Main Sources Of Uncertainty Research Articles

Unit Commitment for Power Generation Systems Based on Prices in Smart Grid Environment Considering Uncertainty

With the growing demand for electricity, the inability of governments to provide the necessary resources to invest in the electricity industry and the rising price of fossil fuels, the tendency is to study and pay attention to economic issues in power systems studies. In this paper a new modified version of gray wolf optimization (MGWO) is proposed to solve the unit commitment (UC) problem in a power system in case of uncertainty. Market price variation is the main source of uncertainty in the UC program. Therefore, a model based on normal probability density function (PDF) is present for reducing the market price uncertainty effect in the model. Simulations are done for a standard 10 thermal units power system, and the results of the optimization by the proposed MGWO are compared with the previous version of the GWO algorithm and particle swarm optimization (PSO) algorithm results. The simulation results confirm the superiority of the proposed MGWO algorithm over the two algorithms PSO and GWO.

Statistical data for system‐based reliability analysis of stainless steel structures with hollow sections

AbstractCurrent codes for steel structures, including AISC 360‐10, AS4100 and Eurocode 3, are in the process of evolving from the current member‐based design method to direct system‐based approaches that allow evaluating the strength of structures directly from numerical simulations. However, these codes do not provide adequate safety factors nor reliability requirements for structural systems. Thus, it is necessary to build a rigorous structural reliability framework to investigate acceptable target reliability indices for structural systems and to provide adequate system safety factors. While this framework has been developed based on advanced Finite Element analysis for carbon steel structures in recent years in the form of the Direct Design Method (DDM), it does not exist for stainless steel structures. This paper presents the first step towards developing a system‐based reliability framework for stainless steel structures by providing statistical distributions of the key parameters governing the strength of systems composed of stainless steel cold‐formed hollow sections. The distributions are based on a comprehensive database collected from the literature including data on the main sources of uncertainty: geometric properties, imperfections and material parameters.

Computational Fluid Dynamics Modelling of Liquid–Solid Slurry Flows in Pipelines: State-of-the-Art and Future Perspectives

Slurry pipe transport has directed the efforts of researchers for decades, not only for the practical impact of this problem, but also for the challenges in understanding and modelling the complex phenomena involved. The increase in computer power and the diffusion of multipurpose codes based on Computational Fluid Dynamics (CFD) have opened up the opportunity to gather information on slurry pipe flows at the local level, in contrast with the traditional approaches of simplified theoretical modelling which are mainly based on a macroscopic description of the flow. This review paper discusses the potential of CFD for simulating slurry pipe flows. A comprehensive description of the modelling methods will be presented, followed by an overview of significant publications on the topic. However, the main focus will be the assessment of the potential and the challenges of the CFD approach, underlying the essential interplay between CFD simulations and experiments, discussing the main sources of uncertainty of CFD models, and evaluating existing models based on their interpretative or predictive capacity. This work aims at providing a solid ground for students, academics, and professional engineers dealing with slurry pipe transport, but it will also provide a methodological approach that goes beyond the specific application.

Determining Temporal Uncertainty of a Global Inland Surface Water Time Series

Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product’s quality with respect to its use for modeling and the geoscientific community.

Measurement uncertainty evaluation and risk of false conformity assessment for microbial enumeration tests

Microbial enumeration tests are widely used to assess the microbiological quality of non-sterile pharmaceutical products. Despite of all efforts to guarantee the reliability of microbial enumeration tests, there will always be an uncertainty associated with the measured values, which can lead to false conformity/non-conformity decisions. In this work, we evaluated the measurement uncertainty using a bottom-up approach and estimate the consumer's or producer's risk due to the measurement uncertainty. Three main sources of uncertainty were identified and quantified: dilution factor, plated volume, and microbial plate counts. The contribution of these sources of uncertainty depends on the measured value of microbial load in pharmaceutical products. The contribution of dilution factor and plated volume uncertainties increase with an increase of measured value, while the contribution of microbial plate count uncertainty decreases with an increase of measured value. The overall uncertainty values were expressed as uncertainty factors, which provide an asymmetric 95% level confidence level of microbial load in pharmaceutical products. In addition, the risk of false conformity/non-conformity decisions due to measurement uncertainty was assess using Monte Carlo method. When the measured value is close to the upper specification limit and/or the measurement uncertainty is large, the risk of false conformity/non-conformity decisions may be significantly high. Thus, we conclude that the use of uncertainty factor in the conformity/non-conformity assessment is important to guarantee the reliability of microbial enumeration test results and to support decision-making.

