Directional kriging implementation for gridded data interpolation and comparative study with common methods

NASA Astrophysics Data System (ADS)

Mahmoudabadi, H.; Briggs, G.

2016-12-01

Gridded data sets, such as geoid models or datum shift grids, are commonly used in coordinate transformation algorithms. Grid files typically contain known or measured values at regular fixed intervals. The process of computing a value at an unknown location from the values in the grid data set is called "interpolation". Generally, interpolation methods predict a value at a given point by computing a weighted average of the known values in the neighborhood of the point. Geostatistical Kriging is a widely used interpolation method for irregular networks. Kriging interpolation first analyzes the spatial structure of the input data, then generates a general model to describe spatial dependencies. This model is used to calculate values at unsampled locations by finding direction, shape, size, and weight of neighborhood points. Because it is based on a linear formulation for the best estimation, Kriging it the optimal interpolation method in statistical terms. The Kriging interpolation algorithm produces an unbiased prediction, as well as the ability to calculate the spatial distribution of uncertainty, allowing you to estimate the errors in an interpolation for any particular point. Kriging is not widely used in geospatial applications today, especially applications that run on low power devices or deal with large data files. This is due to the computational power and memory requirements of standard Kriging techniques. In this paper, improvements are introduced in directional kriging implementation by taking advantage of the structure of the grid files. The regular spacing of points simplifies finding the neighborhood points and computing their pairwise distances, reducing the the complexity and improving the execution time of the Kriging algorithm. Also, the proposed method iteratively loads small portion of interest areas in different directions to reduce the amount of required memory. This makes the technique feasible on almost any computer processor. Comparison between kriging and other standard interpolation methods demonstrated more accurate estimations in less denser data files.

Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China.

PubMed

Xiao, Yong; Gu, Xiaomin; Yin, Shiyang; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Niu, Yong

2016-01-01

Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.

Modeling of earthquake ground motion in the frequency domain

NASA Astrophysics Data System (ADS)

Thrainsson, Hjortur

In recent years, the utilization of time histories of earthquake ground motion has grown considerably in the design and analysis of civil structures. It is very unlikely, however, that recordings of earthquake ground motion will be available for all sites and conditions of interest. Hence, there is a need for efficient methods for the simulation and spatial interpolation of earthquake ground motion. In addition to providing estimates of the ground motion at a site using data from adjacent recording stations, spatially interpolated ground motions can also be used in design and analysis of long-span structures, such as bridges and pipelines, where differential movement is important. The objective of this research is to develop a methodology for rapid generation of horizontal earthquake ground motion at any site for a given region, based on readily available source, path and site characteristics, or (sparse) recordings. The research includes two main topics: (i) the simulation of earthquake ground motion at a given site, and (ii) the spatial interpolation of earthquake ground motion. In topic (i), models are developed to simulate acceleration time histories using the inverse discrete Fourier transform. The Fourier phase differences, defined as the difference in phase angle between adjacent frequency components, are simulated conditional on the Fourier amplitude. Uniformly processed recordings from recent California earthquakes are used to validate the simulation models, as well as to develop prediction formulas for the model parameters. The models developed in this research provide rapid simulation of earthquake ground motion over a wide range of magnitudes and distances, but they are not intended to replace more robust geophysical models. In topic (ii), a model is developed in which Fourier amplitudes and Fourier phase angles are interpolated separately. A simple dispersion relationship is included in the phase angle interpolation. The accuracy of the interpolation model is assessed using data from the SMART-1 array in Taiwan. The interpolation model provides an effective method to estimate ground motion at a site using recordings from stations located up to several kilometers away. Reliable estimates of differential ground motion are restricted to relatively limited ranges of frequencies and inter-station spacings.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

PubMed Central

Zhou, Shenglu; Su, Quanlong; Yi, Haomin

2017-01-01

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution. PMID:29278363

Conflict Prediction Through Geo-Spatial Interpolation of Radicalization in Syrian Social Media

DTIC Science & Technology

2015-09-24

TRAC-M-TM-15-031 September 2015 Conflict Prediction Through Geo-Spatial Interpolation of Radicalization in Syrian Social Media ...Spatial Interpolation of Radicalization in Syrian Social Media Authors MAJ Adam Haupt Dr. Camber Warren...Spatial Interpolation of Radicalization in Syrian Social 1RAC Project Code 060114 Media 6. AUTHOR(S) MAJ Haupt, Dr. Warren 7. PERFORMING OR

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

Interpolating Non-Parametric Distributions of Hourly Rainfall Intensities Using Random Mixing

NASA Astrophysics Data System (ADS)

Mosthaf, Tobias; Bárdossy, András; Hörning, Sebastian

2015-04-01

The correct spatial interpolation of hourly rainfall intensity distributions is of great importance for stochastical rainfall models. Poorly interpolated distributions may lead to over- or underestimation of rainfall and consequently to wrong estimates of following applications, like hydrological or hydraulic models. By analyzing the spatial relation of empirical rainfall distribution functions, a persistent order of the quantile values over a wide range of non-exceedance probabilities is observed. As the order remains similar, the interpolation weights of quantile values for one certain non-exceedance probability can be applied to the other probabilities. This assumption enables the use of kernel smoothed distribution functions for interpolation purposes. Comparing the order of hourly quantile values over different gauges with the order of their daily quantile values for equal probabilities, results in high correlations. The hourly quantile values also show high correlations with elevation. The incorporation of these two covariates into the interpolation is therefore tested. As only positive interpolation weights for the quantile values assure a monotonically increasing distribution function, the use of geostatistical methods like kriging is problematic. Employing kriging with external drift to incorporate secondary information is not applicable. Nonetheless, it would be fruitful to make use of covariates. To overcome this shortcoming, a new random mixing approach of spatial random fields is applied. Within the mixing process hourly quantile values are considered as equality constraints and correlations with elevation values are included as relationship constraints. To profit from the dependence of daily quantile values, distribution functions of daily gauges are used to set up lower equal and greater equal constraints at their locations. In this way the denser daily gauge network can be included in the interpolation of the hourly distribution functions. The applicability of this new interpolation procedure will be shown for around 250 hourly rainfall gauges in the German federal state of Baden-Württemberg. The performance of the random mixing technique within the interpolation is compared to applicable kriging methods. Additionally, the interpolation of kernel smoothed distribution functions is compared with the interpolation of fitted parametric distributions.

Structure-preserving interpolation of temporal and spatial image sequences using an optical flow-based method.

PubMed

Ehrhardt, J; Säring, D; Handels, H

2007-01-01

Modern tomographic imaging devices enable the acquisition of spatial and temporal image sequences. But, the spatial and temporal resolution of such devices is limited and therefore image interpolation techniques are needed to represent images at a desired level of discretization. This paper presents a method for structure-preserving interpolation between neighboring slices in temporal or spatial image sequences. In a first step, the spatiotemporal velocity field between image slices is determined using an optical flow-based registration method in order to establish spatial correspondence between adjacent slices. An iterative algorithm is applied using the spatial and temporal image derivatives and a spatiotemporal smoothing step. Afterwards, the calculated velocity field is used to generate an interpolated image at the desired time by averaging intensities between corresponding points. Three quantitative measures are defined to evaluate the performance of the interpolation method. The behavior and capability of the algorithm is demonstrated by synthetic images. A population of 17 temporal and spatial image sequences are utilized to compare the optical flow-based interpolation method to linear and shape-based interpolation. The quantitative results show that the optical flow-based method outperforms the linear and shape-based interpolation statistically significantly. The interpolation method presented is able to generate image sequences with appropriate spatial or temporal resolution needed for image comparison, analysis or visualization tasks. Quantitative and qualitative measures extracted from synthetic phantoms and medical image data show that the new method definitely has advantages over linear and shape-based interpolation.

Enhancing GIS Capabilities for High Resolution Earth Science Grids

NASA Astrophysics Data System (ADS)

Koziol, B. W.; Oehmke, R.; Li, P.; O'Kuinghttons, R.; Theurich, G.; DeLuca, C.

2017-12-01

Applications for high performance GIS will continue to increase as Earth system models pursue more realistic representations of Earth system processes. Finer spatial resolution model input and output, unstructured or irregular modeling grids, data assimilation, and regional coordinate systems present novel challenges for GIS frameworks operating in the Earth system modeling domain. This presentation provides an overview of two GIS-driven applications that combine high performance software with big geospatial datasets to produce value-added tools for the modeling and geoscientific community. First, a large-scale interpolation experiment using National Hydrography Dataset (NHD) catchments, a high resolution rectilinear CONUS grid, and the Earth System Modeling Framework's (ESMF) conservative interpolation capability will be described. ESMF is a parallel, high-performance software toolkit that provides capabilities (e.g. interpolation) for building and coupling Earth science applications. ESMF is developed primarily by the NOAA Environmental Software Infrastructure and Interoperability (NESII) group. The purpose of this experiment was to test and demonstrate the utility of high performance scientific software in traditional GIS domains. Special attention will be paid to the nuanced requirements for dealing with high resolution, unstructured grids in scientific data formats. Second, a chunked interpolation application using ESMF and OpenClimateGIS (OCGIS) will demonstrate how spatial subsetting can virtually remove computing resource ceilings for very high spatial resolution interpolation operations. OCGIS is a NESII-developed Python software package designed for the geospatial manipulation of high-dimensional scientific datasets. An overview of the data processing workflow, why a chunked approach is required, and how the application could be adapted to meet operational requirements will be discussed here. In addition, we'll provide a general overview of OCGIS's parallel subsetting capabilities including challenges in the design and implementation of a scientific data subsetter.

Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

NASA Astrophysics Data System (ADS)

Reinhardt, Katja; Samimi, Cyrus

2018-01-01

While climatological data of high spatial resolution are largely available in most developed countries, the network of climatological stations in many other regions of the world still constitutes large gaps. Especially for those regions, interpolation methods are important tools to fill these gaps and to improve the data base indispensible for climatological research. Over the last years, new hybrid methods of machine learning and geostatistics have been developed which provide innovative prospects in spatial predictive modelling. This study will focus on evaluating the performance of 12 different interpolation methods for the wind components \\overrightarrow{u} and \\overrightarrow{v} in a mountainous region of Central Asia. Thereby, a special focus will be on applying new hybrid methods on spatial interpolation of wind data. This study is the first evaluating and comparing the performance of several of these hybrid methods. The overall aim of this study is to determine whether an optimal interpolation method exists, which can equally be applied for all pressure levels, or whether different interpolation methods have to be used for the different pressure levels. Deterministic (inverse distance weighting) and geostatistical interpolation methods (ordinary kriging) were explored, which take into account only the initial values of \\overrightarrow{u} and \\overrightarrow{v} . In addition, more complex methods (generalized additive model, support vector machine and neural networks as single methods and as hybrid methods as well as regression-kriging) that consider additional variables were applied. The analysis of the error indices revealed that regression-kriging provided the most accurate interpolation results for both wind components and all pressure heights. At 200 and 500 hPa, regression-kriging is followed by the different kinds of neural networks and support vector machines and for 850 hPa it is followed by the different types of support vector machine and ordinary kriging. Overall, explanatory variables improve the interpolation results.

Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey

NASA Astrophysics Data System (ADS)

Gorji, Taha; Sertel, Elif; Tanik, Aysegul

2017-12-01

Soil management is an essential concern in protecting soil properties, in enhancing appropriate soil quality for plant growth and agricultural productivity, and in preventing soil erosion. Soil scientists and decision makers require accurate and well-distributed spatially continuous soil data across a region for risk assessment and for effectively monitoring and managing soils. Recently, spatial interpolation approaches have been utilized in various disciplines including soil sciences for analysing, predicting and mapping distribution and surface modelling of environmental factors such as soil properties. The study area selected in this research is Tuz Lake Basin in Turkey bearing ecological and economic importance. Fertile soil plays a significant role in agricultural activities, which is one of the main industries having great impact on economy of the region. Loss of trees and bushes due to intense agricultural activities in some parts of the basin lead to soil erosion. Besides, soil salinization due to both human-induced activities and natural factors has exacerbated its condition regarding agricultural land development. This study aims to compare capability of Local Polynomial Interpolation (LPI) and Radial Basis Functions (RBF) as two interpolation methods for mapping spatial pattern of soil properties including organic matter, phosphorus, lime and boron. Both LPI and RBF methods demonstrated promising results for predicting lime, organic matter, phosphorous and boron. Soil samples collected in the field were used for interpolation analysis in which approximately 80% of data was used for interpolation modelling whereas the remaining for validation of the predicted results. Relationship between validation points and their corresponding estimated values in the same location is examined by conducting linear regression analysis. Eight prediction maps generated from two different interpolation methods for soil organic matter, phosphorus, lime and boron parameters were examined based on R2 and RMSE values. The outcomes indicate that RBF performance in predicting lime, organic matter and boron put forth better results than LPI. However, LPI shows better results for predicting phosphorus.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Morphing of spatial objects in real time with interpolation by functions of radial and orthogonal basis

NASA Astrophysics Data System (ADS)

Kosnikov, Yu N.; Kuzmin, A. V.; Ho, Hoang Thai

2018-05-01

The article is devoted to visualization of spatial objects’ morphing described by the set of unordered reference points. A two-stage model construction is proposed to change object’s form in real time. The first (preliminary) stage is interpolation of the object’s surface by radial basis functions. Initial reference points are replaced by new spatially ordered ones. Reference points’ coordinates change patterns during the process of morphing are assigned. The second (real time) stage is surface reconstruction by blending functions of orthogonal basis. Finite differences formulas are applied to increase the productivity of calculations.

Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions.

PubMed

Wong, Stephen; Hargreaves, Eric L; Baltuch, Gordon H; Jaggi, Jurg L; Danish, Shabbar F

2012-01-01

Microelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery. We evaluated two methods of interpolation (standard vs. kernel) that generated spatial profiles from temporal trends. We compared interpolated spatial profiles to true spatial profiles that were calculated with depth windows, using correlation coefficient analysis. Excellent approximation of true spatial profiles is achieved by interpolation. Kernel-interpolated spatial profiles produced superior correlation coefficient values at optimal kernel widths (r = 0.932-0.940) compared to standard interpolation (r = 0.891). The choice of kernel function and kernel width resulted in trade-offs in smoothing and resolution. Interpolation of feature activity to create spatial profiles from temporal trends is accurate and can standardize and facilitate MER functional localization of subcortical structures. The methods are computationally efficient, enhancing localization without imposing additional constraints on the MER clinical procedure during DBS surgery. Copyright © 2012 S. Karger AG, Basel.

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wood, Andrew W; Leung, Lai R; Sridhar, V

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregationmore » (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less

Topsoil pollution forecasting using artificial neural networks on the example of the abnormally distributed heavy metal at Russian subarctic

NASA Astrophysics Data System (ADS)

Tarasov, D. A.; Buevich, A. G.; Sergeev, A. P.; Shichkin, A. V.; Baglaeva, E. M.

2017-06-01

Forecasting the soil pollution is a considerable field of study in the light of the general concern of environmental protection issues. Due to the variation of content and spatial heterogeneity of pollutants distribution at urban areas, the conventional spatial interpolation models implemented in many GIS packages mostly cannot provide appreciate interpolation accuracy. Moreover, the problem of prediction the distribution of the element with high variability in the concentration at the study site is particularly difficult. The work presents two neural networks models forecasting a spatial content of the abnormally distributed soil pollutant (Cr) at a particular location of the subarctic Novy Urengoy, Russia. A method of generalized regression neural network (GRNN) was compared to a common multilayer perceptron (MLP) model. The proposed techniques have been built, implemented and tested using ArcGIS and MATLAB. To verify the models performances, 150 scattered input data points (pollutant concentrations) have been selected from 8.5 km2 area and then split into independent training data set (105 points) and validation data set (45 points). The training data set was generated for the interpolation using ordinary kriging while the validation data set was used to test their accuracies. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. The predictive accuracy of both models was confirmed to be significantly higher than those achieved by the geostatistical approach (kriging). It is shown that MLP could achieve better accuracy than both kriging and even GRNN for interpolating surfaces.

Measurement and modeling of particulate matter concentrations: Applying spatial analysis and regression techniques to assess air quality.

PubMed

Sajjadi, Seyed Ali; Zolfaghari, Ghasem; Adab, Hamed; Allahabadi, Ahmad; Delsouz, Mehri

2017-01-01

This paper presented the levels of PM 2.5 and PM 10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM 2.5 and PM 10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM 2.5 concentrations in the stations were between 10 and 500 μg/m 3 . Furthermore, the PM 10 concentrations for all of 48 stations ranged from 20 to 1500 μg/m 3 . The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM 2.5 and 25.89 for PM 10 ). The best interpolation method for the particulate matter (PM 2.5 and PM 10 ) seemed to be IDW method. •The PM 10 and PM 2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016.•Concentrations of PM 2.5 and PM 10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000.•Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities.

Prediction of soil attributes through interpolators in a deglaciated environment with complex landforms

NASA Astrophysics Data System (ADS)

Schünemann, Adriano Luis; Inácio Fernandes Filho, Elpídio; Rocha Francelino, Marcio; Rodrigues Santos, Gérson; Thomazini, Andre; Batista Pereira, Antônio; Gonçalves Reynaud Schaefer, Carlos Ernesto

2017-04-01

The knowledge of environmental variables values, in non-sampled sites from a minimum data set can be accessed through interpolation technique. Kriging and the classifier Random Forest algorithm are examples of predictors with this aim. The objective of this work was to compare methods of soil attributes spatialization in a recent deglaciated environment with complex landforms. Prediction of the selected soil attributes (potassium, calcium and magnesium) from ice-free areas were tested by using morphometric covariables, and geostatistical models without these covariables. For this, 106 soil samples were collected at 0-10 cm depth in Keller Peninsula, King George Island, Maritime Antarctica. Soil chemical analysis was performed by the gravimetric method, determining values of potassium, calcium and magnesium for each sampled point. Digital terrain models (DTMs) were obtained by using Terrestrial Laser Scanner. DTMs were generated from a cloud of points with spatial resolutions of 1, 5, 10, 20 and 30 m. Hence, 40 morphometric covariates were generated. Simple Kriging was performed using the R package software. The same data set coupled with morphometric covariates, was used to predict values of the studied attributes in non-sampled sites through Random Forest interpolator. Little differences were observed on the DTMs generated by Simple kriging and Random Forest interpolators. Also, DTMs with better spatial resolution did not improved the quality of soil attributes prediction. Results revealed that Simple Kriging can be used as interpolator when morphometric covariates are not available, with little impact regarding quality. It is necessary to go further in soil chemical attributes prediction techniques, especially in periglacial areas with complex landforms.

Preliminary results of spatial modeling of selected forest health variables in Georgia

Treesearch

Brock Stewart; Chris J. Cieszewski

2009-01-01

Variables relating to forest health monitoring, such as mortality, are difficult to predict and model. We present here the results of fitting various spatial regression models to these variables. We interpolate plot-level values compiled from the Forest Inventory and Analysis National Information Management System (FIA-NIMS) data that are related to forest health....

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors.

PubMed

Ma, Xiaolei; Luan, Sen; Du, Bowen; Yu, Bin

2017-09-21

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks.

[Spatial distribution prediction of surface soil Pb in a battery contaminated site].

PubMed

Liu, Geng; Niu, Jun-Jie; Zhang, Chao; Zhao, Xin; Guo, Guan-Lin

2014-12-01

In order to enhance the reliability of risk estimation and to improve the accuracy of pollution scope determination in a battery contaminated site with the soil characteristic pollutant Pb, four spatial interpolation models, including Combination Prediction Model (OK(LG) + TIN), kriging model (OK(BC)), Inverse Distance Weighting model (IDW), and Spline model were employed to compare their effects on the spatial distribution and pollution assessment of soil Pb. The results showed that Pb concentration varied significantly and the data was severely skewed. The variation coefficient of the site was higher in the local region. OK(LG) + TIN was found to be more accurate than the other three models in predicting the actual pollution situations of the contaminated site. The prediction accuracy of other models was lower, due to the effect of the principle of different models and datum feature. The interpolation results of OK(BC), IDW and Spline could not reflect the detailed characteristics of seriously contaminated areas, and were not suitable for mapping and spatial distribution prediction of soil Pb in this site. This study gives great contributions and provides useful references for defining the remediation boundary and making remediation decision of contaminated sites.

3-d interpolation in object perception: evidence from an objective performance paradigm.

PubMed

Kellman, Philip J; Garrigan, Patrick; Shipley, Thomas F; Yin, Carol; Machado, Liana

2005-06-01

Object perception requires interpolation processes that connect visible regions despite spatial gaps. Some research has suggested that interpolation may be a 3-D process, but objective performance data and evidence about the conditions leading to interpolation are needed. The authors developed an objective performance paradigm for testing 3-D interpolation and tested a new theory of 3-D contour interpolation, termed 3-D relatability. The theory indicates for a given edge which orientations and positions of other edges in space may be connected to it by interpolation. Results of 5 experiments showed that processing of orientation relations in 3-D relatable displays was superior to processing in 3-D nonrelatable displays and that these effects depended on object formation. 3-D interpolation and 3-D relatabilty are discussed in terms of their implications for computational and neural models of object perception, which have typically been based on 2-D-orientation-sensitive units. ((c) 2005 APA, all rights reserved).

Summary on several key techniques in 3D geological modeling.

PubMed

Mei, Gang

2014-01-01

Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized.

High-temperature behavior of a deformed Fermi gas obeying interpolating statistics.

PubMed

Algin, Abdullah; Senay, Mustafa

2012-04-01

An outstanding idea originally introduced by Greenberg is to investigate whether there is equivalence between intermediate statistics, which may be different from anyonic statistics, and q-deformed particle algebra. Also, a model to be studied for addressing such an idea could possibly provide us some new consequences about the interactions of particles as well as their internal structures. Motivated mainly by this idea, in this work, we consider a q-deformed Fermi gas model whose statistical properties enable us to effectively study interpolating statistics. Starting with a generalized Fermi-Dirac distribution function, we derive several thermostatistical functions of a gas of these deformed fermions in the thermodynamical limit. We study the high-temperature behavior of the system by analyzing the effects of q deformation on the most important thermostatistical characteristics of the system such as the entropy, specific heat, and equation of state. It is shown that such a deformed fermion model in two and three spatial dimensions exhibits the interpolating statistics in a specific interval of the model deformation parameter 0 < q < 1. In particular, for two and three spatial dimensions, it is found from the behavior of the third virial coefficient of the model that the deformation parameter q interpolates completely between attractive and repulsive systems, including the free boson and fermion cases. From the results obtained in this work, we conclude that such a model could provide much physical insight into some interacting theories of fermions, and could be useful to further study the particle systems with intermediate statistics.

Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

PubMed

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-03-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

PubMed Central

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-01-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction.

PubMed

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-02-27

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 10 16 electrons/m²) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

PubMed Central

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-01-01

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m2) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. PMID:28264424

Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

PubMed

Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

2015-12-01

Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

Evaluation of rainfall structure on hydrograph simulation: Comparison of radar and interpolated methods, a study case in a tropical catchment

NASA Astrophysics Data System (ADS)

Velasquez, N.; Ochoa, A.; Castillo, S.; Hoyos Ortiz, C. D.

2017-12-01

The skill of river discharge simulation using hydrological models strongly depends on the quality and spatio-temporal representativeness of precipitation during storm events. All precipitation measurement strategies have their own strengths and weaknesses that translate into discharge simulation uncertainties. Distributed hydrological models are based on evolving rainfall fields in the same time scale as the hydrological simulation. In general, rainfall measurements from a dense and well maintained rain gauge network provide a very good estimation of the total volume for each rainfall event, however, the spatial structure relies on interpolation strategies introducing considerable uncertainty in the simulation process. On the other hand, rainfall retrievals from radar reflectivity achieve a better spatial structure representation but with higher uncertainty in the surface precipitation intensity and volume depending on the vertical rainfall characteristics and radar scan strategy. To assess the impact of both rainfall measurement methodologies on hydrological simulations, and in particular the effects of the rainfall spatio-temporal variability, a numerical modeling experiment is proposed including the use of a novel QPE (Quantitative Precipitation Estimation) method based on disdrometer data in order to estimate surface rainfall from radar reflectivity. The experiment is based on the simulation of 84 storms, the hydrological simulations are carried out using radar QPE and two different interpolation methods (IDW and TIN), and the assessment of simulated peak flow. Results show significant rainfall differences between radar QPE and the interpolated fields, evidencing a poor representation of storms in the interpolated fields, which tend to miss the precise location of the intense precipitation cores, and to artificially generate rainfall in some areas of the catchment. Regarding streamflow modelling, the potential improvement achieved by using radar QPE depends on the density of the rain gauge network and its distribution relative to the precipitation events. The results for the 84 storms show a better model skill using radar QPE than the interpolated fields. Results using interpolated fields are highly affected by the dominant rainfall type and the basin scale.

Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter PM2:5 in the contiguous U.S. using parallel programming and k-d tree.

PubMed

Li, Lixin; Losser, Travis; Yorke, Charles; Piltner, Reinhard

2014-09-03

Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results.

Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree

PubMed Central

Li, Lixin; Losser, Travis; Yorke, Charles; Piltner, Reinhard

2014-01-01

Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results. PMID:25192146

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

PubMed

Wang, Wei; Zheng, Bin; Chen, Binlin; An, Yaoming; Jiang, Xiaoming; Li, Zhangyong

2018-02-01

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m 3 , the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

Assimilation of the AVISO Altimetry Data into the Ocean Dynamics Model with a High Spatial Resolution Using Ensemble Optimal Interpolation (EnOI)

NASA Astrophysics Data System (ADS)

Kaurkin, M. N.; Ibrayev, R. A.; Belyaev, K. P.

2018-01-01

A parallel realization of the Ensemble Optimal Interpolation (EnOI) data assimilation (DA) method in conjunction with the eddy-resolving global circulation model is implemented. The results of DA experiments in the North Atlantic with the assimilation of the Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) data from the Jason-1 satellite are analyzed. The results of simulation are compared with the independent temperature and salinity data from the ARGO drifters.

Application of spatial methods to identify areas with lime requirement in eastern Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Kisic, Ivica; Mesic, Milan; Zgorelec, Zeljka; Percin, Aleksandra; Pereira, Paulo

2016-04-01

With more than 50% of acid soils in all agricultural land in Croatia, soil acidity is recognized as a big problem. Low soil pH leads to a series of negative phenomena in plant production and therefore as a compulsory measure for reclamation of acid soils is liming, recommended on the base of soil analysis. The need for liming is often erroneously determined only on the basis of the soil pH, because the determination of cation exchange capacity, the hydrolytic acidity and base saturation is a major cost to producers. Therefore, in Croatia, as well as some other countries, the amount of liming material needed to ameliorate acid soils is calculated by considering their hydrolytic acidity. For this research, several interpolation methods were tested to identify the best spatial predictor of hidrolitic acidity. The purpose of this study was to: test several interpolation methods to identify the best spatial predictor of hidrolitic acidity; and to determine the possibility of using multivariate geostatistics in order to reduce the number of needed samples for determination the hydrolytic acidity, all with an aim that the accuracy of the spatial distribution of liming requirement is not significantly reduced. Soil pH (in KCl) and hydrolytic acidity (Y1) is determined in the 1004 samples (from 0-30 cm) randomized collected in agricultural fields near Orahovica in eastern Croatia. This study tested 14 univariate interpolation models (part of ArcGIS software package) in order to provide most accurate spatial map of hydrolytic acidity on a base of: all samples (Y1 100%), and the datasets with 15% (Y1 85%), 30% (Y1 70%) and 50% fewer samples (Y1 50%). Parallel to univariate interpolation methods, the precision of the spatial distribution of the Y1 was tested by the co-kriging method with exchangeable acidity (pH in KCl) as a covariate. The soils at studied area had an average pH (KCl) 4,81, while the average Y1 10,52 cmol+ kg-1. These data suggest that liming is necessary agrotechnical measure for soil conditioning. The results show that ordinary kriging was most accurate univariate interpolation method with smallest error (RMSE) in all four data sets, while the least precise showed Radial Basis Functions (Thin Plate Spline and Inverse Multiquadratic). Furthermore, it is noticeable a trend of increasing errors (RMSE) with a reduced number of samples tested on the most accurate univariate interpolation model: 3,096 (Y1 100%), 3,258 (Y1 85%), 3,317 (Y1 70%), 3,546 (Y1 50%). The best-fit semivariograms show a strong spatial dependence in Y1 100% (Nugget/Sill 20.19) and Y1 85% (Nugget/Sill 23.83), while a further reduction of the number of samples resulted with moderate spatial dependence (Y1 70% -35,85% and Y1 50% - 32,01). Co-kriging method resulted in a reduction in RMSE compared with univariate interpolation methods for each data set with: 2,054, 1,731 and 1,734 for Y1 85%, Y1 70%, Y1 50%, respectively. The results show the possibility for reducing sampling costs by using co-kriging method which is useful from the practical viewpoint. Reduced number of samples by half for determination of hydrolytic acidity in the interaction with the soil pH provides a higher precision for variable liming compared to the univariate interpolation methods of the entire set of data. These data provide new opportunities to reduce costs in the practical plant production in Croatia.

