Gap-filling Technique Research Articles

Estimation of groundwater storage variations in African river basins: Response to global climate change using GRACE and GRACE-FO among past two decades

Groundwater Storage (GWS) is an important component of the water cycle and a vital resource for supplying water to millions of people as well as sustaining domestic and economic activities, especially in arid and semi-arid regions such as Africa. This study aims to estimate and analyze the recent groundwater changes (ΔGW) for 11 major African basins in light of climate changes and global warming utilizing the data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) from January 2003 to December 2021. We proposed a new gap-filling technique to calculate the missing months between GRACE and GRACE-FO missions for the continuous estimation of groundwater changes. Then, we establish a new framework to analyze and investigated the climate parameters that could affect groundwater changes using Empirical Orthogonal Function (EOF) technique. Our analysis reveals that the new gap-filling technique is significantly consistent with the TWS time-series based on the trend line analysis. The lowest GPCC drought index value occurs in the lower Nile basin by approximately −0.93, while the highest occurs in the middle of Africa in Congo basin by approximately −0.02. The precipitation rate is mostly concentrated in mid-Africa while North and South Africa shows a weak rate. Meanwhile, according to the analysis of dependent climate parameters, climate change has had a negative impact on groundwater. However, the long-term effect on the groundwater does not clearly appear because most of Africa is located within semi-arid and arid regions. The effect of rain on groundwater was evident through EOF analysis, which shows GWS EOF Mode-2 (36.28%) and precipitation EOF Mode-1 (78.34%) were found to be in significant agreement in spatio-temporal domains.

Filling the gap between GRACE and GRACE follow-on observations based on principal component analysis

SUMMARY The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On (GRACE-FO), have revolutionized the approach to monitoring global mass variations. However, the presence of several gaps, notably the continuous 11-month gap between the two missions, has generated a disruption in observations and hindered the analysis and application of the data. To address this problem, we have proposed a spectral domain gap-filling approach based on principal component analysis (PCA). Our simulation experiments demonstrate that the PCA gap-filling technique has significant potential to successfully reconstruct global mass variation and accurately capture real signals for most basins with an accuracy of less than 2 cm. When applied to actual missing data, our methodology delivers highly consistent results with previously published filling approaches, such as singular spectrum analysis and improved multichannel singular spectrum analysis method, for most of the global basins. Noteworthy, in the case of the Nelson basin, our PCA gap-filling method outperforms other methods in capturing seasonal signals and the return to a normal level of the terrestrial water storage changes in 2018. A comparison in the spectral domain indicates that the accuracy of the PCA-filling output is comparable to the original GRACE(-FO) data. Moreover, our method exhibits high generality, allowing for direct application to continuous GRACE(-FO) data without other additional data processing and without differentiating the types of missing data. Therefore, the proposed PCA gap-filling method offers exciting opportunities to guarantee the continuity of global mass change observations and benefit subsequent applications that require continuous data records.

Reconstructor: a COBRApy compatible tool for automated genome-scale metabolic network reconstruction with parsimonious flux-based gap-filling.

Genome-scale metabolic network reconstructions (GENREs) are valuable for understanding cellular metabolism in silico. Several tools exist for automatic GENRE generation. However, these tools frequently (i) do not readily integrate with some of the widely-used suites of packaged methods available for network analysis, (ii) lack effective network curation tools, (iii) are not sufficiently user-friendly, and (iv) often produce low-quality draft reconstructions. Here, we present Reconstructor, a user-friendly, COBRApy-compatible tool that produces high-quality draft reconstructions with reaction and metabolite naming conventions that are consistent with the ModelSEED biochemistry database and includes a gap-filling technique based on the principles of parsimony. Reconstructor can generate SBML GENREs from three input types: annotated protein .fasta sequences (Type 1 input), a BLASTp output (Type 2), or an existing SBML GENRE that can be further gap-filled (Type 3). While Reconstructor can be used to create GENREs of any species, we demonstrate the utility of Reconstructor with bacterial reconstructions. We demonstrate how Reconstructor readily generates high-quality GENRES that capture strain, species, and higher taxonomic differences in functional metabolism of bacteria and are useful for further biological discovery. The Reconstructor Python package is freely available for download. Complete installation and usage instructions and benchmarking data are available at http://github.com/emmamglass/reconstructor.

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance.