Uncertainty propagation from n+56Fe nuclear reaction model parameters to neutron multiplication factor

Nuclear data are the main uncertainty sources for modern neutron transport calculations. However, complete correlation matrices among different nuclear data are not presented in the current evaluations. In order to completely consider that correlation between nuclear data, the present work propagates the covariance matrix of continuum n+56Fe reaction model parameters to the effective neutron multiplication factor (keff) of a thermal neutron spectrum benchmark and a fast neutron spectrum benchmark. Results show that the uncertainty propagated from angular distribution is not negligible. The general correlations between angular distributions and cross sections of 56Fe for neutron energies above 850 keV are respectively −0.39 ± 0.05 and −1.02 ± 0.02 for keff calculation in the thermal and fast benchmarks, respectively. These correlations reveal the importance of a complete correlation matrix of nuclear data for propagating uncertainties to neutronic calculations.

Aerodynamic Free-Flight Conditions in Wind Tunnel Modelling through Reduced-Order Wall Inserts

Parallel sidewalls are the standard bounding walls in wind tunnels when making a wind tunnel model for free-flight condition. The consequence of confinement in wind tunnel tests, known as wall-interference, is one of the main sources of uncertainty in experimental aerodynamics, limiting the realizability of free-flight conditions. Although this has been an issue when designing transonic wind tunnels and/or in cases with large blockage ratios, even subsonic wind tunnels at low-blockage-ratios might require wall corrections if a good representation of free-flight conditions is intended. In order to avoid the cumbersome streamlining methods especially for subsonic wind tunnels, a sensitivity analysis is conducted in order to investigate the effect of inclined sidewalls as a reduced-order wall insert in the airfoil plane. This problem is investigated via Reynolds-averaged Navier–Stokes (RANS) simulations, and a NACA4412 wing at the angles of attack between 0 and 11 degrees at a moderate Reynolds number (400 k) is considered. The simulations are validated with well-resolved large-eddy simulation (LES) results and experimental wind tunnel data. Firstly, the wall-interference contribution in aerodynamic forces, as well as the local pressure coefficients, are assessed. Furthermore, the isolated effect of confinement is analyzed independent of the boundary-layer growth. Secondly, wall-alignment is modified as a calibration parameter in order to reduce wall-interference based on the aforementioned assessment. In the outlined method, we propose the use of linear inserts to account for the effect of wind tunnel walls, which are experimentally simple to realize. The use of these inserts in subsonic wind tunnels with moderate blockage ratio leads to very good agreement between free-flight and wind tunnel data, while this approach benefits from simple manufacturing and experimental realization.

An Ecosystem View of Peer-to-Peer Electricity Trading: Scenario Building by Business Model Matrix to Identify New Roles

This article introduces new roles in future peer-to-peer electricity trading markets. Following a qualitative approach, firstly, the value network of the current electricity market is presented. To do so, service streams, critical roles, activities, and their setting in the electricity market are identified. Secondly, in order to identify the main sources of uncertainty, the business model matrix framework is utilized to analyze peer-to-peer electricity trading. Thirdly, four future scenarios are built based on user involvement and customer ownership. The outcome of the scenario building is the emergence of new roles, brokers, and representatives in the future peer-to-peer electricity markets. Fourth, based on the four future scenarios, changes in the value network, new roles, and emerging/evolving activities are identified. Finally, the two new roles are discussed from grid structure, security and privacy, legal, and data protection perspectives. The data is gathered by conducting semi-structured interviews with stakeholders in the current electricity market as well as potential disruptors. This article elaborates on the configuration of the value network in the electricity market and highlights the changes that peer-to-peer trading imposes to the status quo. Through the outcomes of the value network analysis, it assists policy makers to consider the requirements and current market players to reconsider their business models.