Summary on Several Key Techniques in 3D Geological Modeling

PubMed Central

2014-01-01

Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized. PMID:24772029

Improved interpolation of meteorological forcings for hydrologic applications in a Swiss Alpine region

NASA Astrophysics Data System (ADS)

Tobin, Cara; Nicotina, Ludovico; Parlange, Marc B.; Berne, Alexis; Rinaldo, Andrea

2011-04-01

SummaryThis paper presents a comparative study on the mapping of temperature and precipitation fields in complex Alpine terrain. Its relevance hinges on the major impact that inadequate interpolations of meteorological forcings bear on the accuracy of hydrologic predictions regardless of the specifics of the models, particularly during flood events. Three flood events measured in the Swiss Alps are analyzed in detail to determine the interpolation methods which best capture the distribution of intense, orographically-induced precipitation. The interpolation techniques comparatively examined include: Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Kriging with External Drift (KED). Geostatistical methods rely on a robust anisotropic variogram for the definition of the spatial rainfall structure. Results indicate that IDW tends to significantly underestimate rainfall volumes whereas OK and KED methods capture spatial patterns and rainfall volumes induced by storm advection. Using numerical weather forecasts and elevation data as covariates for precipitation, we provide evidence for KED to outperform the other methods. Most significantly, the use of elevation as auxiliary information in KED of temperatures demonstrates minimal errors in estimated instantaneous rainfall volumes and provides instantaneous lapse rates which better capture snow/rainfall partitioning. Incorporation of the temperature and precipitation input fields into a hydrological model used for operational management was found to provide vastly improved outputs with respect to measured discharge volumes and flood peaks, with notable implications for flood modeling.

Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing

NASA Technical Reports Server (NTRS)

Rossi, Richard E.; Dungan, Jennifer L.; Beck, Louisa R.

1994-01-01

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

PubMed Central

Ma, Xiaolei; Du, Bowen; Yu, Bin

2017-01-01

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks. PMID:28934164

Optimization and comparison of three spatial interpolation methods for electromagnetic levels in the AM band within an urban area.

PubMed

Rufo, Montaña; Antolín, Alicia; Paniagua, Jesús M; Jiménez, Antonio

2018-04-01

A comparative study was made of three methods of interpolation - inverse distance weighting (IDW), spline and ordinary kriging - after optimization of their characteristic parameters. These interpolation methods were used to represent the electric field levels for three emission frequencies (774kHz, 900kHz, and 1107kHz) and for the electrical stimulation quotient, Q E , characteristic of complex electromagnetic environments. Measurements were made with a spectrum analyser in a village in the vicinity of medium-wave radio broadcasting antennas. The accuracy of the models was quantified by comparing their predictions with levels measured at the control points not used to generate the models. The results showed that optimizing the characteristic parameters of each interpolation method allows any of them to be used. However, the best results in terms of the regression coefficient between each model's predictions and the actual control point field measurements were for the IDW method. Copyright © 2018 Elsevier Inc. All rights reserved.

Rtop - an R package for interpolation of data with a variable spatial support - examples from river networks

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Laaha, Gregor; Koffler, Daniel; Blöschl, Günter; Pebesma, Edzer; Parajka, Juraj; Viglione, Alberto

2013-04-01

Geostatistical methods have been applied only to a limited extent for spatial interpolation in applications where the observations have an irregular support, such as runoff characteristics or population health data. Several studies have shown the potential of such methods (Gottschalk 1993, Sauquet et al. 2000, Gottschalk et al. 2006, Skøien et al. 2006, Goovaerts 2008), but these developments have so far not led to easily accessible, versatile, easy to apply and open source software. Based on the top-kriging approach suggested by Skøien et al. (2006), we will here present the package rtop, which has been implemented in the statistical environment R (R Core Team 2012). Taking advantage of the existing methods in R for analysis of spatial objects (Bivand et al. 2008), and the extensive possibilities for visualizing the results, rtop makes it easy to apply geostatistical interpolation methods when observations have a non-point spatial support. Although the package is flexible regarding data input, the main application so far has been for interpolation along river networks. We will present some examples showing how the package can easily be used for such interpolation. The model will soon be uploaded to CRAN, but is in the meantime also available from R-forge and can be installed by: > install.packages("rtop", repos="http://R-Forge.R-project.org") Bivand, R.S., Pebesma, E.J. & Gómez-Rubio, V., 2008. Applied spatial data analysis with r: Springer. Goovaerts, P., 2008. Kriging and semivariogram deconvolution in the presence of irregular geographical units. Mathematical Geosciences, 40 (1), 101-128. Gottschalk, L., 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., Krasovskaia, I., Leblois, E. & Sauquet, E., 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. R Core Team, 2012. R: A language and environment for statistical computing. Vienna, Austria, ISBN 3-900051-07-0. Sauquet, E., Gottschalk, L. & Leblois, E., 2000. Mapping average annual runoff: A hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J.O., Merz, R. & Blöschl, G., 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

The Use of Geostatistics in the Study of Floral Phenology of Vulpia geniculata (L.) Link

PubMed Central

León Ruiz, Eduardo J.; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered troughout the city and low mountains in the “Sierra de Córdoba” were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to ellaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps. PMID:22629169

The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.

PubMed

León Ruiz, Eduardo J; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered throughout the city and low mountains in the "Sierra de Córdoba" were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to elaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.

Interpolation between spatial frameworks: an application of process convolution to estimating neighbourhood disease prevalence.

PubMed

Congdon, Peter

2014-04-01

Health data may be collected across one spatial framework (e.g. health provider agencies), but contrasts in health over another spatial framework (neighbourhoods) may be of policy interest. In the UK, population prevalence totals for chronic diseases are provided for populations served by general practitioner practices, but not for neighbourhoods (small areas of circa 1500 people), raising the question whether data for one framework can be used to provide spatially interpolated estimates of disease prevalence for the other. A discrete process convolution is applied to this end and has advantages when there are a relatively large number of area units in one or other framework. Additionally, the interpolation is modified to take account of the observed neighbourhood indicators (e.g. hospitalisation rates) of neighbourhood disease prevalence. These are reflective indicators of neighbourhood prevalence viewed as a latent construct. An illustrative application is to prevalence of psychosis in northeast London, containing 190 general practitioner practices and 562 neighbourhoods, including an assessment of sensitivity to kernel choice (e.g. normal vs exponential). This application illustrates how a zero-inflated Poisson can be used as the likelihood model for a reflective indicator.

Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

PubMed

Juang, K W; Lee, D Y; Ellsworth, T R

2001-01-01

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

PubMed Central

Ng, Edward

2017-01-01

Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM2.5 and PM10 with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM. PMID:28869527

Spatial and spectral interpolation of ground-motion intensity measure observations

USGS Publications Warehouse

Worden, Charles; Thompson, Eric M.; Baker, Jack W.; Bradley, Brendon A.; Luco, Nicolas; Wilson, David

2018-01-01

Following a significant earthquake, ground‐motion observations are available for a limited set of locations and intensity measures (IMs). Typically, however, it is desirable to know the ground motions for additional IMs and at locations where observations are unavailable. Various interpolation methods are available, but because IMs or their logarithms are normally distributed, spatially correlated, and correlated with each other at a given location, it is possible to apply the conditional multivariate normal (MVN) distribution to the problem of estimating unobserved IMs. In this article, we review the MVN and its application to general estimation problems, and then apply the MVN to the specific problem of ground‐motion IM interpolation. In particular, we present (1) a formulation of the MVN for the simultaneous interpolation of IMs across space and IM type (most commonly, spectral response at different oscillator periods) and (2) the inclusion of uncertain observation data in the MVN formulation. These techniques, in combination with modern empirical ground‐motion models and correlation functions, provide a flexible framework for estimating a variety of IMs at arbitrary locations.

A Comparative Study of Three Spatial Interpolation Methodologies for the Analysis of Air Pollution Concentrations in Athens, Greece

NASA Astrophysics Data System (ADS)

Deligiorgi, Despina; Philippopoulos, Kostas; Thanou, Lelouda; Karvounis, Georgios

2010-01-01

Spatial interpolation in air pollution modeling is the procedure for estimating ambient air pollution concentrations at unmonitored locations based on available observations. The selection of the appropriate methodology is based on the nature and the quality of the interpolated data. In this paper, an assessment of three widely used interpolation methodologies is undertaken in order to estimate the errors involved. For this purpose, air quality data from January 2001 to December 2005, from a network of seventeen monitoring stations, operating at the greater area of Athens in Greece, are used. The Nearest Neighbor and the Liner schemes were applied to the mean hourly observations, while the Inverse Distance Weighted (IDW) method to the mean monthly concentrations. The discrepancies of the estimated and measured values are assessed for every station and pollutant, using the correlation coefficient, the scatter diagrams and the statistical residuals. The capability of the methods to estimate air quality data in an area with multiple land-use types and pollution sources, such as Athens, is discussed.

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2017-12-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2018-01-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

Modeling the Spatial and Temporal Variation of Monthly and Seasonal Precipitation on the Nevada Test Site and Vicinity, 1960-2006

USGS Publications Warehouse

Blainey, Joan B.; Webb, Robert H.; Magirl, Christopher S.

2007-01-01

The Nevada Test Site (NTS), located in the climatic transition zone between the Mojave and Great Basin Deserts, has a network of precipitation gages that is unusually dense for this region. This network measures monthly and seasonal variation in a landscape with diverse topography. Precipitation data from 125 climate stations on or near the NTS were used to spatially interpolate precipitation for each month during the period of 1960 through 2006 at high spatial resolution (30 m). The data were collected at climate stations using manual and/or automated techniques. The spatial interpolation method, applied to monthly accumulations of precipitation, is based on a distance-weighted multivariate regression between the amount of precipitation and the station location and elevation. This report summarizes the temporal and spatial characteristics of the available precipitation records for the period 1960 to 2006, examines the temporal and spatial variability of precipitation during the period of record, and discusses some extremes in seasonal precipitation on the NTS.

The geostatistic-based spatial distribution variations of soil salts under long-term wastewater irrigation.

PubMed

Wu, Wenyong; Yin, Shiyang; Liu, Honglu; Niu, Yong; Bao, Zhe

2014-10-01

The purpose of this study was to determine and evaluate the spatial changes in soil salinity by using geostatistical methods. The study focused on the suburb area of Beijing, where urban development led to water shortage and accelerated wastewater reuse to farm irrigation for more than 30 years. The data were then processed by GIS using three different interpolation techniques of ordinary kriging (OK), disjunctive kriging (DK), and universal kriging (UK). The normality test and overall trend analysis were applied for each interpolation technique to select the best fitted model for soil parameters. Results showed that OK was suitable for soil sodium adsorption ratio (SAR) and Na(+) interpolation; UK was suitable for soil Cl(-) and pH; DK was suitable for soil Ca(2+). The nugget-to-sill ratio was applied to evaluate the effects of structural and stochastic factors. The maps showed that the areas of non-saline soil and slight salinity soil accounted for 6.39 and 93.61%, respectively. The spatial distribution and accumulation of soil salt were significantly affected by the irrigation probabilities and drainage situation under long-term wastewater irrigation.

Bayesian Tracking of Emerging Epidemics Using Ensemble Optimal Statistical Interpolation

PubMed Central

Cobb, Loren; Krishnamurthy, Ashok; Mandel, Jan; Beezley, Jonathan D.

2014-01-01

We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately. The EnOSI and EnKF tracking methods worked well on the main body of the simulated spatial epidemic, but the EnOSI was able to detect and track a distant secondary focus of infection that the EnKF missed entirely. PMID:25113590

GMI-IPS: Python Processing Software for Aircraft Campaigns

NASA Technical Reports Server (NTRS)

Damon, M. R.; Strode, S. A.; Steenrod, S. D.; Prather, M. J.

2018-01-01

NASA's Atmospheric Tomography Mission (ATom) seeks to understand the impact of anthropogenic air pollution on gases in the Earth's atmosphere. Four flight campaigns are being deployed on a seasonal basis to establish a continuous global-scale data set intended to improve the representation of chemically reactive gases in global atmospheric chemistry models. The Global Modeling Initiative (GMI), is creating chemical transport simulations on a global scale for each of the ATom flight campaigns. To meet the computational demands required to translate the GMI simulation data to grids associated with the flights from the ATom campaigns, the GMI ICARTT Processing Software (GMI-IPS) has been developed and is providing key functionality for data processing and analysis in this ongoing effort. The GMI-IPS is written in Python and provides computational kernels for data interpolation and visualization tasks on GMI simulation data. A key feature of the GMI-IPS, is its ability to read ICARTT files, a text-based file format for airborne instrument data, and extract the required flight information that defines regional and temporal grid parameters associated with an ATom flight. Perhaps most importantly, the GMI-IPS creates ICARTT files containing GMI simulated data, which are used in collaboration with ATom instrument teams and other modeling groups. The initial main task of the GMI-IPS is to interpolate GMI model data to the finer temporal resolution (1-10 seconds) of a given flight. The model data includes basic fields such as temperature and pressure, but the main focus of this effort is to provide species concentrations of chemical gases for ATom flights. The software, which uses parallel computation techniques for data intensive tasks, linearly interpolates each of the model fields to the time resolution of the flight. The temporally interpolated data is then saved to disk, and is used to create additional derived quantities. In order to translate the GMI model data to the spatial grid of the flight path as defined by the pressure, latitude, and longitude points at each flight time record, a weighted average is then calculated from the nearest neighbors in two dimensions (latitude, longitude). Using SciPya's Regular Grid Interpolator, interpolation functions are generated for the GMI model grid and the calculated weighted averages. The flight path points are then extracted from the ATom ICARTT instrument file, and are sent to the multi-dimensional interpolating functions to generate GMI field quantities along the spatial path of the flight. The interpolated field quantities are then written to a ICARTT data file, which is stored for further manipulation. The GMI-IPS is aware of a generic ATom ICARTT header format, containing basic information for all flight campaigns. The GMI-IPS includes logic to edit metadata for the derived field quantities, as well as modify the generic header data such as processing dates and associated instrument files. The ICARTT interpolated data is then appended to the modified header data, and the ICARTT processing is complete for the given flight and ready for collaboration. The output ICARTT data adheres to the ICARTT file format standards V1.1. The visualization component of the GMI-IPS uses Matplotlib extensively and has several functions ranging in complexity. First, it creates a model background curtain for the flight (time versus model eta levels) with the interpolated flight data superimposed on the curtain. Secondly, it creates a time-series plot of the interpolated flight data. Lastly, the visualization component creates averaged 2D model slices (longitude versus latitude) with overlaid flight track circles at key pressure levels. The GMI-IPS consists of a handful of classes and supporting functionality that have been generalized to be compatible with any ICARTT file that adheres to the base class definition. The base class represents a generic ICARTT entry, only defining a single time entry and 3D spatial positioning parameters. Other classes inherit from this base class; several classes for input ICARTT instrument files, which contain the necessary flight positioning information as a basis for data processing, as well as other classes for output ICARTT files, which contain the interpolated model data. Utility classes provide functionality for routine procedures such as: comparing field names among ICARTT files, reading ICARTT entries from a data file and storing them in data structures, and returning a reduced spatial grid based on a collection of ICARTT entries. Although the GMI-IPS is compatible with GMI model data, it can be adapted with reasonable effort for any simulation that creates Hierarchical Data Format (HDF) files. The same can be said of its adaptability to ICARTT files outside of the context of the ATom mission. The GMI-IPS contains just under 30,000 lines of code, eight classes, and a dozen drivers and utility programs. It is maintained with GIT source code management and has been used to deliver processed GMI model data for the ATom campaigns that have taken place to date.

Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

NASA Astrophysics Data System (ADS)

Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

2016-05-01

Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

Spatial Interpolation of Fine Particulate Matter Concentrations Using the Shortest Wind-Field Path Distance

PubMed Central

Li, Longxiang; Gong, Jianhua; Zhou, Jieping

2014-01-01

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health. PMID:24798197

Spatial interpolation of fine particulate matter concentrations using the shortest wind-field path distance.

PubMed

Li, Longxiang; Gong, Jianhua; Zhou, Jieping

2014-01-01

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health.

A comparison of spatial interpolation methods for soil temperature over a complex topographical region

NASA Astrophysics Data System (ADS)

Wu, Wei; Tang, Xiao-Ping; Ma, Xue-Qing; Liu, Hong-Bin

2016-08-01

Soil temperature variability data provide valuable information on understanding land-surface ecosystem processes and climate change. This study developed and analyzed a spatial dataset of monthly mean soil temperature at a depth of 10 cm over a complex topographical region in southwestern China. The records were measured at 83 stations during the period of 1961-2000. Nine approaches were compared for interpolating soil temperature. The accuracy indicators were root mean square error (RMSE), modelling efficiency (ME), and coefficient of residual mass (CRM). The results indicated that thin plate spline with latitude, longitude, and elevation gave the best performance with RMSE varying between 0.425 and 0.592 °C, ME between 0.895 and 0.947, and CRM between -0.007 and 0.001. A spatial database was developed based on the best model. The dataset showed that larger seasonal changes of soil temperature were from autumn to winter over the region. The northern and eastern areas with hilly and low-middle mountains experienced larger seasonal changes.

Two Methods to Derive Ground-level Concentrations of PM2.5 with Improved Accuracy in the North China, Calibrating MODIS AOD and CMAQ Model Predictions

NASA Astrophysics Data System (ADS)

Lyu, Baolei; Hu, Yongtao; Chang, Howard; Russell, Armistead; Bai, Yuqi

2016-04-01

Reliable and accurate characterizations of ground-level PM2.5 concentrations are essential to understand pollution sources and evaluate human exposures etc. Monitoring network could only provide direct point-level observations at limited locations. At the locations without monitors, there are generally two ways to estimate the pollution levels of PM2.5. One is observations of aerosol properties from the satellite-based remote sensing, such as Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The other one is from deterministic atmospheric chemistry models, such as the Community Multi-Scale Air Quality Model (CMAQ). In this study, we used a statistical spatio-temporal downscaler to calibrate the two datasets to monitor observations to derive fine-scale ground-level concentrations of PM2.5 with improved accuracy. We treated both MODIS AOD and CMAQ model predictions as biased proxy estimations of PM2.5 pollution levels. The downscaler proposed a Bayesian framework to model the spatially and temporally varying coefficients of the two types of estimations in the linear regression setting, in order to correct biases. Especially for calibrating MODIS AOD, a city-specific linear model was established to fill the missing AOD values, and a novel interpolation-based variable, i.e. PM2.5 Spatial Interpolator, was introduced to account for the spatial dependence among grid cells. We selected the heavy polluted and populated North China as our study area, in a grid setting of 81×81 12-km cells. For the evaluation of calibration performance for retrieved MODIS AOD, the R2 was 0.61 by the full model with PM2.5 Spatial Interpolator being presented, and was 0.48 with PM2.5 Spatial Interpolator not being presented. The constructed AOD values effectively predicted PM2.5 concentrations under our model structure, with R2=0.78. For the evaluation of calibrated CMAQ predictions, the R2 was 0.51, a little less than that of calibrated AOD. Finally we obtained two sets of calibrated estimations of ground-level PM2.5 concentrations with complete spatial coverage. By comparing the two datasets, we found that the prediction from AOD have a little smoother texture than that from CMAQ. The former also predicted larger heavy pollution area in the southern Hebei province than the latter, but in a small margin. In general, they have pretty similar spatial patterns, indicating the reliability of our data fusion method. In summary, the statistical spatio-temporal downscaler could provide improvements on MODIS AOD and CMAQ's predictions on PM2.5 pollution levels. Future work would focus on fusing three datasets, as aforementioned monitor observations, MODIS AOD and CMAQ predictions, to derive predictions of ground-level PM2.5 pollution levels with even increased accuracy.

A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models

NASA Technical Reports Server (NTRS)

Giunta, Anthony A.; Watson, Layne T.

1998-01-01

Two methods of creating approximation models are compared through the calculation of the modeling accuracy on test problems involving one, five, and ten independent variables. Here, the test problems are representative of the modeling challenges typically encountered in realistic engineering optimization problems. The first approximation model is a quadratic polynomial created using the method of least squares. This type of polynomial model has seen considerable use in recent engineering optimization studies due to its computational simplicity and ease of use. However, quadratic polynomial models may be of limited accuracy when the response data to be modeled have multiple local extrema. The second approximation model employs an interpolation scheme known as kriging developed in the fields of spatial statistics and geostatistics. This class of interpolating model has the flexibility to model response data with multiple local extrema. However, this flexibility is obtained at an increase in computational expense and a decrease in ease of use. The intent of this study is to provide an initial exploration of the accuracy and modeling capabilities of these two approximation methods.

Spatially continuous interpolation of water stage and water depths using the Everglades depth estimation network (EDEN)

USGS Publications Warehouse

Pearlstine, Leonard; Higer, Aaron; Palaseanu, Monica; Fujisaki, Ikuko; Mazzotti, Frank

2007-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (2000-present), online water-stage and water-depth information for the entire freshwater portion of the Greater Everglades. Continuous daily spatial interpolations of the EDEN network stage data are presented on a 400-square-meter grid spacing. EDEN offers a consistent and documented dataset that can be used by scientists and managers to (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan (CERP) The target users are biologists and ecologists examining trophic level responses to hydrodynamic changes in the Everglades.

Spatial interpolation quality assessments for soil sensor transect datasets

USDA-ARS?s Scientific Manuscript database

Near-ground geophysical soil sensors provide extremely valuable information for precision agriculture applications. Indeed, their readings can be used as proxy for many soil parameters. Typically, leave-one-out (loo) cross-validation (CV) of spatial interpolation of sensor data returns overly optimi...

Method for Pre-Conditioning a Measured Surface Height Map for Model Validation

NASA Technical Reports Server (NTRS)

Sidick, Erkin

2012-01-01

This software allows one to up-sample or down-sample a measured surface map for model validation, not only without introducing any re-sampling errors, but also eliminating the existing measurement noise and measurement errors. Because the re-sampling of a surface map is accomplished based on the analytical expressions of Zernike-polynomials and a power spectral density model, such re-sampling does not introduce any aliasing and interpolation errors as is done by the conventional interpolation and FFT-based (fast-Fourier-transform-based) spatial-filtering method. Also, this new method automatically eliminates the measurement noise and other measurement errors such as artificial discontinuity. The developmental cycle of an optical system, such as a space telescope, includes, but is not limited to, the following two steps: (1) deriving requirements or specs on the optical quality of individual optics before they are fabricated through optical modeling and simulations, and (2) validating the optical model using the measured surface height maps after all optics are fabricated. There are a number of computational issues related to model validation, one of which is the "pre-conditioning" or pre-processing of the measured surface maps before using them in a model validation software tool. This software addresses the following issues: (1) up- or down-sampling a measured surface map to match it with the gridded data format of a model validation tool, and (2) eliminating the surface measurement noise or measurement errors such that the resulted surface height map is continuous or smoothly-varying. So far, the preferred method used for re-sampling a surface map is two-dimensional interpolation. The main problem of this method is that the same pixel can take different values when the method of interpolation is changed among the different methods such as the "nearest," "linear," "cubic," and "spline" fitting in Matlab. The conventional, FFT-based spatial filtering method used to eliminate the surface measurement noise or measurement errors can also suffer from aliasing effects. During re-sampling of a surface map, this software preserves the low spatial-frequency characteristic of a given surface map through the use of Zernike-polynomial fit coefficients, and maintains mid- and high-spatial-frequency characteristics of the given surface map by the use of a PSD model derived from the two-dimensional PSD data of the mid- and high-spatial-frequency components of the original surface map. Because this new method creates the new surface map in the desired sampling format from analytical expressions only, it does not encounter any aliasing effects and does not cause any discontinuity in the resultant surface map.

Inter-comparison of interpolated background nitrogen dioxide concentrations across Greater Manchester, UK

NASA Astrophysics Data System (ADS)

Lindley, S. J.; Walsh, T.

There are many modelling methods dedicated to the estimation of spatial patterns in pollutant concentrations, each with their distinctive advantages and disadvantages. The derivation of a surface of air quality values from monitoring data alone requires the conversion of point-based data from a limited number of monitoring stations to a continuous surface using interpolation. Since interpolation techniques involve the estimation of data at un-sampled points based on calculated relationships between data measured at a number of known sample points, they are subject to some uncertainty, both in terms of the values estimated and their spatial distribution. These uncertainties, which are incorporated into many empirical and semi-empirical mapping methodologies, could be recognised in any further usage of the data and also in the assessment of the extent of an exceedence of an air quality standard and the degree of exposure this may represent. There is a wide range of available interpolation techniques and the differences in the characteristics of these result in variations in the output surfaces estimated from the same set of input points. The work presented in this paper provides an examination of uncertainties through the application of a number of interpolation techniques available in standard GIS packages to a case study nitrogen dioxide data set for the Greater Manchester conurbation in northern England. The implications of the use of different techniques are discussed through application to hourly concentrations during an air quality episode and annual average concentrations in 2001. Patterns of concentrations demonstrate considerable differences in the estimated spatial pattern of maxima as the combined effects of chemical processes, topography and meteorology. In the case of air quality episodes, the considerable spatial variability of concentrations results in large uncertainties in the surfaces produced but these uncertainties vary widely from area to area. In view of the uncertainties with classical techniques research is ongoing to develop alternative methods which should in time help improve the suite of tools available to air quality managers.

Spatial Estimation of Sub-Hour Global Horizontal Irradiance Based on Official Observations and Remote Sensors

PubMed Central

Gutierrez-Corea, Federico-Vladimir; Manso-Callejo, Miguel-Angel; Moreno-Regidor, María-Pilar; Velasco-Gómez, Jesús

2014-01-01

This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of ±2 standard deviations). PMID:24732102

Spatial estimation of sub-hour Global Horizontal Irradiance based on official observations and remote sensors.

PubMed

Gutierrez-Corea, Federico-Vladimir; Manso-Callejo, Miguel-Angel; Moreno-Regidor, María-Pilar; Velasco-Gómez, Jesús

2014-04-11

This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of ±2 standard deviations).

3-D Interpolation in Object Perception: Evidence from an Objective Performance Paradigm

ERIC Educational Resources Information Center

Kellman, Philip J.; Garrigan, Patrick; Shipley, Thomas F.; Yin, Carol; Machado, Liana

2005-01-01

Object perception requires interpolation processes that connect visible regions despite spatial gaps. Some research has suggested that interpolation may be a 3-D process, but objective performance data and evidence about the conditions leading to interpolation are needed. The authors developed an objective performance paradigm for testing 3-D…

Spatial interpolation of river channel topography using the shortest temporal distance

NASA Astrophysics Data System (ADS)

Zhang, Yanjun; Xian, Cuiling; Chen, Huajin; Grieneisen, Michael L.; Liu, Jiaming; Zhang, Minghua

2016-11-01

It is difficult to interpolate river channel topography due to complex anisotropy. As the anisotropy is often caused by river flow, especially the hydrodynamic and transport mechanisms, it is reasonable to incorporate flow velocity into topography interpolator for decreasing the effect of anisotropy. In this study, two new distance metrics defined as the time taken by water flow to travel between two locations are developed, and replace the spatial distance metric or Euclidean distance that is currently used to interpolate topography. One is a shortest temporal distance (STD) metric. The temporal distance (TD) of a path between two nodes is calculated by spatial distance divided by the tangent component of flow velocity along the path, and the STD is searched using the Dijkstra algorithm in all possible paths between two nodes. The other is a modified shortest temporal distance (MSTD) metric in which both the tangent and normal components of flow velocity were combined. They are used to construct the methods for the interpolation of river channel topography. The proposed methods are used to generate the topography of Wuhan Section of Changjiang River and compared with Universal Kriging (UK) and Inverse Distance Weighting (IDW). The results clearly showed that the STD and MSTD based on flow velocity were reliable spatial interpolators. The MSTD, followed by the STD, presents improvement in prediction accuracy relative to both UK and IDW.

Daily air temperature interpolated at high spatial resolution over a large mountainous region

USGS Publications Warehouse

Dodson, R.; Marks, D.

1997-01-01

Two methods are investigated for interpolating daily minimum and maximum air temperatures (Tmin and Tmax) at a 1 km spatial resolution over a large mountainous region (830 000 km2) in the U.S. Pacific Northwest. The methods were selected because of their ability to (1) account for the effect of elevation on temperature and (2) efficiently handle large volumes of data. The first method, the neutral stability algorithm (NSA), used the hydrostatic and potential temperature equations to convert measured temperatures and elevations to sea-level potential temperatures. The potential temperatures were spatially interpolated using an inverse-squared-distance algorithm and then mapped to the elevation surface of a digital elevation model (DEM). The second method, linear lapse rate adjustment (LLRA), involved the same basic procedure as the NSA, but used a constant linear lapse rate instead of the potential temperature equation. Cross-validation analyses were performed using the NSA and LLRA methods to interpolate Tmin and Tmax each day for the 1990 water year, and the methods were evaluated based on mean annual interpolation error (IE). The NSA method showed considerable bias for sites associated with vertical extrapolation. A correction based on climate station/grid cell elevation differences was developed and found to successfully remove the bias. The LLRA method was tested using 3 lapse rates, none of which produced a serious extrapolation bias. The bias-adjusted NSA and the 3 LLRA methods produced almost identical levels of accuracy (mean absolute errors between 1.2 and 1.3??C), and produced very similar temperature surfaces based on image difference statistics. In terms of accuracy, speed, and ease of implementation, LLRA was chosen as the best of the methods tested.

Interpolating precipitation and its relation to runoff and non-point source pollution.

PubMed

Chang, Chia-Ling; Lo, Shang-Lien; Yu, Shaw-L

2005-01-01

When rainfall spatially varies, complete rainfall data for each region with different rainfall characteristics are very important. Numerous interpolation methods have been developed for estimating unknown spatial characteristics. However, no interpolation method is suitable for all circumstances. In this study, several methods, including the arithmetic average method, the Thiessen Polygons method, the traditional inverse distance method, and the modified inverse distance method, were used to interpolate precipitation. The modified inverse distance method considers not only horizontal distances but also differences between the elevations of the region with no rainfall records and of its surrounding rainfall stations. The results show that when the spatial variation of rainfall is strong, choosing a suitable interpolation method is very important. If the rainfall is uniform, the precipitation estimated using any interpolation method would be quite close to the actual precipitation. When rainfall is heavy in locations with high elevation, the rainfall changes with the elevation. In this situation, the modified inverse distance method is much more effective than any other method discussed herein for estimating the rainfall input for WinVAST to estimate runoff and non-point source pollution (NPSP). When the spatial variation of rainfall is random, regardless of the interpolation method used to yield rainfall input, the estimation errors of runoff and NPSP are large. Moreover, the relationship between the relative error of the predicted runoff and predicted pollutant loading of SS is high. However, the pollutant concentration is affected by both runoff and pollutant export, so the relationship between the relative error of the predicted runoff and the predicted pollutant concentration of SS may be unstable.

Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services

NASA Astrophysics Data System (ADS)

Wu, Changshan

Public transit service is a promising transportation mode because of its potential to address urban sustainability. Current ridership of public transit, however, is very low in most urban regions, particularly those in the United States. This woeful transit ridership can be attributed to many factors, among which poor service quality is key. Given this, there is a need for transit planning and analysis to improve service quality. Traditionally, spatially aggregate data are utilized in transit analysis and planning. Examples include data associated with the census, zip codes, states, etc. Few studies, however, address the influences of spatially aggregate data on transit planning results. In this research, previous studies in transit planning that use spatially aggregate data are reviewed. Next, problems associated with the utilization of aggregate data, the so-called modifiable areal unit problem (MAUP), are detailed and the need for fine resolution data to support public transit planning is argued. Fine resolution data is generated using intelligent interpolation techniques with the help of remote sensing imagery. In particular, impervious surface fraction, an important socio-economic indicator, is estimated through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, Ohio in the United States. Four endmembers, low albedo, high albedo, vegetation, and soil are selected to model heterogeneous urban land cover. Impervious surface fraction is estimated by analyzing low and high albedo endmembers. With the derived impervious surface fraction, three spatial interpolation methods, spatial regression, dasymetric mapping, and cokriging, are developed to interpolate detailed population density. Results suggest that cokriging applied to impervious surface is a better alternative for estimating fine resolution population density. With the derived fine resolution data, a multiple route maximal covering/shortest path (MRMCSP) model is proposed to address the tradeoff between public transit service quality and access coverage in an established bus-based transit system. Results show that it is possible to improve current transit service quality by eliminating redundant or underutilized service stops. This research illustrates that fine resolution data can be efficiently generated to support urban planning, management and analysis. Further, this detailed data may necessitate the development of new spatial optimization models for use in analysis.

Spatial variability of soil carbon, pH, available phosphorous and potassium in organic farm located in Mediterranean Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Pereira, Paulo; Šeput, Miranda

2016-04-01

Soil organic carbon (SOC), pH, available phosphorus (P), and potassium (K) are some of the most important factors to soil fertility. These soil parameters are highly variable in space and time, with implications to crop production. The aim of this work is study the spatial variability of SOC, pH, P and K in an organic farm located in river Rasa valley (Croatia). A regular grid (100 x 100 m) was designed and 182 samples were collected on Silty Clay Loam soil. P, K and SOC showed moderate heterogeneity with coefficient of variation (CV) of 21.6%, 32.8% and 51.9%, respectively. Soil pH record low spatial variability with CV of 1.5%. Soil pH, P and SOC did not follow normal distribution. Only after a Box-Cox transformation, data respected the normality requirements. Directional exponential models were the best fitted and used to describe spatial autocorrelation. Soil pH, P and SOC showed strong spatial dependence with nugget to sill ratio with 13.78%, 0.00% and 20.29%, respectively. Only K recorded moderate spatial dependence. Semivariogram ranges indicate that future sampling interval could be 150 - 200 m in order to reduce sampling costs. Fourteen different interpolation models for mapping soil properties were tested. The method with lowest Root Mean Square Error was the most appropriated to map the variable. The results showed that radial basis function models (Spline with Tension and Completely Regularized Spline) for P and K were the best predictors, while Thin Plate Spline and inverse distance weighting models were the least accurate. The best interpolator for pH and SOC was the local polynomial with the power of 1, while the least accurate were Thin Plate Spline. According to soil nutrient maps investigated area record very rich supply with K while P supply was insufficient on largest part of area. Soil pH maps showed mostly neutral reaction while individual parts of alkaline soil indicate the possibility of penetration of seawater and salt accumulation in the soil profile. Future research should focus on spatial patterns on soil pH, electrical conductivity and sodium adsorption ratio. Keywords: geostatistics, semivariogram, interpolation models, soil chemical properties

Distributed snow modeling suitable for use with operational data for the American River watershed.

NASA Astrophysics Data System (ADS)

Shamir, E.; Georgakakos, K. P.

2004-12-01

The mountainous terrain of the American River watershed (~4300 km2) at the Western slope of the Northern Sierra Nevada is subject to significant variability in the atmospheric forcing that controls the snow accumulation and ablations processes (i.e., precipitation, surface temperature, and radiation). For a hydrologic model that attempts to predict both short- and long-term streamflow discharges, a plausible description of the seasonal and intermittent winter snow pack accumulation and ablation is crucial. At present the NWS-CNRFC operational snow model is implemented in a semi distributed manner (modeling unit of about 100-1000 km2) and therefore lump distinct spatial variability of snow processes. In this study we attempt to account for the precipitation, temperature, and radiation spatial variability by constructing a distributed snow accumulation and melting model suitable for use with commonly available sparse data. An adaptation of the NWS-Snow17 energy and mass balance that is used operationally at the NWS River Forecast Centers is implemented at 1 km2 grid cells with distributed input and model parameters. The input to the model (i.e., precipitation and surface temperature) is interpolated from observed point data. The surface temperature was interpolated over the basin based on adiabatic lapse rates using topographic information whereas the precipitation was interpolated based on maps of climatic mean annual rainfall distribution acquired from PRISM. The model parameters that control the melting rate due to radiation were interpolated based on aspect. The study was conducted for the entire American basin for the snow seasons of 1999-2000. Validation of the Snow Water Equivalent (SWE) prediction is done by comparing to observation from 12 snow Sensors. The Snow Cover Area (SCA) prediction was evaluated by comparing to remotely sensed 500m daily snow cover derived from MODIS. The results that the distribution of snow over the area is well captured and the quantity compared to the snow gauges are well estimated in the high elevation.

Effect of the precipitation interpolation method on the performance of a snowmelt runoff model

NASA Astrophysics Data System (ADS)

Jacquin, Alexandra

2014-05-01

Uncertainties on the spatial distribution of precipitation seriously affect the reliability of the discharge estimates produced by watershed models. Although there is abundant research evaluating the goodness of fit of precipitation estimates obtained with different gauge interpolation methods, few studies have focused on the influence of the interpolation strategy on the response of watershed models. The relevance of this choice may be even greater in the case of mountain catchments, because of the influence of orography on precipitation. This study evaluates the effect of the precipitation interpolation method on the performance of conceptual type snowmelt runoff models. The HBV Light model version 4.0.0.2, operating at daily time steps, is used as a case study. The model is applied in Aconcagua at Chacabuquito catchment, located in the Andes Mountains of Central Chile. The catchment's area is 2110[Km2] and elevation ranges from 950[m.a.s.l.] to 5930[m.a.s.l.] The local meteorological network is sparse, with all precipitation gauges located below 3000[m.a.s.l.] Precipitation amounts corresponding to different elevation zones are estimated through areal averaging of precipitation fields interpolated from gauge data. Interpolation methods applied include kriging with external drift (KED), optimal interpolation method (OIM), Thiessen polygons (TP), multiquadratic functions fitting (MFF) and inverse distance weighting (IDW). Both KED and OIM are able to account for the existence of a spatial trend in the expectation of precipitation. By contrast, TP, MFF and IDW, traditional methods widely used in engineering hydrology, cannot explicitly incorporate this information. Preliminary analysis confirmed that these methods notably underestimate precipitation in the study catchment, while KED and OIM are able to reduce the bias; this analysis also revealed that OIM provides more reliable estimations than KED in this region. Using input precipitation obtained by each method, HBV parameters are calibrated with respect to Nash-Sutcliffe efficiency. The performance of HBV in the study catchment is not satisfactory. Although volumetric errors are modest, efficiency values are lower than 70%. Discharge estimates resulting from the application of TP, MFF and IDW obtain similar model efficiencies and volumetric errors. These error statistics moderately improve if KED or OIM are used instead. Even though the quality of precipitation estimates of distinct interpolation methods is dissimilar, the results of this study show that these differences do not necessarily produce noticeable changes in HBV's model performance statistics. This situation arises because the calibration of the model parameters allows some degree of compensation of deficient areal precipitation estimates, mainly through the adjustment of model simulated evaporation and glacier melt, as revealed by the analysis of water balances. In general, even if there is a good agreement between model estimated and observed discharge, this information is not sufficient to assert that the internal hydrological processes of the catchment are properly simulated by a watershed model. Other calibration criteria should be incorporated if a more reliable representation of these processes is desired. Acknowledgements: This research was funded by FONDECYT, Research Project 1110279. The HBV Light software used in this study was kindly provided by J. Seibert, Department of Geography, University of Zürich.

Spatial interpolation techniques using R

EPA Science Inventory

Interpolation techniques are used to predict the cell values of a raster based on sample data points. For example, interpolation can be used to predict the distribution of sediment particle size throughout an estuary based on discrete sediment samples. We demonstrate some inter...

Selection of Optimal Auxiliary Soil Nutrient Variables for Cokriging Interpolation

PubMed Central

Song, Genxin; Zhang, Jing; Wang, Ke

2014-01-01

In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes. PMID:24927129

Empirical Assessment of Spatial Prediction Methods for Location Cost Adjustment Factors

PubMed Central

Migliaccio, Giovanni C.; Guindani, Michele; D'Incognito, Maria; Zhang, Linlin

2014-01-01

In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables. PMID:25018582

USE OF AUXILIARY DATA FOR SPATIAL INTERPOLATION OF OZONE EXPOSURE IN SOUTHEASTERN FORESTS

EPA Science Inventory

In order to assess the impact of tropospheric ozone on forests, it is necessary to quantify ozone exposure on regional scales. Since ozone monitoring stations are widely scattered and mostly concentrate in urban and suburban areas, some form of spatial interpolation is necessary ...

Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective

PubMed Central

Zou, Bin; Luo, Yanqing; Wan, Neng; Zheng, Zhong; Sternberg, Troy; Liao, Yilan

2015-01-01

Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs ‘information entropy’, an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping. PMID:25731103

Precipitation interpolation in mountainous areas

NASA Astrophysics Data System (ADS)

Kolberg, Sjur

2015-04-01

Different precipitation interpolation techniques as well as external drift covariates are tested and compared in a 26000 km2 mountainous area in Norway, using daily data from 60 stations. The main method of assessment is cross-validation. Annual precipitation in the area varies from below 500 mm to more than 2000 mm. The data were corrected for wind-driven undercatch according to operational standards. While temporal evaluation produce seemingly acceptable at-station correlation values (on average around 0.6), the average daily spatial correlation is less than 0.1. Penalising also bias, Nash-Sutcliffe R2 values are negative for spatial correspondence, and around 0.15 for temporal. Despite largely violated assumptions, plain Kriging produces better results than simple inverse distance weighting. More surprisingly, the presumably 'worst-case' benchmark of no interpolation at all, simply averaging all 60 stations for each day, actually outperformed the standard interpolation techniques. For logistic reasons, high altitudes are under-represented in the gauge network. The possible effect of this was investigated by a) fitting a precipitation lapse rate as an external drift, and b) applying a linear model of orographic enhancement (Smith and Barstad, 2004). These techniques improved the results only marginally. The gauge density in the region is one for each 433 km2; higher than the overall density of the Norwegian national network. Admittedly the cross-validation technique reduces the gauge density, still the results suggest that we are far from able to provide hydrological models with adequate data for the main driving force.

Investigation of the interpolation method to improve the distributed strain measurement accuracy in optical frequency domain reflectometry systems.

PubMed

Cui, Jiwen; Zhao, Shiyuan; Yang, Di; Ding, Zhenyang

2018-02-20

We use a spectrum interpolation technique to improve the distributed strain measurement accuracy in a Rayleigh-scatter-based optical frequency domain reflectometry sensing system. We demonstrate that strain accuracy is not limited by the "uncertainty principle" that exists in the time-frequency analysis. Different interpolation methods are investigated and used to improve the accuracy of peak position of the cross-correlation and, therefore, improve the accuracy of the strain. Interpolation implemented by padding zeros on one side of the windowed data in the spatial domain, before the inverse fast Fourier transform, is found to have the best accuracy. Using this method, the strain accuracy and resolution are both improved without decreasing the spatial resolution. The strain of 3 μϵ within the spatial resolution of 1 cm at the position of 21.4 m is distinguished, and the measurement uncertainty is 3.3 μϵ.

Hand skin reconstruction from skeletal landmarks.

PubMed

Lefèvre, P; Van Sint Jan, S; Beauthier, J P; Rooze, M

2007-11-01

Many studies related to three-dimensional facial reconstruction have been previously reported. On the other hand, no extensive work has been found in the literature about hand reconstruction as an identification method. In this paper, the feasibility of virtual reconstruction of hand skin based on (1) its skeleton and (2) another hand skin and skeleton used as template was assessed. One cadaver hand and one volunteer's hand have been used. For the two hands, computer models of the bones and skin were obtained from computerized tomography. A customized software allowed locating spatial coordinates of bony anatomical landmarks on the models. From these landmarks, the spatial relationships between the models were determined and used to interpolate the missing hand skin. The volume of the interpolated skin was compared to the real skin obtained from medical imaging for validation. Results seem to indicate that such a method is of interest to give forensic investigators morphological clues related to an individual hand skin based on its skeleton. Further work is in progress to finalize the method.

Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent LER Calculations

NASA Astrophysics Data System (ADS)

Fasnacht, Z.; Qin, W.; Haffner, D. P.; Loyola, D. G.; Joiner, J.; Krotkov, N. A.; Vasilkov, A. P.; Spurr, R. J. D.

2017-12-01

In order to estimate surface reflectance used in trace gas retrieval algorithms, radiative transfer models (RTM) such as the Vector Linearized Discrete Ordinate Radiative Transfer Model (VLIDORT) can be used to simulate the top of the atmosphere (TOA) radiances with advanced models of surface properties. With large volumes of satellite data, these model simulations can become computationally expensive. Look up table interpolation can improve the computational cost of the calculations, but the non-linear nature of the radiances requires a dense node structure if interpolation errors are to be minimized. In order to reduce our computational effort and improve the performance of look-up tables, neural networks can be trained to predict these radiances. We investigate the impact of using look-up table interpolation versus a neural network trained using the smart sampling technique, and show that neural networks can speed up calculations and reduce errors while using significantly less memory and RTM calls. In future work we will implement a neural network in operational processing to meet growing demands for reflectance modeling in support of high spatial resolution satellite missions.

Image interpolation and denoising for division of focal plane sensors using Gaussian processes.

PubMed

Gilboa, Elad; Cunningham, John P; Nehorai, Arye; Gruev, Viktor

2014-06-16

Image interpolation and denoising are important techniques in image processing. These methods are inherent to digital image acquisition as most digital cameras are composed of a 2D grid of heterogeneous imaging sensors. Current polarization imaging employ four different pixelated polarization filters, commonly referred to as division of focal plane polarization sensors. The sensors capture only partial information of the true scene, leading to a loss of spatial resolution as well as inaccuracy of the captured polarization information. Interpolation is a standard technique to recover the missing information and increase the accuracy of the captured polarization information. Here we focus specifically on Gaussian process regression as a way to perform a statistical image interpolation, where estimates of sensor noise are used to improve the accuracy of the estimated pixel information. We further exploit the inherent grid structure of this data to create a fast exact algorithm that operates in ����(N(3/2)) (vs. the naive ���� (N³)), thus making the Gaussian process method computationally tractable for image data. This modeling advance and the enabling computational advance combine to produce significant improvements over previously published interpolation methods for polarimeters, which is most pronounced in cases of low signal-to-noise ratio (SNR). We provide the comprehensive mathematical model as well as experimental results of the GP interpolation performance for division of focal plane polarimeter.

Rainfall Observed Over Bangladesh 2000-2008: A Comparison of Spatial Interpolation Methods

NASA Astrophysics Data System (ADS)

Pervez, M.; Henebry, G. M.

2010-12-01

In preparation for a hydrometeorological study of freshwater resources in the greater Ganges-Brahmaputra region, we compared the results of four methods of spatial interpolation applied to point measurements of daily rainfall over Bangladesh during a seven year period (2000-2008). Two univariate (inverse distance weighted and spline-regularized and tension) and two multivariate geostatistical (ordinary kriging and kriging with external drift) methods were used to interpolate daily observations from a network of 221 rain gauges across Bangladesh spanning an area of 143,000 sq km. Elevation and topographic index were used as the covariates in the geostatistical methods. The validity of the interpolated maps was analyzed through cross-validation. The quality of the methods was assessed through the Pearson and Spearman correlations and root mean square error measurements of accuracy in cross-validation. Preliminary results indicated that the univariate methods performed better than the geostatistical methods at daily scales, likely due to the relatively dense sampled point measurements and a weak correlation between the rainfall and covariates at daily scales in this region. Inverse distance weighted produced the better results than the spline. For the days with extreme or high rainfall—spatially and quantitatively—the correlation between observed and interpolated estimates appeared to be high (r2 ~ 0.6 RMSE ~ 10mm), although for low rainfall days the correlations were poor (r2 ~ 0.1 RMSE ~ 3mm). The performance quality of these methods was influenced by the density of the sample point measurements, the quantity of the observed rainfall along with spatial extent, and an appropriate search radius defining the neighboring points. Results indicated that interpolated rainfall estimates at daily scales may introduce uncertainties in the successive hydrometeorological analysis. Interpolations at 5-day, 10-day, 15-day, and monthly time scales are currently under investigation.

Interpolation methods and the accuracy of lattice-Boltzmann mesh refinement

DOE PAGES

Guzik, Stephen M.; Weisgraber, Todd H.; Colella, Phillip; ...

2013-12-10

A lattice-Boltzmann model to solve the equivalent of the Navier-Stokes equations on adap- tively refined grids is presented. A method for transferring information across interfaces between different grid resolutions was developed following established techniques for finite- volume representations. This new approach relies on a space-time interpolation and solving constrained least-squares problems to ensure conservation. The effectiveness of this method at maintaining the second order accuracy of lattice-Boltzmann is demonstrated through a series of benchmark simulations and detailed mesh refinement studies. These results exhibit smaller solution errors and improved convergence when compared with similar approaches relying only on spatial interpolation. Examplesmore » highlighting the mesh adaptivity of this method are also provided.« less

Spatial interpolation of solar global radiation

NASA Astrophysics Data System (ADS)

Lussana, C.; Uboldi, F.; Antoniazzi, C.

2010-09-01

Solar global radiation is defined as the radiant flux incident onto an area element of the terrestrial surface. Its direct knowledge plays a crucial role in many applications, from agrometeorology to environmental meteorology. The ARPA Lombardia's meteorological network includes about one hundred of pyranometers, mostly distributed in the southern part of the Alps and in the centre of the Po Plain. A statistical interpolation method based on an implementation of the Optimal Interpolation is applied to the hourly average of the solar global radiation observations measured by the ARPA Lombardia's network. The background field is obtained using SMARTS (The Simple Model of the Atmospheric Radiative Transfer of Sunshine, Gueymard, 2001). The model is initialised by assuming clear sky conditions and it takes into account the solar position and orography related effects (shade and reflection). The interpolation of pyranometric observations introduces in the analysis fields information about cloud presence and influence. A particular effort is devoted to prevent observations affected by large errors of different kinds (representativity errors, systematic errors, gross errors) from entering the analysis procedure. The inclusion of direct cloud information from satellite observations is also planned.

Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal.

PubMed

Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J

2016-08-15

In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their environment are needed to improve human well-being. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2016-01-01

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity. PMID:27415623

Ordinary kriging vs inverse distance weighting: spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico.

PubMed

Zarco-Perello, Salvador; Simões, Nuno

2017-01-01

Information about the distribution and abundance of the habitat-forming sessile organisms in marine ecosystems is of great importance for conservation and natural resource managers. Spatial interpolation methodologies can be useful to generate this information from in situ sampling points, especially in circumstances where remote sensing methodologies cannot be applied due to small-scale spatial variability of the natural communities and low light penetration in the water column. Interpolation methods are widely used in environmental sciences; however, published studies using these methodologies in coral reef science are scarce. We compared the accuracy of the two most commonly used interpolation methods in all disciplines, inverse distance weighting (IDW) and ordinary kriging (OK), to predict the distribution and abundance of hard corals, octocorals, macroalgae, sponges and zoantharians and identify hotspots of these habitat-forming organisms using data sampled at three different spatial scales (5, 10 and 20 m) in Madagascar reef, Gulf of Mexico. The deeper sandy environments of the leeward and windward regions of Madagascar reef were dominated by macroalgae and seconded by octocorals. However, the shallow rocky environments of the reef crest had the highest richness of habitat-forming groups of organisms; here, we registered high abundances of octocorals and macroalgae, with sponges, Millepora alcicornis and zoantharians dominating in some patches, creating high levels of habitat heterogeneity. IDW and OK generated similar maps of distribution for all the taxa; however, cross-validation tests showed that IDW outperformed OK in the prediction of their abundances. When the sampling distance was at 20 m, both interpolation techniques performed poorly, but as the sampling was done at shorter distances prediction accuracies increased, especially for IDW. OK had higher mean prediction errors and failed to correctly interpolate the highest abundance values measured in situ , except for macroalgae, whereas IDW had lower mean prediction errors and high correlations between predicted and measured values in all cases when sampling was every 5 m. The accurate spatial interpolations created using IDW allowed us to see the spatial variability of each taxa at a biological and spatial resolution that remote sensing would not have been able to produce. Our study sets the basis for further research projects and conservation management in Madagascar reef and encourages similar studies in the region and other parts of the world where remote sensing technologies are not suitable for use.

Ordinary kriging vs inverse distance weighting: spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico

PubMed Central

Simões, Nuno

2017-01-01

Information about the distribution and abundance of the habitat-forming sessile organisms in marine ecosystems is of great importance for conservation and natural resource managers. Spatial interpolation methodologies can be useful to generate this information from in situ sampling points, especially in circumstances where remote sensing methodologies cannot be applied due to small-scale spatial variability of the natural communities and low light penetration in the water column. Interpolation methods are widely used in environmental sciences; however, published studies using these methodologies in coral reef science are scarce. We compared the accuracy of the two most commonly used interpolation methods in all disciplines, inverse distance weighting (IDW) and ordinary kriging (OK), to predict the distribution and abundance of hard corals, octocorals, macroalgae, sponges and zoantharians and identify hotspots of these habitat-forming organisms using data sampled at three different spatial scales (5, 10 and 20 m) in Madagascar reef, Gulf of Mexico. The deeper sandy environments of the leeward and windward regions of Madagascar reef were dominated by macroalgae and seconded by octocorals. However, the shallow rocky environments of the reef crest had the highest richness of habitat-forming groups of organisms; here, we registered high abundances of octocorals and macroalgae, with sponges, Millepora alcicornis and zoantharians dominating in some patches, creating high levels of habitat heterogeneity. IDW and OK generated similar maps of distribution for all the taxa; however, cross-validation tests showed that IDW outperformed OK in the prediction of their abundances. When the sampling distance was at 20 m, both interpolation techniques performed poorly, but as the sampling was done at shorter distances prediction accuracies increased, especially for IDW. OK had higher mean prediction errors and failed to correctly interpolate the highest abundance values measured in situ, except for macroalgae, whereas IDW had lower mean prediction errors and high correlations between predicted and measured values in all cases when sampling was every 5 m. The accurate spatial interpolations created using IDW allowed us to see the spatial variability of each taxa at a biological and spatial resolution that remote sensing would not have been able to produce. Our study sets the basis for further research projects and conservation management in Madagascar reef and encourages similar studies in the region and other parts of the world where remote sensing technologies are not suitable for use. PMID:29204321

Modeling current climate conditions for forest pest risk assessment

Treesearch

Frank H. Koch; John W. Coulston

2010-01-01

Current information on broad-scale climatic conditions is essential for assessing potential distribution of forest pests. At present, sophisticated spatial interpolation approaches such as the Parameter-elevation Regressions on Independent Slopes Model (PRISM) are used to create high-resolution climatic data sets. Unfortunately, these data sets are based on 30-year...

Optimal Interpolation scheme to generate reference crop evapotranspiration

NASA Astrophysics Data System (ADS)

Tomas-Burguera, Miquel; Beguería, Santiago; Vicente-Serrano, Sergio; Maneta, Marco

2018-05-01

We used an Optimal Interpolation (OI) scheme to generate a reference crop evapotranspiration (ETo) grid, forcing meteorological variables, and their respective error variance in the Iberian Peninsula for the period 1989-2011. To perform the OI we used observational data from the Spanish Meteorological Agency (AEMET) and outputs from a physically-based climate model. To compute ETo we used five OI schemes to generate grids for the five observed climate variables necessary to compute ETo using the FAO-recommended form of the Penman-Monteith equation (FAO-PM). The granularity of the resulting grids are less sensitive to variations in the density and distribution of the observational network than those generated by other interpolation methods. This is because our implementation of the OI method uses a physically-based climate model as prior background information about the spatial distribution of the climatic variables, which is critical for under-observed regions. This provides temporal consistency in the spatial variability of the climatic fields. We also show that increases in the density and improvements in the distribution of the observational network reduces substantially the uncertainty of the climatic and ETo estimates. Finally, a sensitivity analysis of observational uncertainties and network densification suggests the existence of a trade-off between quantity and quality of observations.

Timescape: a simple space-time interpolation geostatistical Algorithm

NASA Astrophysics Data System (ADS)

Ciolfi, Marco; Chiocchini, Francesca; Gravichkova, Olga; Pisanelli, Andrea; Portarena, Silvia; Scartazza, Andrea; Brugnoli, Enrico; Lauteri, Marco

2016-04-01

Environmental sciences include both time and space variability in their datasets. Some established tools exist for both spatial interpolation and time series analysis alone, but mixing space and time variability calls for compromise: Researchers are often forced to choose which is the main source of variation, neglecting the other. We propose a simple algorithm, which can be used in many fields of Earth and environmental sciences when both time and space variability must be considered on equal grounds. The algorithm has already been implemented in Java language and the software is currently available at https://sourceforge.net/projects/timescapeglobal/ (it is published under GNU-GPL v3.0 Free Software License). The published version of the software, Timescape Global, is focused on continent- to Earth-wide spatial domains, using global longitude-latitude coordinates for samples localization. The companion Timescape Local software is currently under development ad will be published with an open license as well; it will use projected coordinates for a local to regional space scale. The basic idea of the Timescape Algorithm consists in converting time into a sort of third spatial dimension, with the addition of some causal constraints, which drive the interpolation including or excluding observations according to some user-defined rules. The algorithm is applicable, as a matter of principle, to anything that can be represented with a continuous variable (a scalar field, technically speaking). The input dataset should contain position, time and observed value of all samples. Ancillary data can be included in the interpolation as well. After the time-space conversion, Timescape follows basically the old-fashioned IDW (Inverse Distance Weighted) interpolation Algorithm, although users have a wide choice of customization options that, at least partially, overcome some of the known issues of IDW. The three-dimensional model produced by the Timescape Algorithm can be explored in many ways, including the extraction of time series at fixed locations and GIS layers at constant times, allowing for the inclusion of the model in the users' established workflow. The software requirements are relatively modest since it has been purposely designed for potential users in various research field with a limited computing power at their disposal. Any respectful modern PC or laptop can run it. Users however need a separate database for sample data and models storage because these can be quite bulky in terms of data output: a single model can be composed of several billions of voxels (three-dimensional discrete cells, a sort of 3D pixels). Running times range from a few minutes for sketch models to some days of evaluation for a full-size model, depending on the users' hardware and model size.

Airborne laser scanning for forest health status assessment and radiative transfer modelling

NASA Astrophysics Data System (ADS)

Novotny, Jan; Zemek, Frantisek; Pikl, Miroslav; Janoutova, Ruzena

2013-04-01

Structural parameters of forest stands/ecosystems are an important complementary source of information to spectral signatures obtained from airborne imaging spectroscopy when quantitative assessment of forest stands are in the focus, such as estimation of forest biomass, biochemical properties (e.g. chlorophyll /water content), etc. The parameterization of radiative transfer (RT) models used in latter case requires three-dimensional spatial distribution of green foliage and woody biomass. Airborne LiDAR data acquired over forest sites bears these kinds of 3D information. The main objective of the study was to compare the results from several approaches to interpolation of digital elevation model (DEM) and digital surface model (DSM). We worked with airborne LiDAR data with different density (TopEye Mk II 1,064nm instrument, 1-5 points/m2) acquired over the Norway spruce forests situated in the Beskydy Mountains, the Czech Republic. Three different interpolation algorithms with increasing complexity were tested: i/Nearest neighbour approach implemented in the BCAL software package (Idaho Univ.); ii/Averaging and linear interpolation techniques used in the OPALS software (Vienna Univ. of Technology); iii/Active contour technique implemented in the TreeVis software (Univ. of Freiburg). We defined two spatial resolutions for the resulting coupled raster DEMs and DSMs outputs: 0.4 m and 1 m, calculated by each algorithm. The grids correspond to the same spatial resolutions of hyperspectral imagery data for which the DEMs were used in a/geometrical correction and b/building a complex tree models for radiative transfer modelling. We applied two types of analyses when comparing between results from the different interpolations/raster resolution: 1/calculated DEM or DSM between themselves; 2/comparison with field data: DEM with measurements from referential GPS, DSM - field tree alometric measurements, where tree height was calculated as DSM-DEM. The results of the analyses show that: 1/averaging techniques tend to underestimate the tree height and the generated surface does not follow the first LiDAR echoes both for 1 m and 0.4 m pixel size; 2/we did not find any significant difference between tree heights calculated by nearest neighbour algorithm and the active contour technique for 1 m pixel output but the difference increased with finer resolution (0.4 m); 3/the accuracy of the DEMs calculated by tested algorithms is similar.

