitive radar that can sense the changes in the clutter statistics, learn the new statistical characteristics, and adapt to these changes has been proposed to overcome these shortcomings. We have recently developed techniques for detection of statistical changes and learning the new clutter distribution for cognitive radar. In this work, we will extend the learning component. More specifically, in our previous work, we have developed a sparse recovery based clutter distribution identification to learn the distribution of the new clutter characteristics after the detected change in the statistics of the clutter. In our method, we have built a dictionary of clutter distributions and used this dictionary in orthogonal matching pursuit to perform sparse recovery of the clutter distribution assuming that the dictionary includes the new distribution. In this work, we propose a hypothesis testing based approach to detect whether the new distribution of the clutter is included in the dictionary or not, and suggest a method to dynamically update the dictionary. We envision that the successful outcomes of this work will be of high relevance to the adaptive learning and cognitive augmentation of the radar systems that are used in remotely piloted vehicles for surveillance and reconnaissance operations.

Zhu, Yuansheng↗
DOE OSTI2015

Proof-of-Concept Demonstrations for Computation-Based Human Reliability Analysis. Modeling Operator Performance During Flooding Scenarios

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the overall objective to help sustain the existing commercial nuclear power plants (NPPs). To accomplish this program objective, there are multiple LWRS “pathways,” or research and development (R&D) focus areas. One LWRS focus area is called the Risk-Informed Safety Margin and Characterization (RISMC) pathway. Initial efforts under this pathway to combine probabilistic and plant multi-physics models to quantify safety margins and support business decisions also included HRA, but in a somewhat simplified manner. HRA experts at Idaho National Laboratory (INL) have been collaborating with other experts to develop a computational HRA approach, called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), for inclusion into the RISMC framework. The basic premise of this research is to leverage applicable computational techniques, namely simulation and modeling, to develop and then, using RAVEN as a controller, seamlessly integrate virtual operator models (HUNTER) with 1) the dynamic computational MOOSE runtime environment that includes a full-scope plant model, and 2) the RISMC framework PRA models already in use. The HUNTER computational HRA approach is a hybrid approach that leverages past work from cognitive psychology, human performance modeling, and HRA, but it is also a significant departure from existing static and even dynamic HRA methods. This report is divided into five chapters that cover the development of an external flooding event test case and associated statistical modeling considerations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Machine learning bandgaps of double perovskites

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dark Energy Survey: Modeling strategy for multiprobe cluster cosmology and validation for the Full Six-year Dataset

We introduce an updated To&Krause2021 model for joint analyses of cluster abundances and large-scale two-point correlations of weak lensing and galaxy and cluster clustering (termed CL+3x2pt analysis) and validate that this model meets the systematic accuracy requirements of analyses with the statistical precision of the final Dark Energy Survey (DES) Year 6 (Y6) dataset. The validation program consists of two distinct approaches, (1) identification of modeling and parameterization choices and impact studies using simulated analyses with each possible model misspecification (2) end-to-end validation using mock catalogs from customized Cardinal simulations that incorporate realistic galaxy populations and DES-Y6-specific galaxy and cluster selection and photometric redshift modeling, which are the key observational systematics. In combination, these validation tests indicate that the model presented here meets the accuracy requirements of DES-Y6 for CL+3x2pt based on a large list of tests for known systematics. In addition, we also validate that the model is sufficient for several other data combinations: the CL+GC subset of this data vector (excluding galaxy--galaxy lensing and cosmic shear two-point statistics) and the CL+3x2pt+BAO+SN (combination of CL+3x2pt with the previously published Y6 DES baryonic acoustic oscillation and Y5 supernovae data).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Twentieth-Century Hydrometeorological Reconstructions to Study the Multidecadal Variations of the Water Cycle Over France

Characterizing and understanding the multidecadal variations of the continental hydrological cycle is a challenging issue given the limitation of observed data sets. In this paper, a new approach to derive twentieth century hydrological reconstructions over France with an hydrological model is presented. The method combines the results of long-term atmospheric reanalyses downscaled with a stochastic statistical method and homogenized station observations to derive the meteorological forcing needed for hydrological modeling. Different methodological choices are tested and evaluated. We show that using homogenized observations to constrain the results of statistical downscaling help to improve the reproduction of precipitation, temperature, and river flows variability. In particular, it corrects some unrealistic long-term trends associated with the atmospheric reanalyses. Observationally constrained reconstructions therefore constitute a valuable data set to study the multidecadal hydrological variations over France. Thanks to these reconstructions, we confirm that the multidecadal variations previously noted in French river flows have mainly a climatic origin. Moreover, we show that multidecadal variations exist in other hydrological variables (evapotranspiration, snow cover, and soil moisture). Depending on the region, the persistence from spring to summer of soil moisture or snow anomalies generated during spring by temperature and precipitation variations may explain river flows variations in summer, when no concomitant climate variations exist.

54 ENVIRONMENTAL SCIENCES↗