Quantifying the Impacts of Land Surface Modeling on Hub-Height Wind Speed under Different Soil Conditions

AbstractWe investigate the impact of three land surface models (LSMs) on simulating hub-height wind speed under three different soil regimes (dry, wet, and frozen) to improve understanding of the physics of wind energy forecasts using the Weather Research and Forecasting (WRF) model. A six-day representative period is selected for each soil condition. The simulated wind speed, surface energy budget and soil properties are compared with the observations collected from the second Wind Forecast Improvement Project (WFIP2). For the selected cases, our simulation results suggest that the impact of LSMs on hub-height wind speed are sensitive to the soil states but not so much to the choice of LSM. The simulated hub-height wind speed is in much better agreement with the observations for the dry soil case than the wet and frozen soil cases. Over the dry soil, there is a strong physical connection between the land surface and hub-height wind speed through near-surface turbulent mixing. Over the wet soil, the simulated hub-height wind speed is less impacted by the land surface due to weaker surface fluxes and large-scale synoptic disturbances. Over the frozen soil, the LSM seems to have limited impact on hub-height wind speed variability due to the decoupling of the land surface with the overlying atmosphere. Two main sources of modeling uncertainties are proposed. The first is the insufficient model physics representing the surface energy budget, especially the ground heat flux, and the second is the inaccurate initial soil states such as soil temperature and soil moisture.

Research on Model Validation and Uncertainty Quantification of Airfoil Flow

Numerical simulations and wind tunnel tests are both basic methods for aerodynamic research and application. They are the modeling and simulation of real world system. There are various uncertainties in the implementation process. In Computational Fluid Dynamics (CFD), the main sources of uncertainty are the model form, model parameters and numerical solution methods. In wind tunnel tests, there are many uncertainties sources, such as geometric model, test environment, test devices and instrumentation, test methods, data recording and processing, etc. This paper takes NACA0012 airfoil as an example. Bayesian model averaging method (BMA) and grid convergence study are used to analyse model form and numerical solution uncertainty in CFD. For the geometric configuration uncertainty in wind tunnel test, regression analysis and bootstrap method are used to carry out data fusion. Finally, model validation research is conducted.

Development and validation of a model for assessing neutron fluence with lithium fluoride thermoluminescent detectors

Lithium fluoride thermoluminescent detectors are commonly used for neutrons measurements. However, the calibration is usually performed with readily available gamma sources in terms of gamma equivalent air kerma instead of the more meaningful neutron fluence. Neutron calibration coefficients in terms of neutron fluence depend strongly on the experimental conditions and can thus usually not be found in literature. Therefore, the goal of this work was to develop and validate a model to calculate such neutron calibration coefficients and to use this model to quantify the main sources of uncertainty. Comparison of the neutron calibration coefficients predicted by the model with experimental data from literature and this work showed reasonable agreement. However, a slight systematic deviation was found, probably caused by the significant uncertainty on the relative luminescence efficiency values predicted by the recently developed Microdosimetric d(z) Model for the low energy charged particles released during the 6Li(n,α)3H reaction. Therefore, it was decided to use relative luminescence efficiency values based on the experimental results instead. The model also identified the neutron energy and angular distribution and the luminescence reader light collection geometry as the main sources of uncertainty. Imprecise knowledge of these experimental input parameters can easily lead to uncertainties of the order of tens of percent on the calculated neutron calibration coefficients. The developed model is applicable for neutron energies below 0.01 MeV, but could be extended to higher energies in the future.