The natural neighbor series manuals and source codes

NASA Astrophysics Data System (ADS)

Watson, Dave

1999-05-01

This software series is concerned with reconstruction of spatial functions by interpolating a set of discrete observations having two or three independent variables. There are three components in this series: (1) nngridr: an implementation of natural neighbor interpolation, 1994, (2) modemap: an implementation of natural neighbor interpolation on the sphere, 1998 and (3) orebody: an implementation of natural neighbor isosurface generation (publication incomplete). Interpolation is important to geologists because it can offer graphical insights into significant geological structure and behavior, which, although inherent in the data, may not be otherwise apparent. It also is the first step in numerical integration, which provides a primary avenue to detailed quantification of the observed spatial function. Interpolation is implemented by selecting a surface-generating rule that controls the form of a `bridge' built across the interstices between adjacent observations. The cataloging and classification of the many such rules that have been reported is a subject in itself ( Watson, 1992), and the merits of various approaches have been debated at length. However, for practical purposes, interpolation methods are usually judged on how satisfactorily they handle problematic data sets. Sparse scattered data or traverse data, especially if the functional values are highly variable, generally tests interpolation methods most severely; but one method, natural neighbor interpolation, usually does produce preferable results for such data.

Spatial analysis of soil organic carbon in Zhifanggou catchment of the Loess Plateau.

PubMed

Li, Mingming; Zhang, Xingchang; Zhen, Qing; Han, Fengpeng

2013-01-01

Soil organic carbon (SOC) reflects soil quality and plays a critical role in soil protection, food safety, and global climate changes. This study involved grid sampling at different depths (6 layers) between 0 and 100 cm in a catchment. A total of 1282 soil samples were collected from 215 plots over 8.27 km(2). A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability and soil carbon content patterns. The mean SOC content in the 1282 samples from the study field was 3.08 g · kg(-1). The SOC content of each layer decreased with increasing soil depth by a power function relationship. The SOC content of each layer was moderately variable and followed a lognormal distribution. The semi-variograms of the SOC contents of the six different layers were fit with the following models: exponential, spherical, exponential, Gaussian, exponential, and exponential, respectively. A moderate spatial dependence was observed in the 0-10 and 10-20 cm layers, which resulted from stochastic and structural factors. The spatial distribution of SOC content in the four layers between 20 and 100 cm exhibit were mainly restricted by structural factors. Correlations within each layer were observed between 234 and 562 m. A classical Kriging interpolation was used to directly visualize the spatial distribution of SOC in the catchment. The variability in spatial distribution was related to topography, land use type, and human activity. Finally, the vertical distribution of SOC decreased. Our results suggest that the ordinary Kriging interpolation can directly reveal the spatial distribution of SOC and the sample distance about this study is sufficient for interpolation or plotting. More research is needed, however, to clarify the spatial variability on the bigger scale and better understand the factors controlling spatial variability of soil carbon in the Loess Plateau region.

SU-F-T-315: Comparative Studies of Planar Dose with Different Spatial Resolution for Head and Neck IMRT QA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hwang, T; Koo, T

Purpose: To quantitatively investigate the planar dose difference and the γ value between the reference fluence map with the 1 mm detector-to-detector distance and the other fluence maps with less spatial resolution for head and neck intensity modulated radiation (IMRT) therapy. Methods: For ten head and neck cancer patients, the IMRT quality assurance (QA) beams were generated using by the commercial radiation treatment planning system, Pinnacle3 (ver. 8.0.d Philips Medical System, Madison, WI). For each beam, ten fluence maps (detector-to-detector distance: 1 mm to 10 mm by 1 mm) were generated. The fluence maps with larger than 1 mm detector-todetectormore » distance were interpolated using MATLAB (R2014a, the Math Works,Natick, MA) by four different interpolation Methods: for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. These interpolated fluence maps were compared with the reference one using the γ value (criteria: 3%, 3 mm) and the relative dose difference. Results: As the detector-to-detector distance increases, the dose difference between the two maps increases. For the fluence map with the same resolution, the cubic spline interpolation and the bicubic interpolation are almost equally best interpolation methods while the nearest neighbor interpolation is the worst.For example, for 5 mm distance fluence maps, γ≤1 are 98.12±2.28%, 99.48±0.66%, 99.45±0.65% and 82.23±0.48% for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. For 7 mm distance fluence maps, γ≤1 are 90.87±5.91%, 90.22±6.95%, 91.79±5.97% and 71.93±4.92 for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. Conclusion: We recommend that the 2-dimensional detector array with high spatial resolution should be used as an IMRT QA tool and that the measured fluence maps should be interpolated using by the cubic spline interpolation or the bicubic interpolation for head and neck IMRT delivery. This work was supported by Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)« less

Isoscapes of tree-ring carbon-13 perform like meteorological networks in predicting regional precipitation patterns

NASA Astrophysics Data System (ADS)

del Castillo, Jorge; Aguilera, Mònica; Voltas, Jordi; Ferrio, Juan Pedro

2013-03-01

isotopes in tree rings provide climatic information with annual resolution dating back for centuries or even millennia. However, deriving spatially explicit climate models from isotope networks remains challenging. Here we propose a methodology to model regional precipitation from carbon isotope discrimination (Δ13C) in tree rings by (1) building regional spatial models of Δ13C (isoscapes) and (2) deriving precipitation maps from Δ13C-isoscapes, taking advantage of the response of Δ13C to precipitation in seasonally dry climates. As a case study, we modeled the spatial distribution of mean annual precipitation (MAP) in the northeastern Iberian Peninsula, a region with complex topography and climate (MAP = 303-1086 mm). We compiled wood Δ13C data for two Mediterranean species that exhibit complementary responses to seasonal precipitation (Pinus halepensis Mill., N = 38; Quercus ilex L.; N = 44; pooling period: 1975-2008). By combining multiple regression and geostatistical interpolation, we generated one Δ13 C-isoscape for each species. A spatial model of MAP was then built as the sum of two complementary maps of seasonal precipitation, each one derived from the corresponding Δ13C-isoscape (September-November from Q. ilex; December-August from P. halepensis). Our approach showed a predictive power for MAP (RMSE = 84 mm) nearly identical to that obtained by interpolating data directly from a similarly dense network of meteorological stations (RMSE = 80-83 mm, N = 65), being only outperformed when using a much denser meteorological network (RMSE = 56-57 mm, N = 340). This method offers new avenues for modeling spatial variability of past precipitation, exploiting the large amount of information currently available from tree-ring networks.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015.

PubMed

Abatzoglou, John T; Dobrowski, Solomon Z; Parks, Sean A; Hegewisch, Katherine C

2018-01-09

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

NASA Astrophysics Data System (ADS)

Abatzoglou, John T.; Dobrowski, Solomon Z.; Parks, Sean A.; Hegewisch, Katherine C.

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

Improving the spatial and temporal resolution with quantification of uncertainty and errors in earth observation data sets using Data Interpolating Empirical Orthogonal Functions methodology

NASA Astrophysics Data System (ADS)

El Serafy, Ghada; Gaytan Aguilar, Sandra; Ziemba, Alexander

2016-04-01

There is an increasing use of process-based models in the investigation of ecological systems and scenario predictions. The accuracy and quality of these models are improved when run with high spatial and temporal resolution data sets. However, ecological data can often be difficult to collect which manifests itself through irregularities in the spatial and temporal domain of these data sets. Through the use of Data INterpolating Empirical Orthogonal Functions(DINEOF) methodology, earth observation products can be improved to have full spatial coverage within the desired domain as well as increased temporal resolution to daily and weekly time step, those frequently required by process-based models[1]. The DINEOF methodology results in a degree of error being affixed to the refined data product. In order to determine the degree of error introduced through this process, the suspended particulate matter and chlorophyll-a data from MERIS is used with DINEOF to produce high resolution products for the Wadden Sea. These new data sets are then compared with in-situ and other data sources to determine the error. Also, artificial cloud cover scenarios are conducted in order to substantiate the findings from MERIS data experiments. Secondly, the accuracy of DINEOF is explored to evaluate the variance of the methodology. The degree of accuracy is combined with the overall error produced by the methodology and reported in an assessment of the quality of DINEOF when applied to resolution refinement of chlorophyll-a and suspended particulate matter in the Wadden Sea. References [1] Sirjacobs, D.; Alvera-Azcárate, A.; Barth, A.; Lacroix, G.; Park, Y.; Nechad, B.; Ruddick, K.G.; Beckers, J.-M. (2011). Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology. J. Sea Res. 65(1): 114-130. Dx.doi.org/10.1016/j.seares.2010.08.002

Estimating Small-area Populations by Age and Sex Using Spatial Interpolation and Statistical Inference Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qai, Qiang; Rushton, Gerald; Bhaduri, Budhendra L

The objective of this research is to compute population estimates by age and sex for small areas whose boundaries are different from those for which the population counts were made. In our approach, population surfaces and age-sex proportion surfaces are separately estimated. Age-sex population estimates for small areas and their confidence intervals are then computed using a binomial model with the two surfaces as inputs. The approach was implemented for Iowa using a 90 m resolution population grid (LandScan USA) and U.S. Census 2000 population. Three spatial interpolation methods, the areal weighting (AW) method, the ordinary kriging (OK) method, andmore » a modification of the pycnophylactic method, were used on Census Tract populations to estimate the age-sex proportion surfaces. To verify the model, age-sex population estimates were computed for paired Block Groups that straddled Census Tracts and therefore were spatially misaligned with them. The pycnophylactic method and the OK method were more accurate than the AW method. The approach is general and can be used to estimate subgroup-count types of variables from information in existing administrative areas for custom-defined areas used as the spatial basis of support in other applications.« less

Spatial Interpolation of Reference Evapotranspiration in India: Comparison of IDW and Kriging Methods

NASA Astrophysics Data System (ADS)

Hodam, Sanayanbi; Sarkar, Sajal; Marak, Areor G. R.; Bandyopadhyay, A.; Bhadra, A.

2017-12-01

In the present study, to understand the spatial distribution characteristics of the ETo over India, spatial interpolation was performed on the means of 32 years (1971-2002) monthly data of 131 India Meteorological Department stations uniformly distributed over the country by two methods, namely, inverse distance weighted (IDW) interpolation and kriging. Kriging was found to be better while developing the monthly surfaces during cross-validation. However, in station-wise validation, IDW performed better than kriging in almost all the cases, hence is recommended for spatial interpolation of ETo and its governing meteorological parameters. This study also checked if direct kriging of FAO-56 Penman-Monteith (PM) (Allen et al. in Crop evapotranspiration—guidelines for computing crop water requirements, Irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations (FAO), Rome, 1998) point ETo produced comparable results against ETo estimated with individually kriged weather parameters (indirect kriging). Indirect kriging performed marginally well compared to direct kriging. Point ETo values were extended to areal ETo values by IDW and FAO-56 PM mean ETo maps for India were developed to obtain sufficiently accurate ETo estimates at unknown locations.

Context dependent anti-aliasing image reconstruction

NASA Technical Reports Server (NTRS)

Beaudet, Paul R.; Hunt, A.; Arlia, N.

1989-01-01

Image Reconstruction has been mostly confined to context free linear processes; the traditional continuum interpretation of digital array data uses a linear interpolator with or without an enhancement filter. Here, anti-aliasing context dependent interpretation techniques are investigated for image reconstruction. Pattern classification is applied to each neighborhood to assign it a context class; a different interpolation/filter is applied to neighborhoods of differing context. It is shown how the context dependent interpolation is computed through ensemble average statistics using high resolution training imagery from which the lower resolution image array data is obtained (simulation). A quadratic least squares (LS) context-free image quality model is described from which the context dependent interpolation coefficients are derived. It is shown how ensembles of high-resolution images can be used to capture the a priori special character of different context classes. As a consequence, a priori information such as the translational invariance of edges along the edge direction, edge discontinuity, and the character of corners is captured and can be used to interpret image array data with greater spatial resolution than would be expected by the Nyquist limit. A Gibb-like artifact associated with this super-resolution is discussed. More realistic context dependent image quality models are needed and a suggestion is made for using a quality model which now is finding application in data compression.

Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation

USGS Publications Warehouse

Alvarez, Otto; Guo, Qinghua; Klinger, Robert C.; Li, Wenkai; Doherty, Paul

2013-01-01

Climate models may be limited in their inferential use if they cannot be locally validated or do not account for spatial uncertainty. Much of the focus has gone into determining which interpolation method is best suited for creating gridded climate surfaces, which often a covariate such as elevation (Digital Elevation Model, DEM) is used to improve the interpolation accuracy. One key area where little research has addressed is in determining which covariate best improves the accuracy in the interpolation. In this study, a comprehensive evaluation was carried out in determining which covariates were most suitable for interpolating climatic variables (e.g. precipitation, mean temperature, minimum temperature, and maximum temperature). We compiled data for each climate variable from 1950 to 1999 from approximately 500 weather stations across the Western United States (32° to 49° latitude and −124.7° to −112.9° longitude). In addition, we examined the uncertainty of the interpolated climate surface. Specifically, Thin Plate Spline (TPS) was used as the interpolation method since it is one of the most popular interpolation techniques to generate climate surfaces. We considered several covariates, including DEM, slope, distance to coast (Euclidean distance), aspect, solar potential, radar, and two Normalized Difference Vegetation Index (NDVI) products derived from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). A tenfold cross-validation was applied to determine the uncertainty of the interpolation based on each covariate. In general, the leading covariate for precipitation was radar, while DEM was the leading covariate for maximum, mean, and minimum temperatures. A comparison to other products such as PRISM and WorldClim showed strong agreement across large geographic areas but climate surfaces generated in this study (ClimSurf) had greater variability at high elevation regions, such as in the Sierra Nevada Mountains.

Validation of China-wide interpolated daily climate variables from 1960 to 2011

NASA Astrophysics Data System (ADS)

Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang

2015-02-01

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.

Image interpolation allows accurate quantitative bone morphometry in registered micro-computed tomography scans.

PubMed

Schulte, Friederike A; Lambers, Floor M; Mueller, Thomas L; Stauber, Martin; Müller, Ralph

2014-04-01

Time-lapsed in vivo micro-computed tomography is a powerful tool to analyse longitudinal changes in the bone micro-architecture. Registration can overcome problems associated with spatial misalignment between scans; however, it requires image interpolation which might affect the outcome of a subsequent bone morphometric analysis. The impact of the interpolation error itself, though, has not been quantified to date. Therefore, the purpose of this ex vivo study was to elaborate the effect of different interpolator schemes [nearest neighbour, tri-linear and B-spline (BSP)] on bone morphometric indices. None of the interpolator schemes led to significant differences between interpolated and non-interpolated images, with the lowest interpolation error found for BSPs (1.4%). Furthermore, depending on the interpolator, the processing order of registration, Gaussian filtration and binarisation played a role. Independent from the interpolator, the present findings suggest that the evaluation of bone morphometry should be done with images registered using greyscale information.

Spatial interpolation of GPS PWV and meteorological variables over the west coast of Peninsular Malaysia during 2013 Klang Valley Flash Flood

NASA Astrophysics Data System (ADS)

Suparta, Wayan; Rahman, Rosnani

2016-02-01

Global Positioning System (GPS) receivers are widely installed throughout the Peninsular Malaysia, but the implementation for monitoring weather hazard system such as flash flood is still not optimal. To increase the benefit for meteorological applications, the GPS system should be installed in collocation with meteorological sensors so the precipitable water vapor (PWV) can be measured. The distribution of PWV is a key element to the Earth's climate for quantitative precipitation improvement as well as flash flood forecasts. The accuracy of this parameter depends on a large extent on the number of GPS receiver installations and meteorological sensors in the targeted area. Due to cost constraints, a spatial interpolation method is proposed to address these issues. In this paper, we investigated spatial distribution of GPS PWV and meteorological variables (surface temperature, relative humidity, and rainfall) by using thin plate spline (tps) and ordinary kriging (Krig) interpolation techniques over the Klang Valley in Peninsular Malaysia (longitude: 99.5°-102.5°E and latitude: 2.0°-6.5°N). Three flash flood cases in September, October, and December 2013 were studied. The analysis was performed using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) to determine the accuracy and reliability of the interpolation techniques. Results at different phases (pre, onset, and post) that were evaluated showed that tps interpolation technique is more accurate, reliable, and highly correlated in estimating GPS PWV and relative humidity, whereas Krig is more reliable for predicting temperature and rainfall during pre-flash flood events. During the onset of flash flood events, both methods showed good interpolation in estimating all meteorological parameters with high accuracy and reliability. The finding suggests that the proposed method of spatial interpolation techniques are capable of handling limited data sources with high accuracy, which in turn can be used to predict future floods.

Projection correlation based view interpolation for cone beam CT: primary fluence restoration in scatter measurement with a moving beam stop array.

PubMed

Yan, Hao; Mou, Xuanqin; Tang, Shaojie; Xu, Qiong; Zankl, Maria

2010-11-07

Scatter correction is an open problem in x-ray cone beam (CB) CT. The measurement of scatter intensity with a moving beam stop array (BSA) is a promising technique that offers a low patient dose and accurate scatter measurement. However, when restoring the blocked primary fluence behind the BSA, spatial interpolation cannot well restore the high-frequency part, causing streaks in the reconstructed image. To address this problem, we deduce a projection correlation (PC) to utilize the redundancy (over-determined information) in neighbouring CB views. PC indicates that the main high-frequency information is contained in neighbouring angular projections, instead of the current projection itself, which provides a guiding principle that applies to high-frequency information restoration. On this basis, we present the projection correlation based view interpolation (PC-VI) algorithm; that it outperforms the use of only spatial interpolation is validated. The PC-VI based moving BSA method is developed. In this method, PC-VI is employed instead of spatial interpolation, and new moving modes are designed, which greatly improve the performance of the moving BSA method in terms of reliability and practicability. Evaluation is made on a high-resolution voxel-based human phantom realistically including the entire procedure of scatter measurement with a moving BSA, which is simulated by analytical ray-tracing plus Monte Carlo simulation with EGSnrc. With the proposed method, we get visually artefact-free images approaching the ideal correction. Compared with the spatial interpolation based method, the relative mean square error is reduced by a factor of 6.05-15.94 for different slices. PC-VI does well in CB redundancy mining; therefore, it has further potential in CBCT studies.

Stream Kriging: Incremental and recursive ordinary Kriging over spatiotemporal data streams

NASA Astrophysics Data System (ADS)

Zhong, Xu; Kealy, Allison; Duckham, Matt

2016-05-01

Ordinary Kriging is widely used for geospatial interpolation and estimation. Due to the O (n3) time complexity of solving the system of linear equations, ordinary Kriging for a large set of source points is computationally intensive. Conducting real-time Kriging interpolation over continuously varying spatiotemporal data streams can therefore be especially challenging. This paper develops and tests two new strategies for improving the performance of an ordinary Kriging interpolator adapted to a stream-processing environment. These strategies rely on the expectation that, over time, source data points will frequently refer to the same spatial locations (for example, where static sensor nodes are generating repeated observations of a dynamic field). First, an incremental strategy improves efficiency in cases where a relatively small proportion of previously processed spatial locations are absent from the source points at any given iteration. Second, a recursive strategy improves efficiency in cases where there is substantial set overlap between the sets of spatial locations of source points at the current and previous iterations. These two strategies are evaluated in terms of their computational efficiency in comparison to ordinary Kriging algorithm. The results show that these two strategies can reduce the time taken to perform the interpolation by up to 90%, and approach average-case time complexity of O (n2) when most but not all source points refer to the same locations over time. By combining the approaches developed in this paper with existing heuristic ordinary Kriging algorithms, the conclusions indicate how further efficiency gains could potentially be accrued. The work ultimately contributes to the development of online ordinary Kriging interpolation algorithms, capable of real-time spatial interpolation with large streaming data sets.

A comparison of spatial analysis methods for the construction of topographic maps of retinal cell density.

PubMed

Garza-Gisholt, Eduardo; Hemmi, Jan M; Hart, Nathan S; Collin, Shaun P

2014-01-01

Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed 'by eye'. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation 'respects' the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the 'noise' caused by artefacts and permits a clearer representation of the dominant, 'real' distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome.

An algorithm to generate input data from meteorological and space shuttle observations to validate a CH4-CO model

NASA Technical Reports Server (NTRS)

Peters, L. K.; Yamanis, J.

1981-01-01

Objective procedures to analyze data from meteorological and space shuttle observations to validate a three dimensional model were investigated. The transport and chemistry of carbon monoxide and methane in the troposphere were studied. Four aspects were examined: (1) detailed evaluation of the variational calculus procedure, with the equation of continuity as a strong constraint, for adjustment of global tropospheric wind fields; (2) reduction of the National Meteorological Center (NMC) data tapes for data input to the OSTA-1/MAPS Experiment; (3) interpolation of the NMC Data for input to the CH4-CO model; and (4) temporal and spatial interpolation procedures of the CO measurements from the OSTA-1/MAPS Experiment to generate usable contours of the data.

Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing.

PubMed

Qiao, Pengwei; Lei, Mei; Yang, Sucai; Yang, Jun; Guo, Guanghui; Zhou, Xiaoyong

2018-06-01

Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.

[Downscaling research of spatial distribution of incidence of hand foot and mouth disease based on area-to-area Poisson Kriging method].

PubMed

Wang, J X; Hu, M G; Yu, S C; Xiao, G X

2017-09-10

Objective: To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources. Methods: The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected. Downscaling interpolation was conducted by using area-to-area Poisson Kriging method. The interpolation results were visualized by using geographic information system (GIS). The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township. Results: In the downscaling interpolation, the range of the fitting semi-variance equation was 20.38 km. Within the range, the incidence had correlation with each other. The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y =1.053 1 x , R (2)=0.99. The incidences at different scale were consistent. Conclusions: The incidence of HFMD had spatial autocorrelation within 20.38 km. When HFMD occurs in one place, it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km. Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.

Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

USGS Publications Warehouse

Archfield, Stacey A.; Pugliese, Alessio; Castellarin, Attilio; Skøien, Jon O.; Kiang, Julie E.

2013-01-01

In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

Comparing interpolation techniques for annual temperature mapping across Xinjiang region

NASA Astrophysics Data System (ADS)

Ren-ping, Zhang; Jing, Guo; Tian-gang, Liang; Qi-sheng, Feng; Aimaiti, Yusupujiang

2016-11-01

Interpolating climatic variables such as temperature is challenging due to the highly variable nature of meteorological processes and the difficulty in establishing a representative network of stations. In this paper, based on the monthly temperature data which obtained from the 154 official meteorological stations in the Xinjiang region and surrounding areas, we compared five spatial interpolation techniques: Inverse distance weighting (IDW), Ordinary kriging, Cokriging, thin-plate smoothing splines (ANUSPLIN) and Empirical Bayesian kriging(EBK). Error metrics were used to validate interpolations against independent data. Results indicated that, the ANUSPLIN performed best than the other four interpolation methods.

The Variable Grid Method, an Approach for the Simultaneous Visualization and Assessment of Spatial Trends and Uncertainty

NASA Astrophysics Data System (ADS)

Rose, K.; Glosser, D.; Bauer, J. R.; Barkhurst, A.

2015-12-01

The products of spatial analyses that leverage the interpolation of sparse, point data to represent continuous phenomena are often presented without clear explanations of the uncertainty associated with the interpolated values. As a result, there is frequently insufficient information provided to effectively support advanced computational analyses and individual research and policy decisions utilizing these results. This highlights the need for a reliable approach capable of quantitatively producing and communicating spatial data analyses and their inherent uncertainties for a broad range of uses. To address this need, we have developed the Variable Grid Method (VGM), and associated Python tool, which is a flexible approach that can be applied to a variety of analyses and use case scenarios where users need a method to effectively study, evaluate, and analyze spatial trends and patterns while communicating the uncertainty in the underlying spatial datasets. The VGM outputs a simultaneous visualization representative of the spatial data analyses and quantification of underlying uncertainties, which can be calculated using data related to sample density, sample variance, interpolation error, uncertainty calculated from multiple simulations, etc. We will present examples of our research utilizing the VGM to quantify key spatial trends and patterns for subsurface data interpolations and their uncertainties and leverage these results to evaluate storage estimates and potential impacts associated with underground injection for CO2 storage and unconventional resource production and development. The insights provided by these examples identify how the VGM can provide critical information about the relationship between uncertainty and spatial data that is necessary to better support their use in advance computation analyses and informing research, management and policy decisions.

Performance of Statistical Temporal Downscaling Techniques of Wind Speed Data Over Aegean Sea

NASA Astrophysics Data System (ADS)

Gokhan Guler, Hasan; Baykal, Cuneyt; Ozyurt, Gulizar; Kisacik, Dogan

2016-04-01

Wind speed data is a key input for many meteorological and engineering applications. Many institutions provide wind speed data with temporal resolutions ranging from one hour to twenty four hours. Higher temporal resolution is generally required for some applications such as reliable wave hindcasting studies. One solution to generate wind data at high sampling frequencies is to use statistical downscaling techniques to interpolate values of the finer sampling intervals from the available data. In this study, the major aim is to assess temporal downscaling performance of nine statistical interpolation techniques by quantifying the inherent uncertainty due to selection of different techniques. For this purpose, hourly 10-m wind speed data taken from 227 data points over Aegean Sea between 1979 and 2010 having a spatial resolution of approximately 0.3 degrees are analyzed from the National Centers for Environmental Prediction (NCEP) The Climate Forecast System Reanalysis database. Additionally, hourly 10-m wind speed data of two in-situ measurement stations between June, 2014 and June, 2015 are considered to understand effect of dataset properties on the uncertainty generated by interpolation technique. In this study, nine statistical interpolation techniques are selected as w0 (left constant) interpolation, w6 (right constant) interpolation, averaging step function interpolation, linear interpolation, 1D Fast Fourier Transform interpolation, 2nd and 3rd degree Lagrange polynomial interpolation, cubic spline interpolation, piecewise cubic Hermite interpolating polynomials. Original data is down sampled to 6 hours (i.e. wind speeds at 0th, 6th, 12th and 18th hours of each day are selected), then 6 hourly data is temporally downscaled to hourly data (i.e. the wind speeds at each hour between the intervals are computed) using nine interpolation technique, and finally original data is compared with the temporally downscaled data. A penalty point system based on coefficient of variation root mean square error, normalized mean absolute error, and prediction skill is selected to rank nine interpolation techniques according to their performance. Thus, error originated from the temporal downscaling technique is quantified which is an important output to determine wind and wave modelling uncertainties, and the performance of these techniques are demonstrated over Aegean Sea indicating spatial trends and discussing relevance to data type (i.e. reanalysis data or in-situ measurements). Furthermore, bias introduced by the best temporal downscaling technique is discussed. Preliminary results show that overall piecewise cubic Hermite interpolating polynomials have the highest performance to temporally downscale wind speed data for both reanalysis data and in-situ measurements over Aegean Sea. However, it is observed that cubic spline interpolation performs much better along Aegean coastline where the data points are close to the land. Acknowledgement: This research was partly supported by TUBITAK Grant number 213M534 according to Turkish Russian Joint research grant with RFBR and the CoCoNET (Towards Coast to Coast Network of Marine Protected Areas Coupled by Wİnd Energy Potential) project funded by European Union FP7/2007-2013 program.

Ground and satellite based assessment of meteorological droughts: The Coello river basin case study

NASA Astrophysics Data System (ADS)

Cruz-Roa, A. F.; Olaya-Marín, E. J.; Barrios, M. I.

2017-10-01

The spatial distribution of droughts is a key factor for designing water management policies at basin scale in arid and semi-arid regions. Ground hydro-meteorological data in neo-tropical areas are scarce; therefore, the merging of ground and satellite datasets is a promissory approach for improving our understanding of water distribution. This paper compares three monthly rainfall interpolation methods for drought evaluation. The ordinary kriging technique based on ground data, and cokriging with elevation as auxiliary variable were compared against cokriging using the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA). Twenty rain gauge stations and the 3B42V7 version of the TMPA research dataset were considered. Comparisons were made over the Coello river basin (Colombia) at 3″ spatial resolution covering a period of eight years (1998-2005). The best spatial rainfall estimation was found for cokriging using ground data and elevation. The spatial support of TMPA dataset is very coarse for a merged interpolation with ground data, this spatial scales discrepancy highlight the need to consider scaling rules in the interpolation process.

Ambient Ozone Exposure in Czech Forests: A GIS-Based Approach to Spatial Distribution Assessment

PubMed Central

Hůnová, I.; Horálek, J.; Schreiberová, M.; Zapletal, M.

2012-01-01

Ambient ozone (O3) is an important phytotoxic pollutant, and detailed knowledge of its spatial distribution is becoming increasingly important. The aim of the paper is to compare different spatial interpolation techniques and to recommend the best approach for producing a reliable map for O3 with respect to its phytotoxic potential. For evaluation we used real-time ambient O3 concentrations measured by UV absorbance from 24 Czech rural sites in the 2007 and 2008 vegetation seasons. We considered eleven approaches for spatial interpolation used for the development of maps for mean vegetation season O3 concentrations and the AOT40F exposure index for forests. The uncertainty of maps was assessed by cross-validation analysis. The root mean square error (RMSE) of the map was used as a criterion. Our results indicate that the optimal interpolation approach is linear regression of O3 data and altitude with subsequent interpolation of its residuals by ordinary kriging. The relative uncertainty of the map of O3 mean for the vegetation season is less than 10%, using the optimal method as for both explored years, and this is a very acceptable value. In the case of AOT40F, however, the relative uncertainty of the map is notably worse, reaching nearly 20% in both examined years. PMID:22566757

Mapping wildfire effects on Ca2+ and Mg2+ released from ash. A microplot analisis.