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap-filling technique to increase population diversity. SDM further guides rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence in every generation of evolution regardless of environmental changes. The proposed algorithm has been extensively studied through comparison with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with DMO problems.

Fast and operational gap filling in satellite-derived aerosol optical depths using statistical techniques

Satellite observations, used worldwide in the atmospheric sciences, are extremely useful for providing aerosol information within a wide spatial range. However, the coverage of aerosol data by satellite observations is sometimes of inferior quality because of the effects of surface reflectivity and clouds. To fill the gaps in aerosol optical depths (AODs) retrieved from geostationary ocean color imager observations, this study applies operational statistical techniques, including radial basis functions (RBFs) with four different weightings (i.e., linear, multiquadric, thin-plate, and inverse), Poisson, and ordinary Kriging. Based on computation time and accuracy of the individual gap-filling techniques, Poisson and the liner RBF are selected as the two best methods and then averaged with weights using one-dimensional weighted average (1D-WAVG) and two-dimensional weighted average (2D-WAVG) root mean square errors. All methods produce reliable results, yielding a correlation coefficient between 0.74 and 0.87 over the entire research domain. Out of the individual techniques, the Poisson, with an initial estimation from a zonal mean of AODs, is the most accurate with the lowest computational costs, even for a large number of missing pixels and most regions, excluding East China (EC). The Poisson’s high bias over EC is compensated in 1D- and 2D-WAVGs by taking more accurate estimations of the linear RBF than those of the Poisson over the region. If we consider 1D- and 2D-WAVGs in our analysis, the highest correlation is obtained from the 2D-WAVG over all regions. Because of its reliability and fast computation time, applying the 2D-WAVG can be a good solution to provide spatial–temporal continuous aerosol information. In addition to air pollution studies, such as real-time air quality predictions, estimation of ground-level particulate matter concentrations, and other applications, the fast and operational gap-filling technique can also be expanded to remote sensing data obtained from satellite observations to provide helpful and useful information for the public.

Multiple gap-filling for eddy covariance datasets

With novel developments in technology, eddy covariance flux measurements have become feasible for a variety of trace gases. While the statistical properties and gap-filling strategies have been well examined for carbon dioxide, these are much less understood for other gases.Here, we propose a universal methodology deploying multiple gap-filling techniques and artificial gap scenarios to evaluate the techniques’ performances, infer the statistical flux properties, and fill the real gaps in eddy covariance datasets of any trace gas. The methodology was implemented in a gap-filling framework with techniques spanning from simple and diurnal interpolations, look-up tables, artificial neural networks, to an inferential model. For the new scheme of half-hourly and daily artificial gaps, each additional gap was superimposed one at a time (thus keeping the disturbance to a minimum) for the whole dataset and the scenarios were resampled by bootstrapping. The gap-filled sums were then estimated from the ensemble of well-performing gap-filling techniques. The gap-filling framework was applied to campaign data of three different trace gases (51 days of ammonia, 79 days of total reactive nitrogen, and 89 days of methane flux measurements). The aggregated fluxes are stated as ensemble ranges of multiple techniques plus the techniques’ uncertainties. Additionally, the framework was used to gap-fill a full year of carbon dioxide flux measurements yielding similar performances as previously reported.Based on a review of gap-filling comparison studies and on our findings, we suggest reconsidering the standard procedure of using one gap-filling technique for multi-site studies. Deploying multiple gap-filling techniques and providing ensemble results of gap-filled sums will help to minimize the influence of a single technique and thus lead to a more robust flux aggregation. Furthermore, the estimated overall uncertainty will be more realistic by accounting for the ensemble range of multiple techniques.

Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS), which is based on lookup tables. Furthermore, a predictor importance analysis was performed to evaluate the importance of the different meteorological inputs in estimating ET. On the half-hourly time scale, the deep learning method showed a significant 13.5% decrease in nRMSE (normalized root mean square error) throughout all datasets and gap durations. A substantially smaller standard deviation of mean nRMSE, compared with marginal distribution sampling, was also observed. On the whole-gap time scale (half a day to six days), average nMBE (normalized mean bias error) was similar to that of MDS, whereas standard deviation was improved. Using only air temperature and relative humidity as input variables provided an RMSE that was significantly smaller than that resulting from the MDS method. These results suggest that the deep learning method developed here is reliable and more consistent than the standard gap-filling method and thereby demonstrates the potential of advanced deep learning techniques for improving dynamic time series modeling.