Estimation of the error of the simplified algorithm of processing of functions of deflations of deformed frames of bodies of rolling stock

The study is aimed at assessing the size of the error that arises when processing the results of examining the geometric characteristics of the bearing structures of rolling stock units using an algorithm without using trigonometric functions. The object of the research is a method of simplified alignment of the deflection function of body frame beams to the horizontal plane. One of the biggest problem areas is the lack of understanding by some customers of the work of the possibility of using this algorithm due to the lack of information about the errors that arise in the simplified calculation. The study was carried out by comparing the results of processing the initial data by two methods, obtained during the work on the inspection of the state of the supporting structures of the unit of the shunting diesel locomotive TGM6. One method, the algorithm of which is the subject of this study, assumes that no complex calculations are used during data processing. The second method involves the use of an algorithm for aligning the deflection functions of the body frame beams of a rolling stock unit of railways in the horizontal plane, taking into account all trigonometry tools, which will exclude the accompanying calculation errors of the simplified method. After processing the initial data, two sets of results were obtained – with the desired calculation error and without. Comparison of these datasets yielded an error value for frame tilt of 5.7. For clarity, the size of the error was compared with the expanded uncertainty values of the main sources of uncertainty in the methodology for examining the bearing structures of rolling stock. On the basis of the analysis of two methods of leveling the inclination to the horizontal plane of the deformed rolling stock body frame, the expediency of such an approach has been proved. The results obtained make it possible to reasonably use the Simplified approach to processing the data obtained during the survey of the geometric characteristics of the rolling stock. If necessary, the developed mathematical model can be used to improve the accuracy of calculating the uncertainty of measurements of geometric characteristics, as well as for use in the study of modification of existing or development of new measurement techniques

Main Uncertainties in the RF Ultrasound Scanning Simulation of the Standard Ultrasound Phantoms.

Ultrasound echoscopy technologies are continuously evolving towards new modalities including quantitative parameter imaging, elastography, 3D scanning, and others. The development and analysis of new methods and algorithms require an adequate digital simulation of radiofrequency (RF) signal transformations. The purpose of this paper is the quantitative evaluation of RF signal simulation uncertainties in resolution and contrast reproduction with the model of a phased array transducer. The method is based on three types of standard physical phantoms. Digital 3D models of those phantoms are composed of point scatterers representing the weak backscattering of the background material and stronger backscattering from inclusions. The simulation results of echoscopy with sector scanning transducer by Field II software are compared with the RF output of the Ultrasonix scanner after scanning standard phantoms with 2.5 MHz phased array. The quantitative comparison of axial, lateral, and elevation resolutions have shown uncertainties from 9 to 22% correspondingly. The echoscopy simulation with two densities of scatterers is compared with contrast phantom imaging on the backscattered RF signals and B-scan reconstructed image, showing that the main sources of uncertainties limiting the echoscopy RF signal simulation adequacy are an insufficient knowledge of the scanner and phantom’s parameters. The attempt made for the quantitative evaluation of simulation uncertainties shows both problems and the potential of echoscopy simulation in imaging technology developments. The analysis presented could be interesting for researchers developing quantitative ultrasound imaging and elastography technologies looking for simulated raw RF signals comparable to those obtained from real ultrasonic scanning.

From Cosmicflows distance moduli to unbiased distances and peculiar velocities

ABSTRACTSurveys of galaxy distances and radial peculiar velocities can be used to reconstruct the large-scale structure. Other than systematic errors in the zero-point calibration of the galaxy distances the main source of uncertainties of such data is errors on the distance moduli, assumed here to be Gaussian and thus turning into lognormal errors on distances and velocities. Naively treated, this leads to spurious nearby outflow and strong infall at larger distances. The lognormal bias is corrected here and tested against mock data extracted from a ΛCDM simulation, designed to statistically follow the grouped Cosmicflows-3 (CF3) data. Considering a subsample of data points, all of which have the same true distances or the same redshifts, the lognormal bias arises because the means of the distributions of observed distances and velocities are skewed off the means of the true distances and velocities. However, the medians are invariant under the lognormal transformation. This invariance allows the Gaussianization of the distances and velocities and the removal of the lognormal bias. This bias Gaussianization correction (BGc) algorithm is tested against mock CF3 catalogues. The test consists of a comparison of the BGc estimated with the simulated distances and velocities and of an examination of the Wiener filter reconstruction from the BGc data. Indeed, the BGc eliminates the lognormal bias. The estimation of Hubble’s constant (H0) is also tested. The residual of the BGc-estimated H0 from the simulated values is $-0.6\pm 0.7{\, \rm km \ s^{-1}\, Mpc^{-1}}$, and is dominated by the cosmic variance. The BGc correction of the actual CF3 data yields $H_0=75.8\pm 1.1{\, \rm km \ s^{-1}\, Mpc^{-1}}$.