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Úbeda, Xavier; Martin, Deborah

2010-05-01

Wildland fires have important implications in ecosystems dynamic. Their effects depends on many biophysical components, mainly burned specie, ecosystem affected, amount and spatial distribution of the fuel, relative humidity, slope, aspect and time of residence. These parameters are heterogenic across the landscape, producing a complex mosaic of severities. Wildland fires have a heterogenic impact on ecosystems due their diverse biophysical features. It is widely known that fire impacts can change rapidly even in short distances, producing at microplot scale highly spatial variation. Also after a fire, the most visible thing is ash and his physical and chemical properties are of main importance because here reside the majority of the available nutrients available to the plants. Considering this idea, is of major importance, study their characteristics in order to observe the type and amount of elements available to plants. This study is focused on the study of the spatial variability of two nutrients essential to plant growth, Ca2+ and Mg2+, released from ash after a wildfire at microplot scale. The impacts of fire are highly variable even small distances. This creates many problems at the hour of map the effects of fire in the release of the studied elements. Hence is of major priority identify the less biased interpolation method in order to predict with great accuracy the variable in study. The aim of this study is map the effects of wildfire on the referred elements released from ash at microplot scale, testing several interpolation methods. 16 interpolation techniques were tested, Inverse Distance to a Weight (IDW), with the with the weights of 1,2, 3, 4 and 5, Local Polynomial, with the power of 1 (LP1) and 2 (LP2), Polynomial Regression (PR), Radial Basis Functions, especially, Spline With Tension (SPT), Completely Regularized Spline (CRS), Multiquadratic (MTQ), Inverse Multiquadratic (MTQ), and Thin Plate Spline (TPS). Also geostatistical methods were tested from Kriging family, mainly Ordinary Kriging (OK), Simple Kriging (SK) and Universal Kriging (UK). Interpolation techniques were assessed throughout the Mean Error (ME) and Root Mean Square (RMSE), obtained from the cross validation procedure calculated in all methods. The fire occurred in Portugal, near an urban area and inside the affected area we designed a grid with the dimensions of 9 x 27 m and we collected 40 samples. Before modelling data, we tested their normality with the Shapiro Wilk test. Since the distributions of Ca2+ and Mg2+ did not respect the gaussian distribution we transformed data logarithmically (Ln). With this transformation, data respect the normality and spatial distribution was modelled with the transformed data. On average in the entire plot the ash slurries contained 4371.01 mg/l of Ca2+, however with a higher coefficient of variation (CV%) of 54.05%. From all the tested methods LP1 was the less biased and hence the most accurate to interpolate this element. The most biased was LP2. In relation to Mg2+, considering the entire plot, the ash released in solution on average 1196.01 mg/l, with a CV% of 52.36%, similar to the identified in Ca2+. The best interpolator in this case was SK and the most biased was LP1 and TPS. Comparing all methods in both elements, the quality of the interpolations was higher in Ca2+. These results allowed us to conclude that to achieve the best prediction it is necessary test a wide range of interpolation methods. The best accuracy will permit us to understand with more precision where the studied elements are more available and accessible to plant growth and ecosystem recovers. This spatial pattern of both nutrients is related with ash pH and burned severity evaluated from ash colour and CaCO3 content. These aspects will be also discussed in the work.

Mapping Atmospheric Moisture Climatologies across the Conterminous United States

PubMed Central

Daly, Christopher; Smith, Joseph I.; Olson, Keith V.

2015-01-01

Spatial climate datasets of 1981–2010 long-term mean monthly average dew point and minimum and maximum vapor pressure deficit were developed for the conterminous United States at 30-arcsec (~800m) resolution. Interpolation of long-term averages (twelve monthly values per variable) was performed using PRISM (Parameter-elevation Relationships on Independent Slopes Model). Surface stations available for analysis numbered only 4,000 for dew point and 3,500 for vapor pressure deficit, compared to 16,000 for previously-developed grids of 1981–2010 long-term mean monthly minimum and maximum temperature. Therefore, a form of Climatologically-Aided Interpolation (CAI) was used, in which the 1981–2010 temperature grids were used as predictor grids. For each grid cell, PRISM calculated a local regression function between the interpolated climate variable and the predictor grid. Nearby stations entering the regression were assigned weights based on the physiographic similarity of the station to the grid cell that included the effects of distance, elevation, coastal proximity, vertical atmospheric layer, and topographic position. Interpolation uncertainties were estimated using cross-validation exercises. Given that CAI interpolation was used, a new method was developed to allow uncertainties in predictor grids to be accounted for in estimating the total interpolation error. Local land use/land cover properties had noticeable effects on the spatial patterns of atmospheric moisture content and deficit. An example of this was relatively high dew points and low vapor pressure deficits at stations located in or near irrigated fields. The new grids, in combination with existing temperature grids, enable the user to derive a full suite of atmospheric moisture variables, such as minimum and maximum relative humidity, vapor pressure, and dew point depression, with accompanying assumptions. All of these grids are available online at http://prism.oregonstate.edu, and include 800-m and 4-km resolution data, images, metadata, pedigree information, and station inventory files. PMID:26485026

A Residual Kriging method for the reconstruction of 3D high-resolution meteorological fields from airborne and surface observations

NASA Astrophysics Data System (ADS)

Laiti, Lavinia; Zardi, Dino; de Franceschi, Massimiliano; Rampanelli, Gabriele

2013-04-01

Manned light aircrafts and remotely piloted aircrafts represent very valuable and flexible measurement platforms for atmospheric research, as they are able to provide high temporal and spatial resolution observations of the atmosphere above the ground surface. In the present study the application of a geostatistical interpolation technique called Residual Kriging (RK) is proposed for the mapping of airborne measurements of scalar quantities over regularly spaced 3D grids. In RK the dominant (vertical) trend component underlying the original data is first extracted to filter out local anomalies, then the residual field is separately interpolated and finally added back to the trend; the determination of the interpolation weights relies on the estimate of the characteristic covariance function of the residuals, through the computation and modelling of their semivariogram function. RK implementation also allows for the inference of the characteristic spatial scales of variability of the target field and its isotropization, and for an estimate of the interpolation error. The adopted test-bed database consists in a series of flights of an instrumented motorglider exploring the atmosphere of two valleys near the city of Trento (in the southeastern Italian Alps), performed on fair-weather summer days. RK method is used to reconstruct fully 3D high-resolution fields of potential temperature and mixing ratio for specific vertical slices of the valley atmosphere, integrating also ground-based measurements from the nearest surface weather stations. From RK-interpolated meteorological fields, fine-scale features of the atmospheric boundary layer developing over the complex valley topography in connection with the occurrence of thermally-driven slope and valley winds, are detected. The performance of RK mapping is also tested against two other commonly adopted interpolation methods, i.e. the Inverse Distance Weighting and the Delaunay triangulation methods, comparing the results of a cross-validation procedure.

A semiparametric spatio-temporal model for solar irradiance data

DOE PAGES

Patrick, Joshua D.; Harvill, Jane L.; Hansen, Clifford W.

2016-03-01

Here, we evaluate semiparametric spatio-temporal models for global horizontal irradiance at high spatial and temporal resolution. These models represent the spatial domain as a lattice and are capable of predicting irradiance at lattice points, given data measured at other lattice points. Using data from a 1.2 MW PV plant located in Lanai, Hawaii, we show that a semiparametric model can be more accurate than simple interpolation between sensor locations. We investigate spatio-temporal models with separable and nonseparable covariance structures and find no evidence to support assuming a separable covariance structure. These results indicate a promising approach for modeling irradiance atmore » high spatial resolution consistent with available ground-based measurements. Moreover, this kind of modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems.« less

Spatiotemporal Interpolation Methods for Solar Event Trajectories

NASA Astrophysics Data System (ADS)

Filali Boubrahimi, Soukaina; Aydin, Berkay; Schuh, Michael A.; Kempton, Dustin; Angryk, Rafal A.; Ma, Ruizhe

2018-05-01

This paper introduces four spatiotemporal interpolation methods that enrich complex, evolving region trajectories that are reported from a variety of ground-based and space-based solar observatories every day. Our interpolation module takes an existing solar event trajectory as its input and generates an enriched trajectory with any number of additional time–geometry pairs created by the most appropriate method. To this end, we designed four different interpolation techniques: MBR-Interpolation (Minimum Bounding Rectangle Interpolation), CP-Interpolation (Complex Polygon Interpolation), FI-Interpolation (Filament Polygon Interpolation), and Areal-Interpolation, which are presented here in detail. These techniques leverage k-means clustering, centroid shape signature representation, dynamic time warping, linear interpolation, and shape buffering to generate the additional polygons of an enriched trajectory. Using ground-truth objects, interpolation effectiveness is evaluated through a variety of measures based on several important characteristics that include spatial distance, area overlap, and shape (boundary) similarity. To our knowledge, this is the first research effort of this kind that attempts to address the broad problem of spatiotemporal interpolation of solar event trajectories. We conclude with a brief outline of future research directions and opportunities for related work in this area.

Snow water equivalent mapping in Norway

NASA Astrophysics Data System (ADS)

Tveito, O. E.; Udnæs, H.-C.; Engeset, R.; Førland, E. J.; Isaksen, K.; Mengistu, Z.

2003-04-01

In high latitude area snow covers the ground large parts of the year. Information about the water volume as snow is of major importance in many respects. Flood forecasters at NVE need it in order to assess possible flood risks. Hydropower producers need it to plan the most efficient production of the water in their reservoirs, traders to estimate the potential energy available for the market. Meteorologists on their side use the information as boundary conditions in weather forecasting models. The Norwegian meteorological institute has provided snow accumulation maps for Norway for more than 50 years. These maps are now produced twice a month in the winter season. They show the accumulated precipitation in the winter season from the day the permanent snow cover is established. They do however not take melting into account, and do therefore not give a good description of the actual snow amounts during and after periods with snowmelt. Due to an increased need for a direct measure of water volumes as snow cover, met.no and NVE initialized a joint project in order to establish maps of the actual snow cover expressed in water equivalents. The project utilizes recent developments in the use of GIS in spatial modeling. Daily precipitation and temperature are distributed in space by using objective spatial interpolation methods. The interpolation considers topographical and other geographical parameters as well as weather type information. A degree-day model is used at each modeling point to calculate snow-accumulation and snowmelt. The maps represent a spatial scale of 1x1 km2. The modeled snow reservoir is validated by snow pillow values as well traditional snow depth observations. Preliminary results show that the new snow modeling approach reproduces the snow water equivalent well. The spatial approach also opens for a wide use in the terms of areal analysis.

Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

PubMed

de Nazelle, Audrey; Arunachalam, Saravanan; Serre, Marc L

2010-08-01

States in the USA are required to demonstrate future compliance of criteria air pollutant standards by using both air quality monitors and model outputs. In the case of ozone, the demonstration tests aim at relying heavily on measured values, due to their perceived objectivity and enforceable quality. Weight given to numerical models is diminished by integrating them in the calculations only in a relative sense. For unmonitored locations, the EPA has suggested the use of a spatial interpolation technique to assign current values. We demonstrate that this approach may lead to erroneous assignments of nonattainment and may make it difficult for States to establish future compliance. We propose a method that combines different sources of information to map air pollution, using the Bayesian Maximum Entropy (BME) Framework. The approach gives precedence to measured values and integrates modeled data as a function of model performance. We demonstrate this approach in North Carolina, using the State's ozone monitoring network in combination with outputs from the Multiscale Air Quality Simulation Platform (MAQSIP) modeling system. We show that the BME data integration approach, compared to a spatial interpolation of measured data, improves the accuracy and the precision of ozone estimations across the state.

Spatial interpolation of hourly precipitation and dew point temperature for the identification of precipitation phase and hydrologic response in a mountainous catchment

NASA Astrophysics Data System (ADS)

Garen, D. C.; Kahl, A.; Marks, D. G.; Winstral, A. H.

2012-12-01

In mountainous catchments, it is well known that meteorological inputs, such as precipitation, air temperature, humidity, etc. vary greatly with elevation, spatial location, and time. Understanding and monitoring catchment inputs is necessary in characterizing and predicting hydrologic response to these inputs. This is true all of the time, but it is the most dramatically critical during large storms, when the input to the stream system due to rain and snowmelt creates the potential for flooding. Besides such crisis events, however, proper estimation of catchment inputs and their spatial distribution is also needed in more prosaic but no less important water and related resource management activities. The first objective of this study is to apply a geostatistical spatial interpolation technique (elevationally detrended kriging) to precipitation and dew point temperature on an hourly basis and explore its characteristics, accuracy, and other issues. The second objective is to use these spatial fields to determine precipitation phase (rain or snow) during a large, dynamic winter storm. The catchment studied is the data-rich Reynolds Creek Experimental Watershed near Boise, Idaho. As part of this analysis, precipitation-elevation lapse rates are examined for spatial and temporal consistency. A clear dependence of lapse rate on precipitation amount exists. Certain stations, however, are outliers from these relationships, showing that significant local effects can be present and raising the question of whether such stations should be used for spatial interpolation. Experiments with selecting subsets of stations demonstrate the importance of elevation range and spatial placement on the interpolated fields. Hourly spatial fields of precipitation and dew point temperature are used to distinguish precipitation phase during a large rain-on-snow storm in December 2005. This application demonstrates the feasibility of producing hourly spatial fields and the importance of doing so to support an accurate determination of precipitation phase for assessing catchment hydrologic response to the storm.

Evaluation of interpolation methods for TG-43 dosimetric parameters based on comparison with Monte Carlo data for high-energy brachytherapy sources.

PubMed

Pujades-Claumarchirant, Ma Carmen; Granero, Domingo; Perez-Calatayud, Jose; Ballester, Facundo; Melhus, Christopher; Rivard, Mark

2010-03-01

The aim of this work was to determine dose distributions for high-energy brachytherapy sources at spatial locations not included in the radial dose function g L ( r ) and 2D anisotropy function F ( r , θ ) table entries for radial distance r and polar angle θ . The objectives of this study are as follows: 1) to evaluate interpolation methods in order to accurately derive g L ( r ) and F ( r , θ ) from the reported data; 2) to determine the minimum number of entries in g L ( r ) and F ( r , θ ) that allow reproduction of dose distributions with sufficient accuracy. Four high-energy photon-emitting brachytherapy sources were studied: 60 Co model Co0.A86, 137 Cs model CSM-3, 192 Ir model Ir2.A85-2, and 169 Yb hypothetical model. The mesh used for r was: 0.25, 0.5, 0.75, 1, 1.5, 2-8 (integer steps) and 10 cm. Four different angular steps were evaluated for F ( r , θ ): 1°, 2°, 5° and 10°. Linear-linear and logarithmic-linear interpolation was evaluated for g L ( r ). Linear-linear interpolation was used to obtain F ( r , θ ) with resolution of 0.05 cm and 1°. Results were compared with values obtained from the Monte Carlo (MC) calculations for the four sources with the same grid. Linear interpolation of g L ( r ) provided differences ≤ 0.5% compared to MC for all four sources. Bilinear interpolation of F ( r , θ ) using 1° and 2° angular steps resulted in agreement ≤ 0.5% with MC for 60 Co, 192 Ir, and 169 Yb, while 137 Cs agreement was ≤ 1.5% for θ < 15°. The radial mesh studied was adequate for interpolating g L ( r ) for high-energy brachytherapy sources, and was similar to commonly found examples in the published literature. For F ( r , θ ) close to the source longitudinal-axis, polar angle step sizes of 1°-2° were sufficient to provide 2% accuracy for all sources.

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

PubMed

Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

2001-02-01

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

Elastic-wave-mode separation in TTI media with inverse-distance weighted interpolation involving position shading

NASA Astrophysics Data System (ADS)

Wang, Jian; Meng, Xiaohong; Zheng, Wanqiu

2017-10-01

The elastic-wave reverse-time migration of inhomogeneous anisotropic media is becoming the hotspot of research today. In order to ensure the accuracy of the migration, it is necessary to separate the wave mode into P-wave and S-wave before migration. For inhomogeneous media, the Kelvin-Christoffel equation can be solved in the wave-number domain by using the anisotropic parameters of the mesh nodes, and the polarization vector of the P-wave and S-wave at each node can be calculated and transformed into the space domain to obtain the quasi-differential operators. However, this method is computationally expensive, especially for the process of quasi-differential operators. In order to reduce the computational complexity, the wave-mode separation of mixed domain can be realized on the basis of a reference model in the wave-number domain. But conventional interpolation methods and reference model selection methods reduce the separation accuracy. In order to further improve the separation effect, this paper introduces an inverse-distance interpolation method involving position shading and uses the reference model selection method of random points scheme. This method adds the spatial weight coefficient K, which reflects the orientation of the reference point on the conventional IDW algorithm, and the interpolation process takes into account the combined effects of the distance and azimuth of the reference points. Numerical simulation shows that the proposed method can separate the wave mode more accurately using fewer reference models and has better practical value.

A fully 3D approach for metal artifact reduction in computed tomography.

PubMed

Kratz, Barbel; Weyers, Imke; Buzug, Thorsten M

2012-11-01

In computed tomography imaging metal objects in the region of interest introduce inconsistencies during data acquisition. Reconstructing these data leads to an image in spatial domain including star-shaped or stripe-like artifacts. In order to enhance the quality of the resulting image the influence of the metal objects can be reduced. Here, a metal artifact reduction (MAR) approach is proposed that is based on a recomputation of the inconsistent projection data using a fully three-dimensional Fourier-based interpolation. The success of the projection space restoration depends sensitively on a sensible continuation of neighboring structures into the recomputed area. Fortunately, structural information of the entire data is inherently included in the Fourier space of the data. This can be used for a reasonable recomputation of the inconsistent projection data. The key step of the proposed MAR strategy is the recomputation of the inconsistent projection data based on an interpolation using nonequispaced fast Fourier transforms (NFFT). The NFFT interpolation can be applied in arbitrary dimension. The approach overcomes the problem of adequate neighborhood definitions on irregular grids, since this is inherently given through the usage of higher dimensional Fourier transforms. Here, applications up to the third interpolation dimension are presented and validated. Furthermore, prior knowledge may be included by an appropriate damping of the transform during the interpolation step. This MAR method is applicable on each angular view of a detector row, on two-dimensional projection data as well as on three-dimensional projection data, e.g., a set of sequential acquisitions at different spatial positions, projection data of a spiral acquisition, or cone-beam projection data. Results of the novel MAR scheme based on one-, two-, and three-dimensional NFFT interpolations are presented. All results are compared in projection data space and spatial domain with the well-known one-dimensional linear interpolation strategy. In conclusion, it is recommended to include as much spatial information into the recomputation step as possible. This is realized by increasing the dimension of the NFFT. The resulting image quality can be enhanced considerably.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

A Modified Kriging Method to Interpolate the Soil Moisture Measured by Wireless Sensor Network with the Aid of Remote Sensing Images

NASA Astrophysics Data System (ADS)

Zhang, J.; Liu, Q.; Li, X.; Niu, H.; Cai, E.

2015-12-01

In recent years, wireless sensor network (WSN) emerges to collect Earth observation data at relatively low cost and light labor load, while its observations are still point-data. To learn the spatial distribution of a land surface parameter, interpolating the point data is necessary. Taking soil moisture (SM) for example, its spatial distribution is critical information for agriculture management, hydrological and ecological researches. This study developed a method to interpolate the WSN-measured SM to acquire the spatial distribution in a 5km*5km study area, located in the middle reaches of HEIHE River, western China. As SM is related to many factors such as topology, soil type, vegetation and etc., even the WSN observation grid is not dense enough to reflect the SM distribution pattern. Our idea is to revise the traditional Kriging algorithm, introducing spectral variables, i.e., vegetation index (VI) and abledo, from satellite imagery as supplementary information to aid the interpolation. Thus, the new Extended-Kriging algorithm operates on the spatial & spectral combined space. To run the algorithm, first we need to estimate the SM variance function, which is also extended to the combined space. As the number of WSN samples in the study area is not enough to gather robust statistics, we have to assume that the SM variance function is invariant over time. So, the variance function is estimated from a SM map, derived from the airborne CASI/TASI images acquired in July 10, 2012, and then applied to interpolate WSN data in that season. Data analysis indicates that the new algorithm can provide more details to the variation of land SM. Then, the Leave-one-out cross-validation is adopted to estimate the interpolation accuracy. Although a reasonable accuracy can be achieved, the result is not yet satisfactory. Besides improving the algorithm, the uncertainties in WSN measurements may also need to be controlled in our further work.

A Comparison of Spatial Analysis Methods for the Construction of Topographic Maps of Retinal Cell Density

PubMed Central

Garza-Gisholt, Eduardo; Hemmi, Jan M.; Hart, Nathan S.; Collin, Shaun P.

2014-01-01

Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed ‘by eye’. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation ‘respects’ the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the ‘noise’ caused by artefacts and permits a clearer representation of the dominant, ‘real’ distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome. PMID:24747568

Development of Spatial Scaling Technique of Forest Health Sample Point Information

NASA Astrophysics Data System (ADS)

Lee, J. H.; Ryu, J. E.; Chung, H. I.; Choi, Y. Y.; Jeon, S. W.; Kim, S. H.

2018-04-01

Forests provide many goods, Ecosystem services, and resources to humans such as recreation air purification and water protection functions. In rececnt years, there has been an increase in the factors that threaten the health of forests such as global warming due to climate change, environmental pollution, and the increase in interest in forests, and efforts are being made in various countries for forest management. Thus, existing forest ecosystem survey method is a monitoring method of sampling points, and it is difficult to utilize forests for forest management because Korea is surveying only a small part of the forest area occupying 63.7 % of the country (Ministry of Land Infrastructure and Transport Korea, 2016). Therefore, in order to manage large forests, a method of interpolating and spatializing data is needed. In this study, The 1st Korea Forest Health Management biodiversity Shannon;s index data (National Institute of Forests Science, 2015) were used for spatial interpolation. Two widely used methods of interpolation, Kriging method and IDW(Inverse Distance Weighted) method were used to interpolate the biodiversity index. Vegetation indices SAVI, NDVI, LAI and SR were used. As a result, Kriging method was the most accurate method.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods.

PubMed

Vizcaíno, Iván P; Carrera, Enrique V; Muñoz-Romero, Sergio; Cumbal, Luis H; Rojo-Álvarez, José Luis

2017-10-16

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer's kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer's kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

PubMed Central

Vizcaíno, Iván P.; Muñoz-Romero, Sergio; Cumbal, Luis H.

2017-01-01

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem. PMID:29035333

Topographic correction realization based on the CBERS-02B image

NASA Astrophysics Data System (ADS)

Qin, Hui-ping; Yi, Wei-ning; Fang, Yong-hua

2011-08-01

The special topography of mountain terrain will induce the retrieval distortion in same species and surface spectral lines. In order to improve the research accuracy of topographic surface characteristic, many researchers have focused on topographic correction. Topographic correction methods can be statistical-empirical model or physical model, in which the methods based on the digital elevation model data are most popular. Restricted by spatial resolution, previous model mostly corrected topographic effect based on Landsat TM image, whose spatial resolution is 30 meter that can be easily achieved from internet or calculated from digital map. Some researchers have also done topographic correction based on high spatial resolution images, such as Quickbird and Ikonos, but there is little correlative research on the topographic correction of CBERS-02B image. In this study, liao-ning mountain terrain was taken as the objective. The digital elevation model data was interpolated to 2.36 meter by 15 meter original digital elevation model one meter by one meter. The C correction, SCS+C correction, Minnaert correction and Ekstrand-r were executed to correct the topographic effect. Then the corrected results were achieved and compared. The images corrected with C correction, SCS+C correction, Minnaert correction and Ekstrand-r were compared, and the scatter diagrams between image digital number and cosine of solar incidence angel with respect to surface normal were shown. The mean value, standard variance, slope of scatter diagram, and separation factor were statistically calculated. The analysed result shows that the shadow is weakened in corrected images than the original images, and the three-dimensional affect is removed. The absolute slope of fitting lines in scatter diagram is minished. Minnaert correction method has the most effective result. These demonstrate that the former correction methods can be successfully adapted to CBERS-02B images. The DEM data can be interpolated step by step to get the corresponding spatial resolution approximately for the condition that high spatial resolution elevation data is hard to get.

Isoscapes of tree-ring carbon-13 perform like meteorological networks in predicting regional precipitation patterns

NASA Astrophysics Data System (ADS)

del Castillo, Jorge; Aguilera, Mònica; Voltas, Jordi; Ferrio, Juan Pedro

2013-04-01

Stable isotopes in tree rings provide climatic information with annual resolution dating back for centuries or even millennia. However, deriving spatially explicit climate models from isotope networks remains challenging. Here we propose a methodology to model regional precipitation from carbon isotope discrimination (Δ13C) in tree rings by (1) building regional spatial models of Δ13C (isoscapes), and (2) deriving precipitation maps from 13C-isoscapes, taking advantage of the response of Δ13C to precipitation in seasonally-dry climates. As a case study, we modeled the spatial distribution of mean annual precipitation (MAP) in the northeastern Iberian Peninsula, a region with complex orography and climate (MAP=303-1086 mm). We compiled wood Δ13C data for two Mediterranean species that exhibit complementary responses to seasonal precipitation (Pinus halepensis Mill., N=38; Quercus ilex L.; N=44; pooling period: 1975-2008). By combining multiple regression and geostatistical interpolation, we generated one 13C-isoscape for each species. A spatial model of MAP was then built as the sum of two complementary maps of seasonal precipitation, each one derived from the corresponding 13C-isoscape (September-November from Q. ilex; December-August from P. halepensis). Our approach showed a predictive power for MAP (RMSE=84 mm) nearly identical to that obtained by interpolating data directly from a similarly dense network of meteorological stations (RMSE=80-83 mm, N=65), being only outperformed when using a much denser meteorological network (RMSE=56-57 mm, N=340). This method offers new avenues for modeling spatial variability of past precipitation, exploiting the large amount of information currently available from tree-ring networks. Acknowledgements: This work was funded by MC-ERG-246725 (FP7, EU) and AGL 2012-40039-C02-02 (MINECO, Spain). JdC and JPF are supported by FPI fellowship (MCINN) and Ramón y Cajal programme (RYC-2008-02050, MINECO), respectively.

Generation of High Resolution Water Vapour Fields from GPS Observations and Integration With ECMWF and MODIS

NASA Astrophysics Data System (ADS)

Yu, C.; Li, Z.; Penna, N. T.

2016-12-01

Precipitable water vapour (PWV) can be routinely retrieved from ground-based GPS arrays in all-weather conditions and also in real-time. But to provide dense spatial coverage maps, for example for calibrating SAR images, for correcting atmospheric effects in Network RTK GPS positioning and which may be used for numerical weather prediction, the pointwise GPS PWV measurements must be interpolated. Several previous interpolation studies have addressed the importance of the elevation dependency of water vapour, but it is often a challenge to separate elevation-dependent tropospheric delays from turbulent components. We present a tropospheric turbulence iterative decomposition model that decouples the total PWV into (i) a stratified component highly correlated with topography which therefore delineates the vertical troposphere profile, and (ii) a turbulent component resulting from disturbance processes (e.g., severe weather) in the troposphere which trigger uncertain patterns in space and time. We will demonstrate that the iterative decoupled interpolation model generates improved dense tropospheric water vapour fields compared with elevation dependent models, with similar accuracies obtained over both flat and mountainous terrain, as well as for both inland and coastal areas. We will also show that our GPS-based model may be enhanced with ECMWF zenith tropospheric delay and MODIS PWV, producing multi-data sources high temporal-spatial resolution PWV fields. These fields were applied to Sentinel-1 SAR interferograms over the Los Angeles region, for which a maximum noise reduction due to atmosphere artifacts reached 85%. The results reveal that the turbulent troposphere noise, especially those in a SAR image, often occupy more than 50% of the total zenith tropospheric delay and exert systematic, rather than random patterns.

From daily to sub-daily time steps - Creating a high temporal and spatial resolution climate reference data set for hydrological modeling and bias-correction of RCM data

NASA Astrophysics Data System (ADS)

Willkofer, Florian; Wood, Raul R.; Schmid, Josef; von Trentini, Fabian; Ludwig, Ralf

2016-04-01

The ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) focuses on the effects of climate change on hydro-meteorological extreme events and their implications for water management in Bavaria and Québec. It builds on the conjoint analysis of a large ensemble of the CRCM5, driven by 50 members of the CanESM2, and the latest information provided through the CORDEX-initiative, to better assess the influence of natural climate variability and climatic change on the dynamics of extreme events. A critical point in the entire project is the preparation of a meteorological reference dataset with the required temporal (1-6h) and spatial (500m) resolution to be able to better evaluate hydrological extreme events in mesoscale river basins. For Bavaria a first reference data set (daily, 1km) used for bias-correction of RCM data was created by combining raster based data (E-OBS [1], HYRAS [2], MARS [3]) and interpolated station data using the meteorological interpolation schemes of the hydrological model WaSiM [4]. Apart from the coarse temporal and spatial resolution, this mosaic of different data sources is considered rather inconsistent and hence, not applicable for modeling of hydrological extreme events. Thus, the objective is to create a dataset with hourly data of temperature, precipitation, radiation, relative humidity and wind speed, which is then used for bias-correction of the RCM data being used as driver for hydrological modeling in the river basins. Therefore, daily data is disaggregated to hourly time steps using the 'Method of fragments' approach [5], based on available training stations. The disaggregation chooses fragments of daily values from observed hourly datasets, based on similarities in magnitude and behavior of previous and subsequent events. The choice of a certain reference station (hourly data, provision of fragments) for disaggregating daily station data (application of fragments) is crucial and several methods will be tested to achieve a profound spatial interpolation. This entire methodology shall be applicable for existing or newly developed datasets. References [1] Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres) (2008), 113, D20119, doi:10.1029/2008JD10201. [2] Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A. and A. Gratzki. A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift (2013), 22/3, p.238-256. [3] MARS-AGRI4CAST. AGRI4CAST Interpolated Meteorological Data. http://mars.jrc.ec.europa.eu/mars/ About-us/AGRI4CAST/Data-distribution/AGRI4CAST-Interpolated-Meteorological-Data. 2007, last accessed May 10th, 2013. [4] Schulla, J. Model Description WaSiM - Water balance Simulation Model. 2015, available at: http://wasim.ch/en/products/wasim_description.htm. [5] Sharma, A. and S. Srikanthan. Continuous Rainfall Simulation: A Nonparametric Alternative. 30th Hydrology and Water Resources Symposium, Launceston, Tasmania, 4-7 December, 2006.