Effect of gap-filling technique and gap location on linear and nonlinear calculations of motion during locomotor activities

BackgroundMarker occlusion during camera-based movement analysis is common. Different interpolation techniques are available for estimating location of missing marker trajectories. Research questionWhat is the effect of gap location and interpolation technique on linear and nonlinear measures for a given kinematic time series? MethodsKinematic data were recorded during motor-assisted elliptical training and treadmill walking. Gap-filling techniques (i.e., Cubic, Makima, Autoregressive, Nearest Neighbor, and No Interpolation) and gap locations experimentally applied to each cycle across initially complete time series (Gap 1: local minimum and maximum peaks; Gap 2: maximum peaks; Gap 3: maximum peaks at negative slope; Gap 4: random locations) were examined during linear (Maxima and Minima joint angles) and nonlinear [maximum Lyapunov exponent (LyE)] measures. ResultsGap-filling technique and gap location influenced values calculated for linear and nonlinear measures of joint motions. When referenced to the gold standard (original data series without gaps), across all joints studied the average % error of Maxima and Minima joint angles and LyE % error were lower when applying Cubic, Makima, Autoregressive, and Nearest Neighbor techniques compared to No Interpolation (p < 0.0001). The % error of Maxima joint angles was lower for Gaps 1, 3, and 4 compared to Gap 2 (p = 0.0003), while % error of Minima joint angles was lower for Gaps 2 and 3, compared to Gaps 1 and 4 (p < 0.0001). An interaction between gap-filling technique and gap location was identified for LyE % error, in which Gap 4 % error was significantly greater during No Interpolation compared to other gap-filling techniques (p < 0.0001). SignificanceFindings can guide selection of appropriate techniques to manage missing kinematic data points in camera-based motion analysis time series. Gap-filling techniques significantly reduced error in calculating select linear and nonlinear measures of variability, with Cubic most consistently resulting in the greatest reduction in error.

Speech enhancement through information gap filling technique

This study investigates English language use with classroom students after applying the Information Gap Filling Technique (IGT) at the Paiguara public school in San Juan-Gualaceo, Ecuador. A sample of 22 students (12 boys and 10 girls) from the third level of BGU was selected. The research was conducted in two phases. The quantitative part used a pre- and post-test of oral interaction and in the qualitative part, a questionnaire with open-ended questions. The findings of the study showed that students increased their use of English in the classroom as a result of the application of IGT in the variables of comprehension, grammar, vocabulary, fluency and pronunciation with values of (0.37, 0.50, 0.27, 0.05, 0.32) respectively. Consequently, the Information Gap-filling technique has proved to be effective in promoting the use of English language in classrooms as a means of communication.

Capacity of plate anchors in clay under sustained uplift

The use of plate anchors on permanent offshore facilities with taut or semi-taut moorings requires a thorough understanding of their long-term capacity under sustained loading. In this paper, a programme of centrifuge tests and numerical simulations is reported to investigate the capacity of a plate anchor deeply embedded in normally consolidated clay during and after vertical sustained loading. Experimental data show that a sustained load higher than 85% of the ultimate monotonic anchor capacity leads to anchor failure, characterised by acceleration of anchor movement with a large accumulation of vertical displacements towards the soil surface. At sustained loads lower than 85% of the monotonic anchor capacity, the undrained anchor capacity increases due to consolidation and hence strengthening of the soil mobilised by the anchor. The maximum increase in capacity relative to the monotonic capacity is 23% when the sustained load is approximately half the monotonic anchor capacity. A gap-filling technique was proposed and incorporated into large deformation finite element analyses, which are shown to reproduce the experimental observations well.

Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT.

Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology.

A New Route to Enhance the Packing Density of Buckypaper for Superior Piezoresistive Sensor Characteristics.