The effect of soil-moisture uncertainty on irrigation water use and farm profits

Technologies that increase the accuracy of soil-moisture monitoring, such as in-situ sensors, have been proposed as a key solution for increasing agricultural water productivity. However, quantifying how uncertainty in soil-moisture estimates lead to irrigation inefficiencies or economic losses has not been explicitly studied. We develop a framework that combines a crop simulation model with a rule-based irrigation decision-making algorithm to assess the impact of soil-moisture uncertainty on irrigation use and farm profits. We apply this modelling framework to a case study of irrigated maize production in Nebraska, United States, a region where improvements in agricultural water productivity are at the forefront of water-policy debates. We consider two main sources of uncertainty that result in a divergence between the farmers’ perception of soil-water content and the true water status, namely errors in the knowledge of soil texture and measurement of daily soil-water flux inflows and outflows. Even for very large errors in both soil-texture and water-flux measurements, impacts on water use and profits were marginal (11 ha-mm increase and $27 ha–1 decrease, respectively). In contrast, farmers’ choice of irrigation strategy had a much larger impact on water use and profits than uncertainty in soil-moisture information used to implement that strategy. Our findings show that near-optimal irrigation decisions can be made without perfect soil-moisture information. This conclusion suggests that providing farmers with improved irrigation scheduling recommendations – utilizing crop-water models and optimization techniques – would have a larger impact on water-use efficiency than simply providing farmers with technologies to more accurately monitor soil-moisture conditions.

Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies

Abstract. Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These deterministic datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauge-only products (GPCC and CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using ground-based observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I and ERA5) and one merged gauged, satellite and reanalysis product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a top-down hydroclimatic modeling chain using 10 CMIP5 (fifth Coupled Model Intercomparison Project) general circulation models (GCMs) under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071–2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but four precipitation datasets outperformed the others for most catchments. They are, in order, MSWEP, CHIRPS, PERSIANN and ERA5. For the present study, the two-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the four best-performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.

Evaluation of Moisture and Decay Models for a New Design Framework for Decay Prediction of Wood

Performance-based, service-life design of wood has been the focus of much research in recent decades. Previous works have been synthesized in various factorized design frameworks presented in the form of technical reports. Factorization does not consider the non-linear dependency between decay-influencing effects, such as between detail design and climate variables. The CLICKdesign project is a joint European effort targeting digital, performance-based specification for service-life design (SLD) of wood. This study evaluates the feasibility of using a semi-empirical moisture model (SMM) as a basis for a digital SLD framework. The performance of the SMM is assessed by comparison against a finite element model (FEM). In addition, two different wood decay models (a logistic, LM, and simplified logistic model (SLM)) are compared. While discrepancies between the SMM and FEM were detected particularly at high wood moisture content, the overall performance of the SMM was deemed sufficient for the application. The main source of uncertainty instead stems from the choice of wood decay model. Based on the results, a new method based on pre-calculated time series, empirical equations, and interpolation is proposed for predicting the service life of wood. The method is fast and simple yet able to deal with non-linear effects between weather variables and the design of details. As such, it can easily be implemented as part of a digital design guideline to provide decision support for architects and engineers, with less uncertainty than existing factorized guidelines.