Assignment of boundary conditions in embedded ground water flow models

USGS Publications Warehouse

Leake, S.A.

1998-01-01

Many small-scale ground water models are too small to incorporate distant aquifer boundaries. If a larger-scale model exists for the area of interest, flow and head values can be specified for boundaries in the smaller-scale model using values from the larger-scale model. Flow components along rows and columns of a large-scale block-centered finite-difference model can be interpolated to compute horizontal flow across any segment of a perimeter of a small-scale model. Head at cell centers of the larger-scale model can be interpolated to compute head at points on a model perimeter. Simple linear interpolation is proposed for horizontal interpolation of horizontal-flow components. Bilinear interpolation is proposed for horizontal interpolation of head values. The methods of interpolation provided satisfactory boundary conditions in tests using models of hypothetical aquifers.Many small-scale ground water models are too small to incorporate distant aquifer boundaries. If a larger-scale model exists for the area of interest, flow and head values can be specified for boundaries in the smaller-scale model using values from the larger-scale model. Flow components along rows and columns of a large-scale block-centered finite-difference model can be interpolated to compute horizontal flow across any segment of a perimeter of a small-scale model. Head at cell centers of the larger.scale model can be interpolated to compute head at points on a model perimeter. Simple linear interpolation is proposed for horizontal interpolation of horizontal-flow components. Bilinear interpolation is proposed for horizontal interpolation of head values. The methods of interpolation provided satisfactory boundary conditions in tests using models of hypothetical aquifers.

Comparison of different interpolation methods for spatial distribution of soil organic carbon and some soil properties in the Black Sea backward region of Turkey

NASA Astrophysics Data System (ADS)

Göl, Ceyhun; Bulut, Sinan; Bolat, Ferhat

2017-10-01

The purpose of this research is to compare the spatial variability of soil organic carbon (SOC) in four adjacent land uses including the cultivated area, the grassland area, the plantation area and the natural forest area in the semi - arid region of Black Sea backward region of Turkey. Some of the soil properties, including total nitrogen, SOC, soil organic matter, and bulk density were measured on a grid with a 50 m sampling distance on the top soil (0-15 cm depth). Accordingly, a total of 120 samples were taken from the four adjacent land uses. Data was analyzed using geostatistical methods. The methods used were: Block kriging (BK), co - kriging (CK) with organic matter, total nitrogen and bulk density as auxiliary variables and inverse distance weighting (IDW) methods with the power of 1, 2 and 4. The methods were compared using a performance criteria that included root mean square error (RMSE), mean absolute error (MAE) and the coefficient of correlation (r). The one - way ANOVA test showed that differences between the natural (0.6653 ± 0.2901) - plantation forest (0.7109 ± 0.2729) areas and the grassland (1.3964 ± 0.6828) - cultivated areas (1.5851 ± 0.5541) were statistically significant at 0.05 level (F = 28.462). The best model for describing spatially variation of SOC was CK with the lowest error criteria (RMSE = 0.3342, MAE = 0.2292) and the highest coefficient of correlation (r = 0.84). The spatial structure of SOC could be well described by the spherical model. The nugget effect indicated that SOC was moderately dependent on the study area. The error distributions of the model showed that the improved model was unbiased in predicting the spatial distribution of SOC. This study's results revealed that an explanatory variable linked SOC increased success of spatial interpolation methods. In subsequent studies, this case should be taken into account for reaching more accurate outputs.

Geotechnical parameter spatial distribution stochastic analysis based on multi-precision information assimilation

NASA Astrophysics Data System (ADS)

Wang, C.; Rubin, Y.

2014-12-01

Spatial distribution of important geotechnical parameter named compression modulus Es contributes considerably to the understanding of the underlying geological processes and the adequate assessment of the Es mechanics effects for differential settlement of large continuous structure foundation. These analyses should be derived using an assimilating approach that combines in-situ static cone penetration test (CPT) with borehole experiments. To achieve such a task, the Es distribution of stratum of silty clay in region A of China Expo Center (Shanghai) is studied using the Bayesian-maximum entropy method. This method integrates rigorously and efficiently multi-precision of different geotechnical investigations and sources of uncertainty. Single CPT samplings were modeled as a rational probability density curve by maximum entropy theory. Spatial prior multivariate probability density function (PDF) and likelihood PDF of the CPT positions were built by borehole experiments and the potential value of the prediction point, then, preceding numerical integration on the CPT probability density curves, the posterior probability density curve of the prediction point would be calculated by the Bayesian reverse interpolation framework. The results were compared between Gaussian Sequential Stochastic Simulation and Bayesian methods. The differences were also discussed between single CPT samplings of normal distribution and simulated probability density curve based on maximum entropy theory. It is shown that the study of Es spatial distributions can be improved by properly incorporating CPT sampling variation into interpolation process, whereas more informative estimations are generated by considering CPT Uncertainty for the estimation points. Calculation illustrates the significance of stochastic Es characterization in a stratum, and identifies limitations associated with inadequate geostatistical interpolation techniques. This characterization results will provide a multi-precision information assimilation method of other geotechnical parameters.

Spatial reasoning to determine stream network from LANDSAT imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Wang, S.; Elliott, D. B.

1983-01-01

In LANDSAT imagery, spectral and spatial information can be used to detect the drainage network as well as the relative elevation model in mountainous terrain. To do this, mixed information of material reflectance in the original LANDSAT imagery must be separated. From the material reflectance information, big visible rivers can be detected. From the topographic modulation information, ridges and valleys can be detected and assigned relative elevations. A complete elevation model can be generated by interpolating values for nonridge and non-valley pixels. The small streams not detectable from material reflectance information can be located in the valleys with flow direction known from the elevation model. Finally, the flow directions of big visible rivers can be inferred by solving a consistent labeling problem based on a set of spatial reasoning constraints.

Gradient-based interpolation method for division-of-focal-plane polarimeters.

PubMed

Gao, Shengkui; Gruev, Viktor

2013-01-14

Recent advancements in nanotechnology and nanofabrication have allowed for the emergence of the division-of-focal-plane (DoFP) polarization imaging sensors. These sensors capture polarization properties of the optical field at every imaging frame. However, the DoFP polarization imaging sensors suffer from large registration error as well as reduced spatial-resolution output. These drawbacks can be improved by applying proper image interpolation methods for the reconstruction of the polarization results. In this paper, we present a new gradient-based interpolation method for DoFP polarimeters. The performance of the proposed interpolation method is evaluated against several previously published interpolation methods by using visual examples and root mean square error (RMSE) comparison. We found that the proposed gradient-based interpolation method can achieve better visual results while maintaining a lower RMSE than other interpolation methods under various dynamic ranges of a scene ranging from dim to bright conditions.

Detection of Spatially Unresolved (Nominally Sub-Pixel) Submerged and Surface Targets Using Hyperspectral Data

DTIC Science & Technology

2012-09-01

Feasibility (MT Modeling ) a. Continuum of mixture distributions interpolated b. Mixture infeasibilities calculated for each pixel c. Valid detections...Visible/Infrared Imaging Spectrometer BRDF Bidirectional Reflectance Distribution Function CASI Compact Airborne Spectrographic Imager CCD...filtering (MTMF), and was designed by Healey and Slater (1999) to use “a physical model to generate the set of sensor spectra for a target that will be

Monitoring global snow cover

NASA Technical Reports Server (NTRS)

Armstrong, Richard; Hardman, Molly

1991-01-01

A snow model that supports the daily, operational analysis of global snow depth and age has been developed. It provides improved spatial interpolation of surface reports by incorporating digital elevation data, and by the application of regionalized variables (kriging) through the use of a global snow depth climatology. Where surface observations are inadequate, the model applies satellite remote sensing. Techniques for extrapolation into data-void mountain areas and a procedure to compute snow melt are also contained in the model.

Spatial Interpolation of Rain-field Dynamic Time-Space Evolution in Hong Kong

NASA Astrophysics Data System (ADS)

Liu, P.; Tung, Y. K.

2017-12-01

Accurate and reliable measurement and prediction of spatial and temporal distribution of rain-field over a wide range of scales are important topics in hydrologic investigations. In this study, geostatistical treatment of precipitation field is adopted. To estimate the rainfall intensity over a study domain with the sample values and the spatial structure from the radar data, the cumulative distribution functions (CDFs) at all unsampled locations were estimated. Indicator Kriging (IK) was used to estimate the exceedance probabilities for different pre-selected cutoff levels and a procedure was implemented for interpolating CDF values between the thresholds that were derived from the IK. Different interpolation schemes of the CDF were proposed and their influences on the performance were also investigated. The performance measures and visual comparison between the observed rain-field and the IK-based estimation suggested that the proposed method can provide fine results of estimation of indicator variables and is capable of producing realistic image.

Spatial interpolation of pesticide drift from hand-held knapsack sprayers used in potato production

NASA Astrophysics Data System (ADS)

Garcia-Santos, Glenda; Pleschberger, Martin; Scheiber, Michael; Pilz, Jürgen

2017-04-01

Tropical mountainous regions in developing countries are often neglected in research and policy but represent key areas to be considered if sustainable agricultural and rural development is to be promoted. One example is the lack of information of pesticide drift soil deposition, which can support pesticide risk assessment for soil, surface water, bystanders and off-target plants and fauna. This is considered a serious gap, given the evidence of pesticide-related poisoning in those regions. Empirical data of drift deposition of a pesticide surrogate, Uranine tracer, were obtained within one of the highest potato producing regions in Colombia. Based on the empirical data, different spatial interpolation techniques i.e. Thiessen, inverse distance squared weighting, co-kriging, pair-copulas and drift curves depending on distance and wind speed were tested and optimized. Results of the best performing spatial interpolation methods, suitable curves to assess mean relative drift and implications on risk assessment studies will be presented.

Use of geostatistics for remediation planning to transcend urban political boundaries.

PubMed

Milillo, Tammy M; Sinha, Gaurav; Gardella, Joseph A

2012-11-01

Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible. Copyright © 2012 Elsevier Ltd. All rights reserved.

Restoring method for missing data of spatial structural stress monitoring based on correlation

NASA Astrophysics Data System (ADS)

Zhang, Zeyu; Luo, Yaozhi

2017-07-01

Long-term monitoring of spatial structures is of great importance for the full understanding of their performance and safety. The missing part of the monitoring data link will affect the data analysis and safety assessment of the structure. Based on the long-term monitoring data of the steel structure of the Hangzhou Olympic Center Stadium, the correlation between the stress change of the measuring points is studied, and an interpolation method of the missing stress data is proposed. Stress data of correlated measuring points are selected in the 3 months of the season when missing data is required for fitting correlation. Data of daytime and nighttime are fitted separately for interpolation. For a simple linear regression when single point's correlation coefficient is 0.9 or more, the average error of interpolation is about 5%. For multiple linear regression, the interpolation accuracy is not significantly increased after the number of correlated points is more than 6. Stress baseline value of construction step should be calculated before interpolating missing data in the construction stage, and the average error is within 10%. The interpolation error of continuous missing data is slightly larger than that of the discrete missing data. The data missing rate of this method should better not exceed 30%. Finally, a measuring point's missing monitoring data is restored to verify the validity of the method.

Digital x-ray tomosynthesis with interpolated projection data for thin slab objects

NASA Astrophysics Data System (ADS)

Ha, S.; Yun, J.; Kim, H. K.

2017-11-01

In relation with a thin slab-object inspection, we propose a digital tomosynthesis reconstruction with fewer numbers of measured projections in combinations with additional virtual projections, which are produced by interpolating the measured projections. Hence we can reconstruct tomographic images with less few-view artifacts. The projection interpolation assumes that variations in cone-beam ray path-lengths through an object are negligible and the object is rigid. The interpolation is performed in the projection-space domain. Pixel values in the interpolated projection are the weighted sum of pixel values of the measured projections considering their projection angles. The experimental simulation shows that the proposed method can enhance the contrast-to-noise performance in reconstructed images while sacrificing the spatial resolving power.

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

PubMed

Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

2015-06-01

Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation

NASA Astrophysics Data System (ADS)

Carreau, J.; Naveau, P.; Neppel, L.

2017-05-01

The French Mediterranean is subject to intense precipitation events occurring mostly in autumn. These can potentially cause flash floods, the main natural danger in the area. The distribution of these events follows specific spatial patterns, i.e., some sites are more likely to be affected than others. The peaks-over-threshold approach consists in modeling extremes, such as heavy precipitation, by the generalized Pareto (GP) distribution. The shape parameter of the GP controls the probability of extreme events and can be related to the hazard level of a given site. When interpolating across a region, the shape parameter should reproduce the observed spatial patterns of the probability of heavy precipitation. However, the shape parameter estimators have high uncertainty which might hide the underlying spatial variability. As a compromise, we choose to let the shape parameter vary in a moderate fashion. More precisely, we assume that the region of interest can be partitioned into subregions with constant hazard level. We formalize the model as a conditional mixture of GP distributions. We develop a two-step inference strategy based on probability weighted moments and put forward a cross-validation procedure to select the number of subregions. A synthetic data study reveals that the inference strategy is consistent and not very sensitive to the selected number of subregions. An application on daily precipitation data from the French Mediterranean shows that the conditional mixture of GPs outperforms two interpolation approaches (with constant or smoothly varying shape parameter).

A comparison of interpolation methods for predicting spatial variability of soil organic matter content in Eastern Croatia

NASA Astrophysics Data System (ADS)

Đurđević, Boris; Jug, Irena; Jug, Danijel; Vukadinović, Vesna; Bogunović, Igor; Brozović, Bojana; Stipešević, Bojan

2017-04-01

Soil organic matter (SOM) plays crucial role in soil health and productivity and represents one of the key functions for determining soil degradation and soil suitability for crop production. Nowadays, continuing decline of organic matter in soils in agroecosystems, due to inappropriate agricultural practice (burning and removal of crop residue, overgrazing, inappropriate tillage, etc.) and environmental conditions (climate change, extreme weather conditions, erosion) leads to devastating soil degradation processes and decreases soil productivity. The main objectives of this research is to compare three different interpolation methods (Inverse Distance Weighting IDW, Ordinary kriging OK and Empirical Bayesian Kriging EBK) and provide best spatial predictor in order to ensure detailed analysis of the agricultural land in Osijek-Baranja County, Croatia. A number of 9,099 soil samples have been compiled from layer 0-30 cm and analyzed in laboratory. The average value of SOM in the study area was 2.66%, while 70.7 % of samples had SOM value below 3% in Osijek-Baranja County. Among the applied methods, the lowest root mean square error was recorded under Empirical Bayesian Kriging method which had most accurately assessed soil organic matter. The main advantage of EBK is that the process of creating a valid kriging model is automated so the manual parameter adjusting is eliminated, and this resulted with reduced uncertainty of EBK model. Conducted interpolation and visualization of data showed that 85.7% of agricultural land in Osijek-Baranja County has SOM content lower than 3%, which may indicate some sort of soil degradation process. By using interpolation methods combined with visualization of data, we can detect problematic areas much easier and with additional analysis, suggest measures to repair degraded soils. This kind of approach to problem solving in agriculture can be applied on various agroecological conditions and can significantly facilitate and accelerate the decision-making process, and thus directly affect the profitability and sustainability of agricultural production.

AHP-based spatial analysis of water quality impact assessment due to change in vehicular traffic caused by highway broadening in Sikkim Himalaya

NASA Astrophysics Data System (ADS)

Banerjee, Polash; Ghose, Mrinal Kanti; Pradhan, Ratika

2018-05-01

Spatial analysis of water quality impact assessment of highway projects in mountainous areas remains largely unexplored. A methodology is presented here for Spatial Water Quality Impact Assessment (SWQIA) due to highway-broadening-induced vehicular traffic change in the East district of Sikkim. Pollution load of the highway runoff was estimated using an Average Annual Daily Traffic-Based Empirical model in combination with mass balance model to predict pollution in the rivers within the study area. Spatial interpolation and overlay analysis were used for impact mapping. Analytic Hierarchy Process-Based Water Quality Status Index was used to prepare a composite impact map. Model validation criteria, cross-validation criteria, and spatial explicit sensitivity analysis show that the SWQIA model is robust. The study shows that vehicular traffic is a significant contributor to water pollution in the study area. The model is catering specifically to impact analysis of the concerned project. It can be an aid for decision support system for the project stakeholders. The applicability of SWQIA model needs to be explored and validated in the context of a larger set of water quality parameters and project scenarios at a greater spatial scale.

Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system.

PubMed

Huang, Ni; Wang, Li; Guo, Yiqiang; Hao, Pengyu; Niu, Zheng

2014-01-01

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m(-2) s(-1). The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

Modeling Spatial Patterns of Soil Respiration in Maize Fields from Vegetation and Soil Property Factors with the Use of Remote Sensing and Geographical Information System

PubMed Central

Huang, Ni; Wang, Li; Guo, Yiqiang; Hao, Pengyu; Niu, Zheng

2014-01-01

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China. PMID:25157827

A catchment scale water balance model for FIFE

NASA Technical Reports Server (NTRS)

Famiglietti, J. S.; Wood, E. F.; Sivapalan, M.; Thongs, D. J.

1992-01-01

A catchment scale water balance model is presented and used to predict evaporation from the King's Creek catchment at the First ISLSCP Field Experiment site on the Konza Prairie, Kansas. The model incorporates spatial variability in topography, soils, and precipitation to compute the land surface hydrologic fluxes. A network of 20 rain gages was employed to measure rainfall across the catchment in the summer of 1987. These data were spatially interpolated and used to drive the model during storm periods. During interstorm periods the model was driven by the estimated potential evaporation, which was calculated using net radiation data collected at site 2. Model-computed evaporation is compared to that observed, both at site 2 (grid location 1916-BRS) and the catchment scale, for the simulation period from June 1 to October 9, 1987.

Accuration of Time Series and Spatial Interpolation Method for Prediction of Precipitation Distribution on the Geographical Information System

NASA Astrophysics Data System (ADS)

Prasetyo, S. Y. J.; Hartomo, K. D.

2018-01-01

The Spatial Plan of the Province of Central Java 2009-2029 identifies that most regencies or cities in Central Java Province are very vulnerable to landslide disaster. The data are also supported by other data from Indonesian Disaster Risk Index (In Indonesia called Indeks Risiko Bencana Indonesia) 2013 that suggest that some areas in Central Java Province exhibit a high risk of natural disasters. This research aims to develop an application architecture and analysis methodology in GIS to predict and to map rainfall distribution. We propose our GIS architectural application of “Multiplatform Architectural Spatiotemporal” and data analysis methods of “Triple Exponential Smoothing” and “Spatial Interpolation” as our significant scientific contribution. This research consists of 2 (two) parts, namely attribute data prediction using TES method and spatial data prediction using Inverse Distance Weight (IDW) method. We conduct our research in 19 subdistricts in the Boyolali Regency, Central Java Province, Indonesia. Our main research data is the biweekly rainfall data in 2000-2016 Climatology, Meteorology, and Geophysics Agency (In Indonesia called Badan Meteorologi, Klimatologi, dan Geofisika) of Central Java Province and Laboratory of Plant Disease Observations Region V Surakarta, Central Java. The application architecture and analytical methodology of “Multiplatform Architectural Spatiotemporal” and spatial data analysis methodology of “Triple Exponential Smoothing” and “Spatial Interpolation” can be developed as a GIS application framework of rainfall distribution for various applied fields. The comparison between the TES and IDW methods show that relative to time series prediction, spatial interpolation exhibit values that are approaching actual. Spatial interpolation is closer to actual data because computed values are the rainfall data of the nearest location or the neighbour of sample values. However, the IDW’s main weakness is that some area might exhibit the rainfall value of 0. The representation of 0 in the spatial interpolation is mainly caused by the absence of rainfall data in the nearest sample point or too far distance that produces smaller weight.

Analysis of rainfall distribution in Kelantan river basin, Malaysia

NASA Astrophysics Data System (ADS)

Che Ros, Faizah; Tosaka, Hiroyuki

2018-03-01

Using rainfall gauge on its own as input carries great uncertainties regarding runoff estimation, especially when the area is large and the rainfall is measured and recorded at irregular spaced gauging stations. Hence spatial interpolation is the key to obtain continuous and orderly rainfall distribution at unknown points to be the input to the rainfall runoff processes for distributed and semi-distributed numerical modelling. It is crucial to study and predict the behaviour of rainfall and river runoff to reduce flood damages of the affected area along the Kelantan river. Thus, a good knowledge on rainfall distribution is essential in early flood prediction studies. Forty six rainfall stations and their daily time-series were used to interpolate gridded rainfall surfaces using inverse-distance weighting (IDW), inverse-distance and elevation weighting (IDEW) methods and average rainfall distribution. Sensitivity analysis for distance and elevation parameters were conducted to see the variation produced. The accuracy of these interpolated datasets was examined using cross-validation assessment.

Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.

Objective Interpolation of Scatterometer Winds

NASA Technical Reports Server (NTRS)

Tang, Wenquing; Liu, W. Timothy

1996-01-01

Global wind fields are produced by successive corrections that use measurements by the European Remote Sensing Satellite (ERS-1) scatterometer. The methodology is described. The wind fields at 10-meter height provided by the European Center for Medium-Range Weather Forecasting (ECMWF) are used to initialize the interpolation process. The interpolated wind field product ERSI is evaluated in terms of its improvement over the initial guess field (ECMWF) and the bin-averaged ERS-1 wind field (ERSB). Spatial and temporal differences between ERSI, ECMWF and ERSB are presented and discussed.

Integrated model for predicting rice yield with climate change

NASA Astrophysics Data System (ADS)

Park, Jin-Ki; Das, Amrita; Park, Jong-Hwa

2018-04-01

Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country's economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.

Visual distraction and visuo-spatial memory: a sandwich effect.

PubMed

Tremblay, Sébastien; Nicholls, Alastair P; Parmentier, Fabrice B R; Jones, Dylan M

2005-01-01

The functional characteristics of visuo-spatial serial memory and its sensitivity to irrelevant visual information are examined in the present study, through the investigation of the sandwich effect (e.g., Hitch, 1975). The memory task was one of serial recall for the position of a sequence of seven spatially and temporally separated dots. The presence of irrelevant dots interpolated with to-be-remembered dots affected performance over most serial positions (Experiment 1) but that effect was significantly reduced when the interpolated dots were distinct from the to-be-remembered dots by colour and shape (Experiment 2). Parallels are made between verbal and spatial serial memory, and the reduction of the sandwich effect is discussed in terms of the contribution of perceptual organisation and attentional factors in short-term memory.

Creating a monthly time series of the potentiometric surface in the Upper Floridan aquifer, Northern Tampa Bay area, Florida, January 2000-December 2009

USGS Publications Warehouse

Lee, Terrie M.; Fouad, Geoffrey G.

2014-01-01

In Florida’s karst terrain, where groundwater and surface waters interact, a mapping time series of the potentiometric surface in the Upper Floridan aquifer offers a versatile metric for assessing the hydrologic condition of both the aquifer and overlying streams and wetlands. Long-term groundwater monitoring data were used to generate a monthly time series of potentiometric surfaces in the Upper Floridan aquifer over a 573-square-mile area of west-central Florida between January 2000 and December 2009. Recorded groundwater elevations were collated for 260 groundwater monitoring wells in the Northern Tampa Bay area, and a continuous time series of daily observations was created for 197 of the wells by estimating missing daily values through regression relations with other monitoring wells. Kriging was used to interpolate the monthly average potentiometric-surface elevation in the Upper Floridan aquifer over a decade. The mapping time series gives spatial and temporal coherence to groundwater monitoring data collected continuously over the decade by three different organizations, but at various frequencies. Further, the mapping time series describes the potentiometric surface beneath parts of six regionally important stream watersheds and 11 municipal well fields that collectively withdraw about 90 million gallons per day from the Upper Floridan aquifer. Monthly semivariogram models were developed using monthly average groundwater levels at wells. Kriging was used to interpolate the monthly average potentiometric-surface elevations and to quantify the uncertainty in the interpolated elevations. Drawdown of the potentiometric surface within well fields was likely the cause of a characteristic decrease and then increase in the observed semivariance with increasing lag distance. This characteristic made use of the hole effect model appropriate for describing the monthly semivariograms and the interpolated surfaces. Spatial variance reflected in the monthly semivariograms decreased markedly between 2002 and 2003, timing that coincided with decreases in well-field pumping. Cross-validation results suggest that the kriging interpolation may smooth over the drawdown of the potentiometric surface near production wells. The groundwater monitoring network of 197 wells yielded an average kriging error in the potentiometric-surface elevations of 2 feet or less over approximately 70 percent of the map area. Additional data collection within the existing monitoring network of 260 wells and near selected well fields could reduce the error in individual months. Reducing the kriging error in other areas would require adding new monitoring wells. Potentiometric-surface elevations fluctuated by as much as 30 feet over the study period, and the spatially averaged elevation for the entire surface rose by about 2 feet over the decade. Monthly potentiometric-surface elevations describe the lateral groundwater flow patterns in the aquifer and are usable at a variety of spatial scales to describe vertical groundwater recharge and discharge conditions for overlying surface-water features.

Modeling the uncertainty of estimating forest carbon stocks in China

NASA Astrophysics Data System (ADS)

Yue, T. X.; Wang, Y. F.; Du, Z. P.; Zhao, M. W.; Zhang, L. L.; Zhao, N.; Lu, M.; Larocque, G. R.; Wilson, J. P.

2015-12-01

Earth surface systems are controlled by a combination of global and local factors, which cannot be understood without accounting for both the local and global components. The system dynamics cannot be recovered from the global or local controls alone. Ground forest inventory is able to accurately estimate forest carbon stocks at sample plots, but these sample plots are too sparse to support the spatial simulation of carbon stocks with required accuracy. Satellite observation is an important source of global information for the simulation of carbon stocks. Satellite remote-sensing can supply spatially continuous information about the surface of forest carbon stocks, which is impossible from ground-based investigations, but their description has considerable uncertainty. In this paper, we validated the Lund-Potsdam-Jena dynamic global vegetation model (LPJ), the Kriging method for spatial interpolation of ground sample plots and a satellite-observation-based approach as well as an approach for fusing the ground sample plots with satellite observations and an assimilation method for incorporating the ground sample plots into LPJ. The validation results indicated that both the data fusion and data assimilation approaches reduced the uncertainty of estimating carbon stocks. The data fusion had the lowest uncertainty by using an existing method for high accuracy surface modeling to fuse the ground sample plots with the satellite observations (HASM-SOA). The estimates produced with HASM-SOA were 26.1 and 28.4 % more accurate than the satellite-based approach and spatial interpolation of the sample plots, respectively. Forest carbon stocks of 7.08 Pg were estimated for China during the period from 2004 to 2008, an increase of 2.24 Pg from 1984 to 2008, using the preferred HASM-SOA method.

Applications of remote sensing to stream discharge predictions

NASA Technical Reports Server (NTRS)

Krause, F. R.; Winn, C. B.

1972-01-01

A feasibility study has been initiated on the use of remote earth observations for augmenting stream discharge prediction for the design and/or operation of major reservoir systems, pumping systems and irrigation systems. The near-term objectives are the interpolation of sparsely instrumented precipitation surveillance networks and the direct measurement of water loss by evaporation. The first steps of the study covered a survey of existing reservoir systems, stream discharge prediction methods, gage networks and the development of a self-adaptive variation of the Kentucky Watershed model, SNOPSET, that includes snowmelt. As a result of these studies, a special three channel scanner is being built for a small aircraft, which should provide snow, temperature and water vapor maps for the spatial and temporal interpolation of stream gages.

Holes in the ocean: Filling voids in bathymetric lidar data

NASA Astrophysics Data System (ADS)

Coleman, John B.; Yao, Xiaobai; Jordan, Thomas R.; Madden, Marguertie

2011-04-01

The mapping of coral reefs may be efficiently accomplished by the use of airborne laser bathymetry. However, there are often data holes within the bathymetry data which must be filled in order to produce a complete representation of the coral habitat. This study presents a method to fill these data holes through data merging and interpolation. The method first merges ancillary digital sounding data with airborne laser bathymetry data in order to populate data points in all areas but particularly those of data holes. What follows is to generate an elevation surface by spatial interpolation based on the merged data points obtained in the first step. We conduct a case study of the Dry Tortugas National Park in Florida and produced an enhanced digital elevation model in the ocean with this method. Four interpolation techniques, including Kriging, natural neighbor, spline, and inverse distance weighted, are implemented and evaluated on their ability to accurately and realistically represent the shallow-water bathymetry of the study area. The natural neighbor technique is found to be the most effective. Finally, this enhanced digital elevation model is used in conjunction with Ikonos imagery to produce a complete, three-dimensional visualization of the study area.

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

PubMed Central

Zhang, Jialin; Li, Xiuhong; Yang, Rongjin; Liu, Qiang; Zhao, Long; Dou, Baocheng

2017-01-01

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation. PMID:28617351

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India.

PubMed

Kumar, Awkash; Gupta, Indrani; Brandt, Jørgen; Kumar, Rakesh; Dikshit, Anil Kumar; Patil, Rashmi S

2016-05-01

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO2), nitrogen dioxide (NO2) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution. Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.