Transforming individual carbon nanotubes (CNTs) into bulk form is necessary for the utilization of the extraordinary properties of CNTs in sensor applications. Individual CNTs are randomly arranged when transformed into the bulk structure in the form of buckypaper. The random arrangement has many pores among individual CNTs, which can be treated as gaps or defects contributing to the degradation of CNT properties in the bulk form. A novel technique of filling these gaps is successfully developed in this study and termed as a gap-filling technique (GFT). The GFT is implemented on SWCNT-based buckypaper in which the pores are filled through small-size MWCNTs, resulting in a ~45.9% improvement in packing density. The GFT is validated through the analysis of packing density along with characterization and surface morphological study of buckypaper using Raman spectrum, particle size analysis, scanning electron microscopy, atomic force microscopy and optical microscopy. The sensor characteristics parameters of buckypaper are investigated using a dynamic mechanical analyzer attached with a digital multimeter. The percentage improvement in the electrical conductivity, tensile gauge factor, tensile strength and failure strain of a GFT-implemented buckypaper sensor are calculated as 4.11 ± 0.61, 44.81 ± 1.72, 49.82 ± 8.21 and 113.36 ± 28.74, respectively.

Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across

Read-across (RAX) is a popular data-gap filling technique that uses category and analogue approaches to predict toxicological endpoints for a target. Despite its increasing relevance, RAX relies on human expert judgement and lacks a reproducible and automated protocol. It also only relies on structural similarity for identifying the analogues, while other aspects are often neglected. In this paper, we propose an automated procedure for the selection of analogues for data gap-filling. Analogues were identified with a decision algorithm that integrates three similarity metrics, each considering different toxicologically relevant aspects (i.e., structural, biological and metabolic similarity). Structural filters based on the presence of maximum common substructures (MCS) and common functional groups were applied to narrow the chemical space for the analogues search. The procedure has been implemented as a workflow in KNIME and is freely available. The workflow provides informative tabular and graphical outputs to support toxicologists and risk assessors in drawing conclusion based on the RAX approach. The procedure has been validated for its predictive power on two datasets related to high-tier in vivo toxicological endpoints, i.e. human hepatotoxicity and drug-induced liver injury (DILI). The validation results gave good accuracy values (i.e., up to 0.79 for the binary hepatotoxicity classification and up to 0.67 for the three-class DILI classification) that were higher than those returned by RAX based on the sole use of structural similarity. Results confirmed the suitability of the procedure as a source of data to support regulatory decision-making.

기후변화 시나리오 자료의 통계적 상세화를 위한 결측자료 추정 기법 개발

기후변화 시나리오 자료의 통계적 상세화를 위한 결측자료 추정 기법 개발

Exploring current read-across applications and needs among selected U.S. Federal Agencies

Read-across is a well-established data gap-filling technique applied for regulatory purposes. In US Environmental Protection Agency's New Chemicals Program under TSCA, read-across has been used extensively for decades, however the extent of application and acceptance of read-across among U.S. federal agencies is less clear. In an effort to build read-across capacity, raise awareness of the state of the science, and work towards a harmonization of read-across approaches across U.S. agencies, a new read-across workgroup was established under the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM). This is one of several ad hoc groups ICCVAM has convened to implement the ICCVAM Strategic Roadmap. In this article, we outline the charge and scope of the workgroup and summarize the current applications, tools used, and needs of the agencies represented on the workgroup for read-across. Of the agencies surveyed, the Environmental Protection Agency had the greatest experience in using read-across whereas other agencies indicated that they would benefit from gaining a perspective of the landscape of the tools and available guidance. Two practical case studies are also described to illustrate how the read-across approaches applied by two agencies vary on account of decision context.

Gap-filling method for suppressing grating lobes in ultrasound imaging: Theory and simulation results.

Sparse array (SA) is an approach to reduce the number of system channels. However, SA suffers from grating lobe (GL) artefacts due to the sparsity of array aperture resulting in degradation of the ultrasound image quality. Based on a given or known data sets of radio frequency (RF) echo acquired from active elements of an array, RF echo data in unknown and/or inactive elements of array can be created virtually and used to suppress the GL artefact in SA. This letter presents gap-filling (GF) approaches to generate channel data, which are not physically acquired. It is demonstrated that the proposed GF technique can reduce the artefacts of SA by filling the gaps in the array aperture. Simulation results show that the GF technique can suppress the GL level of SA by up to 16 dB. Also, the GF technique can improve the image quality of fully sampled arrays with small number of active elements.

Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States

To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM2.5 concentrations. In this work, we develop a daily PM2.5 product at 1 × 1 km2 spatial resolution across the eastern United States (east of 90° W) with the aid of 1 × 1 km2 MAIAC aerosol optical depth (AOD) data, 36 × 36 km2 WRF-Chem output, 1 × 1 km2 land-use type from the National Land Cover Database, and 0.125° × 0.125° ERA-Interim re-analysis meteorology. A gap-filling technique is applied to MAIAC AOD data to construct robust daily estimates of AOD when the satellite data are missing (e.g., areas obstructed by clouds or snow cover). The input data are incorporated into a multiple-linear regression model trained to surface observations of PM2.5 from the EPA Air Quality System (AQS) monitoring network. The model generates a high-fidelity estimate (r2 = 0.75 using a 10-fold random cross-validation) of daily PM2.5 throughout the eastern United States. Of the inputs to the statistical model, WRF-Chem output (r2 = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributor (r2 = 0.52). Daily PM2.5 output from our statistical model can be easily integrated into county-level epidemiological studies. The novelty of this project is that we are able to simulate PM2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution (1 × 1 km2) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km2).

Daily temperature records from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010

Abstract. Near-surface air temperatures were monitored from 2005 to 2010 in a mesoscale network of 230 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18 000 km2, sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km2. This paper presents the multiyear temperature dataset from this study, with minimum, maximum, and mean daily temperature data available at https://doi.org/10.1594/PANGAEA.880611. In this paper, we describe the quality control and processing methods used to clean and filter the data and assess its accuracy. Overall data coverage for the study period is 91 %. We introduce a weather-system-dependent gap-filling technique to estimate the missing 9 % of data. Monthly and seasonal distributions of minimum, maximum, and mean daily temperature lapse rates are shown for the region.

Gap-Filling Algorithm for Ground Surface Temperature Data Measured in Permafrost and Periglacial Environments

Ground surface temperatures (GST) are widely measured in mountain permafrost areas, but their time series data can be interrupted by gaps. Gaps complicate the calculation of aggregates and indices required for analysing temporal and spatial variability between loggers and sites. We present an algorithm to estimate daily mean GST and the resulting uncertainty. The algorithm is designed to automatically fill data gaps in a database of several tens to hundreds of time series, for example, the Swiss Permafrost Monitoring Network (PERMOS). Using numerous randomly generated artificial gaps, we validated the performance of the gap-filling routine in terms of (1) the bias resulting on annual means, (2) thawing and freezing degree-days, and (3) the accuracy of the uncertainty estimation. Although quantile mapping provided the most reliable gap-filling approach overall, linear interpolation between neighbouring values performed equally well for gap durations of up to 3–5 days. Finding the most similar regressors is crucial and also the main source of errors, particularly because of the large spatial and temporal variability of ground and snow properties in high-mountain terrains. Applying the gap-filling technique to the PERMOS GST data increased the total number of complete hydrological years available for analysis by 70 per cent (>450-filled gaps), likely without exceeding a maximal uncertainty of ± 0.25 °C in calculated annual mean values. Copyright © 2016 John Wiley & Sons, Ltd.

Eddy covariance measurements of water vapor and CO2 fluxes above the Erhai Lake

Measurement of turbulence fluxes were performed over the Erhai Lake using eddy covariance (EC) method. Basic physical parameters in the lake-air interaction processes, such as surface albedo of the lake, aerodynamic roughness length, bulk transfer coefficients, etc., were investigated using the EC data in 2012. The characteristics of turbulence fluxes over the lake including momentum flux, sensible heat flux, latent heat flux, and CO2 flux, and their controlling factors were analyzed. The total annual evaporation of the lake was also estimated based on the artificial neural network (ANN) gap-filling technique. Results showed that the total annual evaporation in 2012 was 1165 ± 15 mm, which was larger than the annual precipitation (818 mm). Local circulation between the lake and the surrounding land was found to be significant throughout the year due to the land-lake breeze or the mountain-valley breeze in this area. The prevailing winds of southeasterly and northwesterly were observed throughout the year. The sensible heat flux over this plateau lake usually had a few tens of W m−2, and generally became negative in the afternoon, indicating that heat was transferred from the lake to the atmosphere. The sensible heat flux was governed by the lake-air temperature difference and had its maximum in the early morning. The diurnal variation of the latent heat flux was controlled by vapor pressure deficit with a peak in the afternoon. The latent heat flux was dominant in the partition of available energy in daytime over this lake. The lake acted as a weak CO2 source to the atmosphere except for the midday of summer. Seasonal variations of surface albedo over the lake were related to the solar elevation angle and opacity of the water. Furthermore, compared with the observation data, the surface albedo estimated by CLM4-LISSS model was underestimated in winter and overestimated in summer.