Measuring the Accuracy of Aggregates Computed from a Statistical Register

AbstractThe Italian National Statistical Institute (Istat) is currently engaged in a modernization programme that foresees a significant revision of the methods traditionally used for the production of official statistics. The main concept behind this transformation is the use of the Integrated System Statistical Registers, created by a massive integration of administrative archives and survey data. In this article, we focus on how to measure the accuracy of register estimates of a population total from measurements calculated at the unit level. We propose the global mean squared error (GMSE) as a statistical quantity suitable for measuring accuracy in the context of the production of official statistics. It can be defined to explicitly consider the main sources of uncertainty that may affect registers. The article suggests a feasible calculation strategy for the GMSE that allows National Statistical Institutes to build algorithms that can promptly be applied for each user request, thus improving the relevance, transparency and confidence of official statistics. Through a simulation study, we verified the efficacy of the proposed strategy.

On the Sources and Sizes of Uncertainty in Predicting the Arrival Time of Interplanetary Coronal Mass Ejections Using Global MHD Models

AbstractAccurate predictions of the properties of interplanetary coronal mass ejection (ICME)‐driven disturbances are a key objective for space weather forecasts. The ICME's time of arrival (ToA) at Earth is an important parameter, and one that is amenable to a variety of modeling approaches. Previous studies suggest that the best models can predict the arrival time to within an absolute uncertainty of 10–15 h. Here, we investigate the main sources of uncertainty in predicting a CME's ToA at Earth. These can be broken into two main categories: (a) the initial properties of the ejecta, including its speed, mass, and direction of propagation and (b) the properties of the ambient solar wind into which it propagates. To estimate the relative contribution to ToA uncertainties, we construct a set of numerical experiments of cone‐model CMEs, where we vary the initial speed, mass, and direction at the inner radial boundary. Additionally, we build an ensemble of 12 ambient solar wind solutions using realizations from the ADAPT model. We find that each component in the chain contributes between ±2.5 and ±7 h of uncertainty to the estimate of the CME's ToA. Importantly, different realizations of the synoptic produce the largest uncertainties. This suggests that estimates of ToA will continue to be plagued with intrinsic uncertainties of ±10 h until tighter constraints can be found for these boundary conditions. Our results suggest that there are clear benefits to focused investigations aimed at reducing the uncertainties in CME speed, mass, direction, and input boundary magnetic fields.

Snowcover Survey over an Arctic Glacier Forefield: Contribution of Photogrammetry to Identify “Icing” Variability and Processes

The global climate shift currently underway has significant impacts on both the quality and quantity of snow precipitation. This directly influences the spatial variability of the snowpack as well as cumulative snow height. Contemporary glacier retreat reorganizes periglacial morphology: while the glacier area decreases, the moraine area increases. The latter is becoming a new water storage potential that is almost as important as the glacier itself, but with considerably more complex topography. Hence, this work fills one of the missing variables of the hydrological budget equation of an arctic glacier basin by providing an estimate of the snow water equivalent (SWE) of the moraine contribution. Such a result is achieved by investigating Structure from Motion (SfM) image processing that is applied to pictures collected from an Unmanned Aerial Vehicle (UAV) as a method for producing snow depth maps over the proglacial moraine area. Several UAV campaigns were carried out on a small glacial basin in Spitsbergen (Arctic): the measurements were made at the maximum snow accumulation season (late April), while the reference topography maps were acquired at the end of the hydrological year (late September) when the moraine is mostly free of snow. The snow depth is determined from Digital Surface Model (DSM) subtraction. Utilizing dedicated and natural ground control points for relative positioning of the DSMs, the relative DSM georeferencing with sub-meter accuracy removes the main source of uncertainty when assessing snow depth. For areas where snow is deposited on bare rock surfaces, the correlation between avalanche probe in-situ snow depth measurements and DSM differences is excellent. Differences in ice covered areas between the two measurement techniques are attributed to the different quantities measured: while the former only measures snow accumulation, the latter includes all of the ice accumulation during winter through which the probe cannot penetrate, in addition to the snow cover. When such inconsistencies are observed, icing thicknesses are the source of the discrepancy that is observed between avalanche probe snow cover depth measurements and differences of DSMs.