Evaluation of statistical and rainfall-runoff models for predicting historical daily streamflow time series in the Des Moines and Iowa River watersheds

USGS Publications Warehouse

Farmer, William H.; Knight, Rodney R.; Eash, David A.; Kasey J. Hutchinson,; Linhart, S. Mike; Christiansen, Daniel E.; Archfield, Stacey A.; Over, Thomas M.; Kiang, Julie E.

2015-08-24

Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.

GRID2D/3D: A computer program for generating grid systems in complex-shaped two- and three-dimensional spatial domains. Part 2: User's manual and program listing

NASA Technical Reports Server (NTRS)

Bailey, R. T.; Shih, T. I.-P.; Nguyen, H. L.; Roelke, R. J.

1990-01-01

An efficient computer program, called GRID2D/3D, was developed to generate single and composite grid systems within geometrically complex two- and three-dimensional (2- and 3-D) spatial domains that can deform with time. GRID2D/3D generates single grid systems by using algebraic grid generation methods based on transfinite interpolation in which the distribution of grid points within the spatial domain is controlled by stretching functions. All single grid systems generated by GRID2D/3D can have grid lines that are continuous and differentiable everywhere up to the second-order. Also, grid lines can intersect boundaries of the spatial domain orthogonally. GRID2D/3D generates composite grid systems by patching together two or more single grid systems. The patching can be discontinuous or continuous. For continuous composite grid systems, the grid lines are continuous and differentiable everywhere up to the second-order except at interfaces where different single grid systems meet. At interfaces where different single grid systems meet, the grid lines are only differentiable up to the first-order. For 2-D spatial domains, the boundary curves are described by using either cubic or tension spline interpolation. For 3-D spatial domains, the boundary surfaces are described by using either linear Coon's interpolation, bi-hyperbolic spline interpolation, or a new technique referred to as 3-D bi-directional Hermite interpolation. Since grid systems generated by algebraic methods can have grid lines that overlap one another, GRID2D/3D contains a graphics package for evaluating the grid systems generated. With the graphics package, the user can generate grid systems in an interactive manner with the grid generation part of GRID2D/3D. GRID2D/3D is written in FORTRAN 77 and can be run on any IBM PC, XT, or AT compatible computer. In order to use GRID2D/3D on workstations or mainframe computers, some minor modifications must be made in the graphics part of the program; no modifications are needed in the grid generation part of the program. The theory and method used in GRID2D/3D is described.

GRID2D/3D: A computer program for generating grid systems in complex-shaped two- and three-dimensional spatial domains. Part 1: Theory and method

NASA Technical Reports Server (NTRS)

Shih, T. I.-P.; Bailey, R. T.; Nguyen, H. L.; Roelke, R. J.

1990-01-01

An efficient computer program, called GRID2D/3D was developed to generate single and composite grid systems within geometrically complex two- and three-dimensional (2- and 3-D) spatial domains that can deform with time. GRID2D/3D generates single grid systems by using algebraic grid generation methods based on transfinite interpolation in which the distribution of grid points within the spatial domain is controlled by stretching functions. All single grid systems generated by GRID2D/3D can have grid lines that are continuous and differentiable everywhere up to the second-order. Also, grid lines can intersect boundaries of the spatial domain orthogonally. GRID2D/3D generates composite grid systems by patching together two or more single grid systems. The patching can be discontinuous or continuous. For continuous composite grid systems, the grid lines are continuous and differentiable everywhere up to the second-order except at interfaces where different single grid systems meet. At interfaces where different single grid systems meet, the grid lines are only differentiable up to the first-order. For 2-D spatial domains, the boundary curves are described by using either cubic or tension spline interpolation. For 3-D spatial domains, the boundary surfaces are described by using either linear Coon's interpolation, bi-hyperbolic spline interpolation, or a new technique referred to as 3-D bi-directional Hermite interpolation. Since grid systems generated by algebraic methods can have grid lines that overlap one another, GRID2D/3D contains a graphics package for evaluating the grid systems generated. With the graphics package, the user can generate grid systems in an interactive manner with the grid generation part of GRID2D/3D. GRID2D/3D is written in FORTRAN 77 and can be run on any IBM PC, XT, or AT compatible computer. In order to use GRID2D/3D on workstations or mainframe computers, some minor modifications must be made in the graphics part of the program; no modifications are needed in the grid generation part of the program. This technical memorandum describes the theory and method used in GRID2D/3D.

Interpretation of heavy rainfall spatial distribution in mountain watersheds by copula functions

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Balistrocchi, Matteo

2016-04-01

The spatial distribution of heavy rainfalls can strongly influence flood dynamics in mountain watersheds, depending on their geomorphologic features, namely orography, slope, land covers and soil types. Unfortunately, the direct observation of rainfall fields by meteorological radar is very difficult in this situation, so that interpolation of rain gauge observations or downscaling of meteorological predictions must be adopted to derive spatial rainfall distributions. To do so, various stochastic and physically based approaches are already available, even though the first one is the most familiar in hydrology. Indeed, Kriging interpolation procedures represent very popular techniques to face this problem by means of a stochastic approach. A certain number of restrictive assumptions and parameter uncertainties however affects Kriging. Many alternative formulations and additional procedures were therefore developed during the last decades. More recently, copula functions (Joe, 1997; Nelsen, 2006; Salvadori et al. 2007) were suggested to provide a more straightforward solution to carry out spatial interpolations of hydrologic variables (Bardossy & Pegram; 2009). Main advantages lie in the possibility of i) assessing the dependence structure relating to rainfall variables independently of marginal distributions, ii) expressing the association degree through rank correlation coefficients, iii) implementing marginal distributions and copula functions belonging to different models to develop complex joint distribution functions, iv) verifying the model reliability by effective statistical tests (Genest et al., 2009). A suitable case study to verify these potentialities is provided by the Taro River, a right-bank tributary of the Po River (northern Italy), whose contributing area amounts to about 2˙000 km2. The mountain catchment area is divided into two similar watersheds, so that spatial distribution is crucial in extreme flood event generation. A quite well diffused hydro-meteorological network, consisting of about 30 rain gauges and 10 hydrometers, monitors this medium-size watershed. A decade of rainfall-runoff event observations are available. Severe rainfall events were identified with reference to a main raingauge station, by using an interevent time definition and a depth threshold. Rainfall depths were thus derived and the spatial variability of their association degree was represented by using the Kendall coefficient. A unique copula model based on Gumbel copula function was finally found to be suitable to represent the dependence structure relating to rainfall depths observed in distinct raingauges. Bardossy A., Pegram G. (2009), Copula based multisite model for daily precipitation simulation, Hydrol. Earth Syst. Sci., 13, 2299-2314. Genest C., Rémilland B., Beaudoin D. (2009), Goodness-of-fit tests for copulas: a review and a power study, Insur. Math. Econ., 44(2), 199-213. Joe H. (1997), Multivariate models and dependence concepts, Chapman and Hall, London. Nelsen R. B. (2006), An introduction to copulas, second ed., Springer, New York. Salvadori G., De Michele C., Kottegoda N. T., Rosso R. (2007), Extremes in nature: an approach using copulas, Springer, Dordrecht, The Nederlands.

Random vectors and spatial analysis by geostatistics for geotechnical applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, D.S.

1987-08-01

Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators; kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics tomore » spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings.« less

Effective 3-D surface modeling for geographic information systems

NASA Astrophysics Data System (ADS)

Yüksek, K.; Alparslan, M.; Mendi, E.

2013-11-01

In this work, we propose a dynamic, flexible and interactive urban digital terrain platform (DTP) with spatial data and query processing capabilities of Geographic Information Systems (GIS), multimedia database functionality and graphical modeling infrastructure. A new data element, called Geo-Node, which stores image, spatial data and 3-D CAD objects is developed using an efficient data structure. The system effectively handles data transfer of Geo-Nodes between main memory and secondary storage with an optimized Directional Replacement Policy (DRP) based buffer management scheme. Polyhedron structures are used in Digital Surface Modeling (DSM) and smoothing process is performed by interpolation. The experimental results show that our framework achieves high performance and works effectively with urban scenes independent from the amount of spatial data and image size. The proposed platform may contribute to the development of various applications such as Web GIS systems based on 3-D graphics standards (e.g. X3-D and VRML) and services which integrate multi-dimensional spatial information and satellite/aerial imagery.

Effective 3-D surface modeling for geographic information systems

NASA Astrophysics Data System (ADS)

Yüksek, K.; Alparslan, M.; Mendi, E.

2016-01-01

In this work, we propose a dynamic, flexible and interactive urban digital terrain platform with spatial data and query processing capabilities of geographic information systems, multimedia database functionality and graphical modeling infrastructure. A new data element, called Geo-Node, which stores image, spatial data and 3-D CAD objects is developed using an efficient data structure. The system effectively handles data transfer of Geo-Nodes between main memory and secondary storage with an optimized directional replacement policy (DRP) based buffer management scheme. Polyhedron structures are used in digital surface modeling and smoothing process is performed by interpolation. The experimental results show that our framework achieves high performance and works effectively with urban scenes independent from the amount of spatial data and image size. The proposed platform may contribute to the development of various applications such as Web GIS systems based on 3-D graphics standards (e.g., X3-D and VRML) and services which integrate multi-dimensional spatial information and satellite/aerial imagery.

[Kriging analysis of vegetation index depression in peak cluster karst area].

PubMed

Yang, Qi-Yong; Jiang, Zhong-Cheng; Ma, Zu-Lu; Cao, Jian-Hua; Luo, Wei-Qun; Li, Wen-Jun; Duan, Xiao-Fang

2012-04-01

In order to master the spatial variability of the normal different vegetation index (NDVI) of the peak cluster karst area, taking into account the problem of the mountain shadow "missing" information of remote sensing images existing in the karst area, NDVI of the non-shaded area were extracted in Guohua Ecological Experimental Area, in Pingguo County, Guangxi applying image processing software, ENVI. The spatial variability of NDVI was analyzed applying geostatistical method, and the NDVI of the mountain shadow areas was predicted and validated. The results indicated that the NDVI of the study area showed strong spatial variability and spatial autocorrelation resulting from the impact of intrinsic factors, and the range was 300 m. The spatial distribution maps of the NDVI interpolated by Kriging interpolation method showed that the mean of NDVI was 0.196, apparently strip and block. The higher NDVI values distributed in the area where the slope was greater than 25 degrees of the peak cluster area, while the lower values distributed in the area such as foot of the peak cluster and depression, where slope was less than 25 degrees. Kriging method validation results show that interpolation has a very high prediction accuracy and could predict the NDVI of the shadow area, which provides a new idea and method for monitoring and evaluation of the karst rocky desertification.

Edge directed image interpolation with Bamberger pyramids

NASA Astrophysics Data System (ADS)

Rosiles, Jose Gerardo

2005-08-01

Image interpolation is a standard feature in digital image editing software, digital camera systems and printers. Classical methods for resizing produce blurred images with unacceptable quality. Bamberger Pyramids and filter banks have been successfully used for texture and image analysis. They provide excellent multiresolution and directional selectivity. In this paper we present an edge-directed image interpolation algorithm which takes advantage of the simultaneous spatial-directional edge localization at the subband level. The proposed algorithm outperform classical schemes like bilinear and bicubic schemes from the visual and numerical point of views.

Geostatistical interpolation of available copper in orchard soil as influenced by planting duration.

PubMed

Fu, Chuancheng; Zhang, Haibo; Tu, Chen; Li, Lianzhen; Luo, Yongming

2018-01-01

Mapping the spatial distribution of available copper (A-Cu) in orchard soils is important in agriculture and environmental management. However, data on the distribution of A-Cu in orchard soils is usually highly variable and severely skewed due to the continuous input of fungicides. In this study, ordinary kriging combined with planting duration (OK_PD) is proposed as a method for improving the interpolation of soil A-Cu. Four normal distribution transformation methods, namely, the Box-Cox, Johnson, rank order, and normal score methods, were utilized prior to interpolation. A total of 317 soil samples were collected in the orchards of the Northeast Jiaodong Peninsula. Moreover, 1472 orchards were investigated to obtain a map of planting duration using Voronoi tessellations. The soil A-Cu content ranged from 0.09 to 106.05 with a mean of 18.10 mg kg -1 , reflecting the high availability of Cu in the soils. Soil A-Cu concentrations exhibited a moderate spatial dependency and increased significantly with increasing planting duration. All the normal transformation methods successfully decreased the skewness and kurtosis of the soil A-Cu and the associated residuals, and also computed more robust variograms. OK_PD could generate better spatial prediction accuracy than ordinary kriging (OK) for all transformation methods tested, and it also provided a more detailed map of soil A-Cu. Normal score transformation produced satisfactory accuracy and showed an advantage in ameliorating smoothing effect derived from the interpolation methods. Thus, normal score transformation prior to kriging combined with planting duration (NSOK_PD) is recommended for the interpolation of soil A-Cu in this area.

Spatial correlation of auroral zone geomagnetic variations

NASA Astrophysics Data System (ADS)

Jackel, B. J.; Davalos, A.

2016-12-01

Magnetic field perturbations in the auroral zone are produced by a combination of distant ionospheric and local ground induced currents. Spatial and temporal structure of these currents is scientifically interesting and can also have a significant influence on critical infrastructure.Ground-based magnetometer networks are an essential tool for studying these phenomena, with the existing complement of instruments in Canada providing extended local time coverage. In this study we examine the spatial correlation between magnetic field observations over a range of scale lengths. Principal component and canonical correlation analysis are used to quantify relationships between multiple sites. Results could be used to optimize network configurations, validate computational models, and improve methods for empirical interpolation.

Suitability of Spatial Interpolation Techniques in Varying Aquifer Systems of a Basaltic Terrain for Monitoring Groundwater Availability

NASA Astrophysics Data System (ADS)

Katpatal, Y. B.; Paranjpe, S. V.; Kadu, M. S.

2017-12-01

Geological formations act as aquifer systems and variability in the hydrological properties of aquifers have control over groundwater occurrence and dynamics. To understand the groundwater availability in any terrain, spatial interpolation techniques are widely used. It has been observed that, with varying hydrogeological conditions, even in a geologically homogenous set up, there are large variations in observed groundwater levels. Hence, the accuracy of groundwater estimation depends on the use of appropriate interpretation techniques. The study area of the present study is Venna Basin of Maharashtra State, India which is a basaltic terrain with four different types of basaltic layers laid down horizontally; weathered vesicular basalt, weathered and fractured basalt, highly weathered unclassified basalt and hard massive basalt. The groundwater levels vary with topography as different types of basalts are present at varying depths. The local stratigraphic profiles were generated at different types of basaltic terrains. The present study aims to interpolate the groundwater levels within the basin and to check the co-relation between the estimated and the observed values. The groundwater levels for 125 observation wells situated in these different basaltic terrains for 20 years (1995 - 2015) have been used in the study. The interpolation was carried out in Geographical Information System (GIS) using ordinary kriging and Inverse Distance Weight (IDW) method. A comparative analysis of the interpolated values of groundwater levels is carried out for validating the recorded groundwater level dataset. The results were co-related to various types of basaltic terrains present in basin forming the aquifer systems. Mean Error (ME) and Mean Square Errors (MSE) have been computed and compared. It was observed that within the interpolated values, a good correlation does not exist between the two interpolation methods used. The study concludes that in crystalline basaltic terrain, interpolation methods must be verified with the changes in the geological profiles.

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

DOE PAGES

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.; ...

2017-09-11

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

Spatio-temporal interpolation of soil moisture in 3D+T using automated sensor network data

NASA Astrophysics Data System (ADS)

Gasch, C.; Hengl, T.; Magney, T. S.; Brown, D. J.; Gräler, B.

2014-12-01

Soil sensor networks provide frequent in situ measurements of dynamic soil properties at fixed locations, producing data in 2- or 3-dimensions and through time (2D+T and 3D+T). Spatio-temporal interpolation of 3D+T point data produces continuous estimates that can then be used for prediction at unsampled times and locations, as input for process models, and can simply aid in visualization of properties through space and time. Regression-kriging with 3D and 2D+T data has successfully been implemented, but currently the field of geostatistics lacks an analytical framework for modeling 3D+T data. Our objective is to develop robust 3D+T models for mapping dynamic soil data that has been collected with high spatial and temporal resolution. For this analysis, we use data collected from a sensor network installed on the R.J. Cook Agronomy Farm (CAF), a 37-ha Long-Term Agro-Ecosystem Research (LTAR) site in Pullman, WA. For five years, the sensors have collected hourly measurements of soil volumetric water content at 42 locations and five depths. The CAF dataset also includes a digital elevation model and derivatives, a soil unit description map, crop rotations, electromagnetic induction surveys, daily meteorological data, and seasonal satellite imagery. The soil-water sensor data, combined with the spatial and temporal covariates, provide an ideal dataset for developing 3D+T models. The presentation will include preliminary results and address main implementation strategies.

MAGIC: A Tool for Combining, Interpolating, and Processing Magnetograms

NASA Technical Reports Server (NTRS)

Allred, Joel

2012-01-01

Transients in the solar coronal magnetic field are ultimately the source of space weather. Models which seek to track the evolution of the coronal field require magnetogram images to be used as boundary conditions. These magnetograms are obtained by numerous instruments with different cadences and resolutions. A tool is required which allows modelers to fmd all available data and use them to craft accurate and physically consistent boundary conditions for their models. We have developed a software tool, MAGIC (MAGnetogram Interpolation and Composition), to perform exactly this function. MAGIC can manage the acquisition of magneto gram data, cast it into a source-independent format, and then perform the necessary spatial and temporal interpolation to provide magnetic field values as requested onto model-defined grids. MAGIC has the ability to patch magneto grams from different sources together providing a more complete picture of the Sun's field than is possible from single magneto grams. In doing this, care must be taken so as not to introduce nonphysical current densities along the seam between magnetograms. We have designed a method which minimizes these spurious current densities. MAGIC also includes a number of post-processing tools which can provide additional information to models. For example, MAGIC includes an interface to the DA VE4VM tool which derives surface flow velocities from the time evolution of surface magnetic field. MAGIC has been developed as an application of the KAMELEON data formatting toolkit which has been developed by the CCMC.

Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

2016-12-01

The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In order to assess the value of different data sources, reforecasts will be produced for a historical period and performance measures will be computed to assess forecast skill. The existing CBRFC Ensemble Streamflow Prediction (ESP) reforecasts will provide a baseline for comparison to determine the added-value of the data assimilation process.

Spatial Resolution Requirements for Traffic-Related Air Pollutant Exposure Evaluations

PubMed Central

Batterman, Stuart; Chambliss, Sarah; Isakov, Vlad

2014-01-01

Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification. PMID:25132794

Quantifying Libya-4 Surface Reflectance Heterogeneity With WorldView-1, 2 and EO-1 Hyperion

NASA Technical Reports Server (NTRS)

Neigh, Christopher S. R.; McCorkel, Joel; Middleton, Elizabeth M.

2015-01-01

The land surface imaging (LSI) virtual constellation approach promotes the concept of increasing Earth observations from multiple but disparate satellites. We evaluated this through spectral and spatial domains, by comparing surface reflectance from 30-m Hyperion and 2-m resolution WorldView-2 (WV-2) data in the Libya-4 pseudoinvariant calibration site. We convolved and resampled Hyperion to WV-2 bands using both cubic convolution and nearest neighbor (NN) interpolation. Additionally, WV-2 and WV-1 same-date imagery were processed as a cross-track stereo pair to generate a digital terrain model to evaluate the effects from large (>70 m) linear dunes. Agreement was moderate to low on dune peaks between WV-2 and Hyperion (R2 <; 0.4) but higher in areas of lower elevation and slope (R2 > 0.6). Our results provide a satellite sensor intercomparison protocol for an LSI virtual constellation at high spatial resolution, which should start with geolocation of pixels, followed by NN interpolation to avoid tall dunes that enhance surface reflectance differences across this internationally utilized site.

Space-time interpolation of satellite winds in the tropics

NASA Astrophysics Data System (ADS)

Patoux, Jérôme; Levy, Gad

2013-09-01

A space-time interpolator for creating average geophysical fields from satellite measurements is presented and tested. It is designed for optimal spatiotemporal averaging of heterogeneous data. While it is illustrated with satellite surface wind measurements in the tropics, the methodology can be useful for interpolating, analyzing, and merging a wide variety of heterogeneous and satellite data in the atmosphere and ocean over the entire globe. The spatial and temporal ranges of the interpolator are determined by averaging satellite and in situ measurements over increasingly larger space and time windows and matching the corresponding variability at each scale. This matching provides a relationship between temporal and spatial ranges, but does not provide a unique pair of ranges as a solution to all averaging problems. The pair of ranges most appropriate for a given application can be determined by performing a spectral analysis of the interpolated fields and choosing the smallest values that remove any or most of the aliasing due to the uneven sampling by the satellite. The methodology is illustrated with the computation of average divergence fields over the equatorial Pacific Ocean from SeaWinds-on-QuikSCAT surface wind measurements, for which 72 h and 510 km are suggested as optimal interpolation windows. It is found that the wind variability is reduced over the cold tongue and enhanced over the Pacific warm pool, consistent with the notion that the unstably stratified boundary layer has generally more variable winds and more gustiness than the stably stratified boundary layer. It is suggested that the spectral analysis optimization can be used for any process where time-space correspondence can be assumed.

DEFINITION OF MULTIVARIATE GEOCHEMICAL ASSOCIATIONS WITH POLYMETALLIC MINERAL OCCURRENCES USING A SPATIALLY DEPENDENT CLUSTERING TECHNIQUE AND RASTERIZED STREAM SEDIMENT DATA - AN ALASKAN EXAMPLE.

USGS Publications Warehouse

Jenson, Susan K.; Trautwein, C.M.

1984-01-01

The application of an unsupervised, spatially dependent clustering technique (AMOEBA) to interpolated raster arrays of stream sediment data has been found to provide useful multivariate geochemical associations for modeling regional polymetallic resource potential. The technique is based on three assumptions regarding the compositional and spatial relationships of stream sediment data and their regional significance. These assumptions are: (1) compositionally separable classes exist and can be statistically distinguished; (2) the classification of multivariate data should minimize the pair probability of misclustering to establish useful compositional associations; and (3) a compositionally defined class represented by three or more contiguous cells within an array is a more important descriptor of a terrane than a class represented by spatial outliers.

Rtop - an R package for interpolation along the stream network

NASA Astrophysics Data System (ADS)

Skøien, J. O.

2009-04-01

Rtop - an R package for interpolation along the stream network Geostatistical methods have been used to a limited extent for estimation along stream networks, with a few exceptions(Gottschalk, 1993; Gottschalk, et al., 2006; Sauquet, et al., 2000; Skøien, et al., 2006). Interpolation of runoff characteristics are more complicated than the traditional random variables estimated by geostatistical methods, as the measurements have a more complicated support, and many catchments are nested. Skøien et al. (2006) presented the model Top-kriging which takes these effects into account for interpolation of stream flow characteristics (exemplified by the 100 year flood). The method has here been implemented as a package in the statistical environment R (R Development Core Team, 2004). Taking advantage of the existing methods in R for working with spatial objects, and the extensive possibilities for visualizing the result, this makes it considerably easier to apply the method on new data sets, in comparison to earlier implementation of the method. Gottschalk, L. 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., I. Krasovskaia, E. Leblois, and E. Sauquet. 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. R Development Core Team. 2004. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Sauquet, E., L. Gottschalk, and E. Leblois. 2000. Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J. O., R. Merz, and G. Blöschl. 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

Locally adaptive, spatially explicit projection of US population for 2030 and 2050.

PubMed

McKee, Jacob J; Rose, Amy N; Bright, Edward A; Huynh, Timmy; Bhaduri, Budhendra L

2015-02-03

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census's projection methodology, with the US Census's official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

Simulation of Pellet Ablation

NASA Astrophysics Data System (ADS)

Parks, P. B.; Ishizaki, Ryuichi

2000-10-01

In order to clarify the structure of the ablation flow, 2D simulation is carried out with a fluid code solving temporal evolution of MHD equations. The code includes electrostatic sheath effect at the cloud interface.(P.B. Parks et al.), Plasma Phys. Contr. Fusion 38, 571 (1996). An Eulerian cylindrical coordinate system (r,z) is used with z in a spherical pellet. The code uses the Cubic-Interpolated Psudoparticle (CIP) method(H. Takewaki and T. Yabe, J. Comput. Phys. 70), 355 (1987). that divides the fluid equations into non-advection and advection phases. The most essential element of the CIP method is in calculation of the advection phase. In this phase, a cubic interpolated spatial profile is shifted in space according to the total derivative equations, similarly to a particle scheme. Since the profile is interpolated by using the value and the spatial derivative value at each grid point, there is no numerical oscillation in space, that often appears in conventional spline interpolation. A free boundary condition is used in the code. The possibility of a stationary shock will also be shown in the presentation because the supersonic ablation flow across the magnetic field is impeded.

The Mars Climate Database (MCD version 5.2)

NASA Astrophysics Data System (ADS)

Millour, E.; Forget, F.; Spiga, A.; Navarro, T.; Madeleine, J.-B.; Montabone, L.; Pottier, A.; Lefevre, F.; Montmessin, F.; Chaufray, J.-Y.; Lopez-Valverde, M. A.; Gonzalez-Galindo, F.; Lewis, S. R.; Read, P. L.; Huot, J.-P.; Desjean, M.-C.; MCD/GCM development Team

2015-10-01

The Mars Climate Database (MCD) is a database of meteorological fields derived from General Circulation Model (GCM) numerical simulations of the Martian atmosphere and validated using available observational data. The MCD includes complementary post-processing schemes such as high spatial resolution interpolation of environmental data and means of reconstructing the variability thereof. We have just completed (March 2015) the generation of a new version of the MCD, MCD version 5.2

Quantifying measurement uncertainty and spatial variability in the context of model evaluation

NASA Astrophysics Data System (ADS)

Choukulkar, A.; Brewer, A.; Pichugina, Y. L.; Bonin, T.; Banta, R. M.; Sandberg, S.; Weickmann, A. M.; Djalalova, I.; McCaffrey, K.; Bianco, L.; Wilczak, J. M.; Newman, J. F.; Draxl, C.; Lundquist, J. K.; Wharton, S.; Olson, J.; Kenyon, J.; Marquis, M.

2017-12-01

In an effort to improve wind forecasts for the wind energy sector, the Department of Energy and the NOAA funded the second Wind Forecast Improvement Project (WFIP2). As part of the WFIP2 field campaign, a large suite of in-situ and remote sensing instrumentation was deployed to the Columbia River Gorge in Oregon and Washington from October 2015 - March 2017. The array of instrumentation deployed included 915-MHz wind profiling radars, sodars, wind- profiling lidars, and scanning lidars. The role of these instruments was to provide wind measurements at high spatial and temporal resolution for model evaluation and improvement of model physics. To properly determine model errors, the uncertainties in instrument-model comparisons need to be quantified accurately. These uncertainties arise from several factors such as measurement uncertainty, spatial variability, and interpolation of model output to instrument locations, to name a few. In this presentation, we will introduce a formalism to quantify measurement uncertainty and spatial variability. The accuracy of this formalism will be tested using existing datasets such as the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign. Finally, the uncertainties in wind measurement and the spatial variability estimates from the WFIP2 field campaign will be discussed to understand the challenges involved in model evaluation.

Evaluation of a spatial rainfall generator and an interpolation methods for the creation of future gridded data sets over complex terrains

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Michaelides, Silas; Lange, Manfred A.

2015-04-01

Space-time variability of precipitation plays a key role as a driver of many processes in different environmental fields like hydrology, ecology, biology, agriculture, and natural hazards. The objective of this study was to compare two approaches for statistical downscaling of precipitation from climate models. The study was applied to the island of Cyprus, an orographically complex terrain. The first approach makes use of a spatial temporal Neyman-Scott Rectangular Pulses (NSRP) model and a previously tested interpolation scheme (Camera et al., 2014). The second approach is based on the use of the single site NSRP model and a simplified gridded scheme based on scaling coefficients obtained from past observations. The rainfall generators were evaluated on the period 1980-2010. Both approaches were subsequently used to downscale three RCMs from the EU ENSEMBLE project to calculate climate projections (2020-2050). The main advantage of the spatial-temporal approach is that it allows creating spatially consistent daily maps of precipitation. On the other hand, due to the assumptions made using a stochastic generator based on homogeneous Poisson processes, it shows a smoothing out of all the rainfall statistics (except mean and variance) all over the study area. This leads to high errors when analyzing indices related to extremes. Examples are the number of days with rainfall over 50 mm (R50 - mean error 65%), the 95th percentile value of rainy days (RT95 - mean error 19%), and the mean annual rainfall recorded on days with rainfall above the 95th percentile (RA95 - mean error 22%). The single site approach excludes the possibility of using the created gridded data sets for case studies involving spatial connection between grid cells (e.g. hydrologic modelling), but it leads to a better reproduction of rainfall statistics and properties. The errors for the extreme indices are in fact much lower: 17% for R50, 4% for RT95, and 2% for RA95. Future projections show a decrease of the mean annual rainfall (for both approaches) over the study area between 70 mm (≈15%) and 5 mm (≈1%), in comparison to the reference period 1980-2010. Regarding extremes, calculated only with the single site approach, the projections show a decrease of the R50 index between 25% and 7%, and of the RT95 between 8% and 0%. Thus, these projections indicate that a slight reduction in the number and intensity of extremes can be expected. Further research will be done to adapt and evaluate the use of a spatial-temporal generator with nonhomogeneous spatial activation of raincells (Burton et al., 2010) to the study area. Burton, A., Fowler, H.J., Kilsby, C.G., O'Connell, P. E., 2010a. A stochastic model for the spatial-temporal simulation of non-homogeneous rainfall occurrence and amounts, Water Resour. Res. 46, W11501. DOI: 10.1029/2009WR008884 Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., Lange, M. A., 2014. Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010. J. Geophys. Res. Atmos., 119, 693-712. DOI: 10.1002/2013JD020611.

A Lagrangian particle model to predict the airborne spread of foot-and-mouth disease virus

NASA Astrophysics Data System (ADS)

Mayer, D.; Reiczigel, J.; Rubel, F.

Airborne spread of bioaerosols in the boundary layer over a complex terrain is simulated using a Lagrangian particle model, and applied to modelling the airborne spread of foot-and-mouth disease (FMD) virus. Two case studies are made with study domains located in a hilly region in the northwest of the Styrian capital Graz, the second largest town in Austria. Mountainous terrain as well as inhomogeneous and time varying meteorological conditions prevent from application of so far used Gaussian dispersion models, while the proposed model can handle these realistically. In the model, trajectories of several thousands of particles are computed and the distribution of virus concentration near the ground is calculated. This allows to assess risk of infection areas with respect to animal species of interest, such as cattle, swine or sheep. Meteorological input data like wind field and other variables necessary to compute turbulence were taken from the new pre-operational version of the non-hydrostatic numerical weather prediction model LMK ( Lokal-Modell-Kürzestfrist) running at the German weather service DWD ( Deutscher Wetterdienst). The LMK model provides meteorological parameters with a spatial resolution of about 2.8 km. To account for the spatial resolution of 400 m used by the Lagrangian particle model, the initial wind field is interpolated upon the finer grid by a mass consistent interpolation method. Case studies depict a significant influence of local wind systems on the spread of virus. Higher virus concentrations at the upwind side of the hills and marginal concentrations in the lee are well observable, as well as canalization effects by valleys. The study demonstrates that the Lagrangian particle model is an appropriate tool for risk assessment of airborne spread of virus by taking into account the realistic orographic and meteorological conditions.

Spatial patterns of high Aedes aegypti oviposition activity in northwestern Argentina.

PubMed

Estallo, Elizabet Lilia; Más, Guillermo; Vergara-Cid, Carolina; Lanfri, Mario Alberto; Ludueña-Almeida, Francisco; Scavuzzo, Carlos Marcelo; Introini, María Virginia; Zaidenberg, Mario; Almirón, Walter Ricardo

2013-01-01

In Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively. Oviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005-2007). Spatial autocorrelation was measured with Moran's Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity. Both cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC=0.77), obtaining 99% of sensitivity and 75.29% of specificity. Distance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively.

Estimating changes in urban land and urban population using refined areal interpolation techniques

NASA Astrophysics Data System (ADS)

Zoraghein, Hamidreza; Leyk, Stefan

2018-05-01

The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Target Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combinations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead, it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.

Changing Urbania: Estimating Changes in Urban Land and Urban Population Using Refined Areal Interpolation Techniques

NASA Astrophysics Data System (ADS)

Zoraghein, H.; Leyk, S.; Balk, D.

2017-12-01

The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. The U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Target Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combinations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.

High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya.

PubMed

Jia, Peng; Anderson, John D; Leitner, Michael; Rheingans, Richard

2016-01-01

Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals. The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation. The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing. There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.

Kriging - a challenge in geochemical mapping

NASA Astrophysics Data System (ADS)

Stojdl, Jiri; Matys Grygar, Tomas; Elznicova, Jitka; Popelka, Jan; Vachova, Tatina; Hosek, Michal

2017-04-01

Geochemists can easily provide datasets for contamination mapping thanks to recent advances in geographical information systems (GIS) and portable chemical-analytical instrumentation. Kriging is commonly used to visualise the results of such mapping. It is understandable, as kriging is a well-established method of spatial interpolation. It was created in 1950's for geochemical data processing to estimate the most likely distribution of gold based on samples from a few boreholes. However, kriging is based on the assumption of continuous spatial distribution of numeric data that is not realistic in environmental geochemistry. The use of kriging is correct when the data density is sufficient with respect to heterogeneity of the spatial distribution of the geochemical parameters. However, if anomalous geochemical values are focused in hotspots of which boundaries are insufficiently densely sampled, kriging could provide misleading maps with the real contours of hotspots blurred by data smoothing and levelling out individual (isolated) but relevant anomalous values. The data smoothing can thus it results in underestimation of geochemical extremes, which may in fact be of the greatest importance in mapping projects. In our study we characterised hotspots of contamination by uranium and zinc in the floodplain of the Ploučnice River. The first objective of our study was to compare three methods of sampling: random (based on stochastic generation of sampling points), systematic (square grid) and judgemental sampling (based on judgement stemming from principles of fluvial deposition) as the basis for pollution maps. The first detected problem in production of the maps was the reduction of the smoothing effect of kriging using appropriate function of empirical semivariogram and setting the variation of at microscales smaller than the sampling distances to minimum (the "nugget" parameter of semivariogram). Exact interpolators such as Inverse Distance Weighting (IDW) or Radial Basis Functions (RBF) provides better solutions in this respect. The second detected problem was heterogeneous structure of the floodplain: it consists of distinct sedimentary bodies (e.g., natural levees, meander scars, point bars), which have been formed by different process (erosion or deposition on flooding, channel shifts by meandering, channel abandonment). Interpolation through these sedimentary bodies has thus not much sense. Solution is to identify boundaries between sedimentary bodies and interpolation of data with this additional information using exact interpolators with barriers (IDW, RBF or stratified kriging) or regression kriging. Those boundaries can be identified using, e.g., digital elevation model (DEM), dipole electromagnetic profiling (DEMP), gamma spectrometry, or an expertise by a geomorphologist.

Assessment of spatial distribution of fallout radionuclides through geostatistics concept.

PubMed

Mabit, L; Bernard, C

2007-01-01

After introducing geostatistics concept and its utility in environmental science and especially in Fallout Radionuclide (FRN) spatialisation, a case study for cesium-137 ((137)Cs) redistribution at the field scale using geostatistics is presented. On a Canadian agricultural field, geostatistics coupled with a Geographic Information System (GIS) was used to test three different techniques of interpolation [Ordinary Kriging (OK), Inverse Distance Weighting power one (IDW1) and two (IDW2)] to create a (137)Cs map and to establish a radioisotope budget. Following the optimization of variographic parameters, an experimental semivariogram was developed to determine the spatial dependence of (137)Cs. It was adjusted to a spherical isotropic model with a range of 30 m and a very small nugget effect. This (137)Cs semivariogram showed a good autocorrelation (R(2)=0.91) and was well structured ('nugget-to-sill' ratio of 4%). It also revealed that the sampling strategy was adequate to reveal the spatial correlation of (137)Cs. The spatial redistribution of (137)Cs was estimated by Ordinary Kriging and IDW to produce contour maps. A radioisotope budget was established for the 2.16 ha agricultural field under investigation. It was estimated that around 2 x 10(7)Bq of (137)Cs were missing (around 30% of the total initial fallout) and were exported by physical processes (runoff and erosion processes) from the area under investigation. The cross-validation analysis showed that in the case of spatially structured data, OK is a better interpolation method than IDW1 or IDW2 for the assessment of potential radioactive contamination and/or pollution.

Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

Spatial interpolation and simulation of post-burn duff thickness after prescribed fire

Treesearch

Peter R. Robichaud; S. M. Miller

1999-01-01

Prescribed fire is used as a site treatment after timber harvesting. These fires result in spatial patterns with some portions consuming all of the forest floor material (duff) and others consuming little. Prior to the burn, spatial sampling of duff thickness and duff water content can be used to generate geostatistical spatial simulations of these characteristics....

On the paradoxical evolution of the number of photons in a new model of interpolating Hamiltonians

NASA Astrophysics Data System (ADS)

Valverde, Clodoaldo; Baseia, Basílio

2018-01-01

We introduce a new Hamiltonian model which interpolates between the Jaynes-Cummings model (JCM) and other types of such Hamiltonians. It works with two interpolating parameters, rather than one as traditional. Taking advantage of this greater degree of freedom, we can perform continuous interpolation between the various types of these Hamiltonians. As applications, we discuss a paradox raised in literature and compare the time evolution of the photon statistics obtained in the various interpolating models. The role played by the average excitation in these comparisons is also highlighted.

Spatiotemporal Interpolation of Elevation Changes Derived from Satellite Altimetry for Jakobshavn Isbrae, Greenland

NASA Technical Reports Server (NTRS)

Hurkmans, R.T.W.L.; Bamber, J.L.; Sorensen, L. S.; Joughin, I. R.; Davis, C. H.; Krabill, W. B.

2012-01-01

Estimation of ice sheet mass balance from satellite altimetry requires interpolation of point-scale elevation change (dHdt) data over the area of interest. The largest dHdt values occur over narrow, fast-flowing outlet glaciers, where data coverage of current satellite altimetry is poorest. In those areas, straightforward interpolation of data is unlikely to reflect the true patterns of dHdt. Here, four interpolation methods are compared and evaluated over Jakobshavn Isbr, an outlet glacier for which widespread airborne validation data are available from NASAs Airborne Topographic Mapper (ATM). The four methods are ordinary kriging (OK), kriging with external drift (KED), where the spatial pattern of surface velocity is used as a proxy for that of dHdt, and their spatiotemporal equivalents (ST-OK and ST-KED).

Spatially Detailed Porosity Prediction From Airborne Electromagnetics and Sparse Borehole Fluid Sampling

NASA Astrophysics Data System (ADS)

Macnae, J.; Ley-Cooper, Y.

2009-05-01

Sub-surface porosity is of importance in estimating fluid contant and salt-load parameters for hydrological modelling. While sparse boreholes may adequately sample the depth to a sub-horizontal water-table and usually also adequately sample ground-water salinity, they do not provide adequate sampling of the spatial variations in porosity or hydraulic permeability caused by spatial variations in sedimentary and other geological processes.. We show in this presentation that spatially detailed porosity can be estimated by applying Archie's law to conductivity estimates from airborne electromagnetic surveys with interpolated ground-water conductivity values. The prediction was tested on data from the Chowilla flood plain in the Murray-Darling Basin of South Australia. A frequency domain, helicopter-borne electromagnetic system collected data at 6 frequencies and 3 to 4 m spacings on lines spaced 100 m apart. This data was transformed into conductivity-depth sections, from which a 3D bulk-conductivity map could be created with about 30 m spatial resolution and 2 to 5 m vertical depth resolution. For that portion of the volume below the interpolated water-table, we predicted porosity in each cell using Archie's law. Generally, predicted porosities were in the 30 to 50 % range, consistent with expectations for the partially consolidated sediments in the floodplain. Porosities were directly measured on core from eight boreholes in the area, and compared quite well with the predictions. The predicted porosity map was spatially consistent, and when combined with measured salinities in the ground water, was able to provide a detailed 3D map of salt-loads in the saturated zone, and as such contribute to a hazard assessment of the saline threat to the river.

Using geostatistical methods to estimate snow water equivalence distribution in a mountain watershed

USGS Publications Warehouse

Balk, B.; Elder, K.; Baron, Jill S.

1998-01-01

Knowledge of the spatial distribution of snow water equivalence (SWE) is necessary to adequately forecast the volume and timing of snowmelt runoff.  In April 1997, peak accumulation snow depth and density measurements were independently taken in the Loch Vale watershed (6.6 km2), Rocky Mountain National Park, Colorado.  Geostatistics and classical statistics were used to estimate SWE distribution across the watershed.  Snow depths were spatially distributed across the watershed through kriging interpolation methods which provide unbiased estimates that have minimum variances.  Snow densities were spatially modeled through regression analysis.  Combining the modeled depth and density with snow-covered area (SCA produced an estimate of the spatial distribution of SWE.  The kriged estimates of snow depth explained 37-68% of the observed variance in the measured depths.  Steep slopes, variably strong winds, and complex energy balance in the watershed contribute to a large degree of heterogeneity in snow depth.

The concentration dependence of the galaxy–halo connection: Modeling assembly bias with abundance matching

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lehmann, Benjamin V.; Mao, Yao -Yuan; Becker, Matthew R.

Empirical methods for connecting galaxies to their dark matter halos have become essential for interpreting measurements of the spatial statistics of galaxies. In this work, we present a novel approach for parameterizing the degree of concentration dependence in the abundance matching method. Furthermore, this new parameterization provides a smooth interpolation between two commonly used matching proxies: the peak halo mass and the peak halo maximal circular velocity. This parameterization controls the amount of dependence of galaxy luminosity on halo concentration at a fixed halo mass. Effectively this interpolation scheme enables abundance matching models to have adjustable assembly bias in the resulting galaxy catalogs. With the newmore » $$400\\,\\mathrm{Mpc}\\,{h}^{-1}$$ DarkSky Simulation, whose larger volume provides lower sample variance, we further show that low-redshift two-point clustering and satellite fraction measurements from SDSS can already provide a joint constraint on this concentration dependence and the scatter within the abundance matching framework.« less

The concentration dependence of the galaxy–halo connection: Modeling assembly bias with abundance matching

DOE PAGES

Lehmann, Benjamin V.; Mao, Yao -Yuan; Becker, Matthew R.; ...

2016-12-28

Empirical methods for connecting galaxies to their dark matter halos have become essential for interpreting measurements of the spatial statistics of galaxies. In this work, we present a novel approach for parameterizing the degree of concentration dependence in the abundance matching method. Furthermore, this new parameterization provides a smooth interpolation between two commonly used matching proxies: the peak halo mass and the peak halo maximal circular velocity. This parameterization controls the amount of dependence of galaxy luminosity on halo concentration at a fixed halo mass. Effectively this interpolation scheme enables abundance matching models to have adjustable assembly bias in the resulting galaxy catalogs. With the newmore » $$400\\,\\mathrm{Mpc}\\,{h}^{-1}$$ DarkSky Simulation, whose larger volume provides lower sample variance, we further show that low-redshift two-point clustering and satellite fraction measurements from SDSS can already provide a joint constraint on this concentration dependence and the scatter within the abundance matching framework.« less

Application of validation data for assessing spatial interpolation methods for 8-h ozone or other sparsely monitored constituents.

PubMed

Joseph, John; Sharif, Hatim O; Sunil, Thankam; Alamgir, Hasanat

2013-07-01

The adverse health effects of high concentrations of ground-level ozone are well-known, but estimating exposure is difficult due to the sparseness of urban monitoring networks. This sparseness discourages the reservation of a portion of the monitoring stations for validation of interpolation techniques precisely when the risk of overfitting is greatest. In this study, we test a variety of simple spatial interpolation techniques for 8-h ozone with thousands of randomly selected subsets of data from two urban areas with monitoring stations sufficiently numerous to allow for true validation. Results indicate that ordinary kriging with only the range parameter calibrated in an exponential variogram is the generally superior method, and yields reliable confidence intervals. Sparse data sets may contain sufficient information for calibration of the range parameter even if the Moran I p-value is close to unity. R script is made available to apply the methodology to other sparsely monitored constituents. Copyright © 2013 Elsevier Ltd. All rights reserved.

Automatic elastic image registration by interpolation of 3D rotations and translations from discrete rigid-body transformations.

PubMed

Walimbe, Vivek; Shekhar, Raj

2006-12-01

We present an algorithm for automatic elastic registration of three-dimensional (3D) medical images. Our algorithm initially recovers the global spatial mismatch between the reference and floating images, followed by hierarchical octree-based subdivision of the reference image and independent registration of the floating image with the individual subvolumes of the reference image at each hierarchical level. Global as well as local registrations use the six-parameter full rigid-body transformation model and are based on maximization of normalized mutual information (NMI). To ensure robustness of the subvolume registration with low voxel counts, we calculate NMI using a combination of current and prior mutual histograms. To generate a smooth deformation field, we perform direct interpolation of six-parameter rigid-body subvolume transformations obtained at the last subdivision level. Our interpolation scheme involves scalar interpolation of the 3D translations and quaternion interpolation of the 3D rotational pose. We analyzed the performance of our algorithm through experiments involving registration of synthetically deformed computed tomography (CT) images. Our algorithm is general and can be applied to image pairs of any two modalities of most organs. We have demonstrated successful registration of clinical whole-body CT and positron emission tomography (PET) images using this algorithm. The registration accuracy for this application was evaluated, based on validation using expert-identified anatomical landmarks in 15 CT-PET image pairs. The algorithm's performance was comparable to the average accuracy observed for three expert-determined registrations in the same 15 image pairs.

Spatial characteristics of observed precipitation fields: A catalog of summer storms in Arizona, Volume 2

NASA Technical Reports Server (NTRS)

Fennessey, N. M.; Eagleson, P. S.; Qinliang, W.; Rodriguez-Iturbe, I.

1986-01-01

The parameters of the conceptual model are evaluated from the analysis of eight years of summer rainstorm data from the dense raingage network in the Walnut Gulch catchment near Tucson, Arizona. The occurrence of measurable rain at any one of the 93 gages during a noon to noon day defined a storm. The total rainfall at each of the gages during a storm day constituted the data set for a single storm. The data are interpolated onto a fine grid and analyzed to obtain: an isohyetal plot at 2 mm intervals, the first three moments of point storm depth, the spatial correlation function, the spatial variance function, and the spatial distribution of the total storm depth. The description of the data analysis and the computer programs necessary to read the associated data tapes are presented.

Gridded precipitation fields at high temporal and spatial resolution for operational flood forecasting in the Rhine basin

NASA Astrophysics Data System (ADS)

van Osnabrugge, Bart; Weerts, Albrecht; Uijlenhoet, Remko

2017-04-01

Gridded areal precipitation, as one of the most important hydrometeorological input variables for initial state estimation in operational hydrological forecasting, is available in the form of raster data sets (e.g. HYRAS and EOBS) for the River Rhine basin. These datasets are compiled off-line on a daily time step using station data with the highest possible spatial density. However, such a product is not available operationally and at an hourly discretisation. Therefore, we constructed an hourly gridded precipitation dataset at 1.44 km2 resolution for the Rhine basin for the period from 1998 to present using a REGNIE-like interpolation procedure (Weerts et al., 2008) using a low and a high density rain gauge network. The datasets were validated against daily HYRAS (Rauthe, 2013) and EOBS (Haylock, 2008) data. The main goal of the operational procedure is to emulate the HYRAS dataset as good as possible, as the daily HYRAS dataset is used in the off-line calibration of the hydrological model. Our main findings are that even with low station density, the spatial patterns found in the HYRAS data set are well reproduced. With low station density (years 1999-2006) our dataset underestimates precipitation compared to HYRAS and EOBS, notably during the winter. However, interpolation based on the same set of stations overestimates precipitation compared to EOBS for the years 2006-2014. This discrepancy disappears when switching to the high station density. We also analyze the robustness of the hourly precipitation fields by comparing with stations not used during interpolation. Specific issues regarding the data when creating the gridded precipitation fields will be highlighted. Finally, the datasets are used to drive an hourly and daily gridded WFLOW_HBV model of the Rhine at the same spatial resolution. Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201 Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., Gratzki, A. 2013: A Central European precipitation climatology - Part 1: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift, 22(3), 235 256 Weerts, A.H., D. Meißner, and S. Rademacher, 2008. Input data rainfall-runoff model operational system FEWS-NL & FEWS-DE. Technical report, Deltares.

New developments in spatial interpolation methods of Sea-Level Anomalies in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Troupin, Charles; Barth, Alexander; Beckers, Jean-Marie; Pascual, Ananda

2014-05-01

The gridding of along-track Sea-Level Anomalies (SLA) measured by a constellation of satellites has numerous applications in oceanography, such as model validation, data assimilation or eddy tracking. Optimal Interpolation (OI) is often the preferred method for this task, as it leads to the lowest expected error and provides an error field associated to the analysed field. However, the numerical cost of the method may limit its utilization in situations where the number of data points is significant. Furthermore, the separation of non-adjacent regions with OI requires adaptation of the code, leading to a further increase of the numerical cost. To solve these issues, the Data-Interpolating Variational Analysis (DIVA), a technique designed to produce gridded from sparse in situ measurements, is applied on SLA data in the Mediterranean Sea. DIVA and OI have been shown to be equivalent (provided some assumptions on the covariances are made). The main difference lies in the covariance function, which is not explicitly formulated in DIVA. The particular spatial and temporal distributions of measurements required adaptation in the Software tool (data format, parameter determinations, ...). These adaptation are presented in the poster. The daily analysed and error fields obtained with this technique are compared with available products such as the gridded field from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) data server. The comparison reveals an overall good agreement between the products. The time evolution of the mean error field evidences the need of a large number of simultaneous altimetry satellites: in period during which 4 satellites are available, the mean error is on the order of 17.5%, while when only 2 satellites are available, the error exceeds 25%. Finally, we propose the use sea currents to improve the results of the interpolation, especially in the coastal area. These currents can be constructed from the bathymetry or extracted from a HF radar located in the Balearic Sea.

High Performance Geostatistical Modeling of Biospheric Resources

NASA Astrophysics Data System (ADS)

Pedelty, J. A.; Morisette, J. T.; Smith, J. A.; Schnase, J. L.; Crosier, C. S.; Stohlgren, T. J.

2004-12-01

We are using parallel geostatistical codes to study spatial relationships among biospheric resources in several study areas. For example, spatial statistical models based on large- and small-scale variability have been used to predict species richness of both native and exotic plants (hot spots of diversity) and patterns of exotic plant invasion. However, broader use of geostastics in natural resource modeling, especially at regional and national scales, has been limited due to the large computing requirements of these applications. To address this problem, we implemented parallel versions of the kriging spatial interpolation algorithm. The first uses the Message Passing Interface (MPI) in a master/slave paradigm on an open source Linux Beowulf cluster, while the second is implemented with the new proprietary Xgrid distributed processing system on an Xserve G5 cluster from Apple Computer, Inc. These techniques are proving effective and provide the basis for a national decision support capability for invasive species management that is being jointly developed by NASA and the US Geological Survey.

Exploring the Role of Genetic Algorithms and Artificial Neural Networks for Interpolation of Elevation in Geoinformation Models

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadjadi, S. Y.; Sadeghian, S.

2013-09-01

One of the most significant tools to study many engineering projects is three-dimensional modelling of the Earth that has many applications in the Geospatial Information System (GIS), e.g. creating Digital Train Modelling (DTM). DTM has numerous applications in the fields of sciences, engineering, design and various project administrations. One of the most significant events in DTM technique is the interpolation of elevation to create a continuous surface. There are several methods for interpolation, which have shown many results due to the environmental conditions and input data. The usual methods of interpolation used in this study along with Genetic Algorithms (GA) have been optimised and consisting of polynomials and the Inverse Distance Weighting (IDW) method. In this paper, the Artificial Intelligent (AI) techniques such as GA and Neural Networks (NN) are used on the samples to optimise the interpolation methods and production of Digital Elevation Model (DEM). The aim of entire interpolation methods is to evaluate the accuracy of interpolation methods. Universal interpolation occurs in the entire neighbouring regions can be suggested for larger regions, which can be divided into smaller regions. The results obtained from applying GA and ANN individually, will be compared with the typical method of interpolation for creation of elevations. The resulting had performed that AI methods have a high potential in the interpolation of elevations. Using artificial networks algorithms for the interpolation and optimisation based on the IDW method with GA could be estimated the high precise elevations.

An interpolation method for stream habitat assessments

USGS Publications Warehouse

Sheehan, Kenneth R.; Welsh, Stuart A.

2015-01-01

Interpolation of stream habitat can be very useful for habitat assessment. Using a small number of habitat samples to predict the habitat of larger areas can reduce time and labor costs as long as it provides accurate estimates of habitat. The spatial correlation of stream habitat variables such as substrate and depth improves the accuracy of interpolated data. Several geographical information system interpolation methods (natural neighbor, inverse distance weighted, ordinary kriging, spline, and universal kriging) were used to predict substrate and depth within a 210.7-m2 section of a second-order stream based on 2.5% and 5.0% sampling of the total area. Depth and substrate were recorded for the entire study site and compared with the interpolated values to determine the accuracy of the predictions. In all instances, the 5% interpolations were more accurate for both depth and substrate than the 2.5% interpolations, which achieved accuracies up to 95% and 92%, respectively. Interpolations of depth based on 2.5% sampling attained accuracies of 49–92%, whereas those based on 5% percent sampling attained accuracies of 57–95%. Natural neighbor interpolation was more accurate than that using the inverse distance weighted, ordinary kriging, spline, and universal kriging approaches. Our findings demonstrate the effective use of minimal amounts of small-scale data for the interpolation of habitat over large areas of a stream channel. Use of this method will provide time and cost savings in the assessment of large sections of rivers as well as functional maps to aid the habitat-based management of aquatic species.

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

NASA Astrophysics Data System (ADS)

Lussana, Cristian; Saloranta, Tuomo; Skaugen, Thomas; Magnusson, Jan; Tveito, Ole Einar; Andersen, Jess

2018-02-01

The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957-2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

Super-resolution mapping using multi-viewing CHRIS/PROBA data

NASA Astrophysics Data System (ADS)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

Geostatistical methods in evaluating spatial variability of groundwater quality in Al-Kharj Region, Saudi Arabia

NASA Astrophysics Data System (ADS)

Al-Omran, Abdulrasoul M.; Aly, Anwar A.; Al-Wabel, Mohammad I.; Al-Shayaa, Mohammad S.; Sallam, Abdulazeam S.; Nadeem, Mahmoud E.

2017-11-01

The analyses of 180 groundwater samples of Al-Kharj, Saudi Arabia, recorded that most groundwaters are unsuitable for drinking uses due to high salinity; however, they can be used for irrigation with some restriction. The electric conductivity of studied groundwater ranged between 1.05 and 10.15 dS m-1 with an average of 3.0 dS m-1. Nitrate was also found in high concentration in some groundwater. Piper diagrams revealed that the majority of water samples are magnesium-calcium/sulfate-chloride water type. The Gibbs's diagram revealed that the chemical weathering of rock-forming minerals and evaporation are influencing the groundwater chemistry. A kriging method was used for predicting spatial distribution of salinity (EC dS m-1) and NO3 - (mg L-1) in Al-Kharj's groundwater using data of 180 different locations. After normalization of data, variogram was drawn, for selecting suitable model for fitness on experimental variogram, less residual sum of squares value was used. Then cross-validation and root mean square error were used to select the best method for interpolation. The kriging method was found suitable methods for groundwater interpolation and management using either GS+ or ArcGIS.

Downscaling climate information for local disease mapping.

PubMed

Bernardi, M; Gommes, R; Grieser, J

2006-06-01

The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical downscaling using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the downscaling of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage.

The Statistical Differences Between the Gridded Temperature Datasets, and its Implications for Stochastic Modelling

NASA Astrophysics Data System (ADS)

Fredriksen, H. B.; Løvsletten, O.; Rypdal, M.; Rypdal, K.

2014-12-01

Several research groups around the world collect instrumental temperature data and combine them in different ways to obtain global gridded temperature fields. The three most well known datasets are HadCRUT4 produced by the Climatic Research Unit and the Met Office Hadley Centre in UK, one produced by NASA GISS, and one produced by NOAA. Recently Berkeley Earth has also developed a gridded dataset. All these four will be compared in our analysis. The statistical properties we will focus on are the standard deviation and the Hurst exponent. These two parameters are sufficient to describe the temperatures as long-range memory stochastic processes; the standard deviation describes the general fluctuation level, while the Hurst exponent relates the strength of the long-term variability to the strength of the short-term variability. A higher Hurst exponent means that the slow variations are stronger compared to the fast, and that the autocovariance function will have a stronger tail. Hence the Hurst exponent gives us information about the persistence or memory of the process. We make use of these data to show that data averaged over a larger area exhibit higher Hurst exponents and lower variance than data averaged over a smaller area, which provides information about the relationship between temporal and spatial correlations of the temperature fluctuations. Interpolation in space has some similarities with averaging over space, although interpolation is more weighted towards the measurement locations. We demonstrate that the degree of spatial interpolation used can explain some differences observed between the variances and memory exponents computed from the various datasets.

Spatial adaptive sampling in multiscale simulation

NASA Astrophysics Data System (ADS)

Rouet-Leduc, Bertrand; Barros, Kipton; Cieren, Emmanuel; Elango, Venmugil; Junghans, Christoph; Lookman, Turab; Mohd-Yusof, Jamaludin; Pavel, Robert S.; Rivera, Axel Y.; Roehm, Dominic; McPherson, Allen L.; Germann, Timothy C.

2014-07-01

In a common approach to multiscale simulation, an incomplete set of macroscale equations must be supplemented with constitutive data provided by fine-scale simulation. Collecting statistics from these fine-scale simulations is typically the overwhelming computational cost. We reduce this cost by interpolating the results of fine-scale simulation over the spatial domain of the macro-solver. Unlike previous adaptive sampling strategies, we do not interpolate on the potentially very high dimensional space of inputs to the fine-scale simulation. Our approach is local in space and time, avoids the need for a central database, and is designed to parallelize well on large computer clusters. To demonstrate our method, we simulate one-dimensional elastodynamic shock propagation using the Heterogeneous Multiscale Method (HMM); we find that spatial adaptive sampling requires only ≈ 50 ×N0.14 fine-scale simulations to reconstruct the stress field at all N grid points. Related multiscale approaches, such as Equation Free methods, may also benefit from spatial adaptive sampling.

A Study on the Effects of Spatial Scale on Snow Process in Hyper-Resolution Hydrological Modelling over Mountainous Areas

NASA Astrophysics Data System (ADS)

Garousi Nejad, I.; He, S.; Tang, Q.; Ogden, F. L.; Steinke, R. C.; Frazier, N.; Tarboton, D. G.; Ohara, N.; Lin, H.

2017-12-01

Spatial scale is one of the main considerations in hydrological modeling of snowmelt in mountainous areas. The size of model elements controls the degree to which variability can be explicitly represented versus what needs to be parameterized using effective properties such as averages or other subgrid variability parameterizations that may degrade the quality of model simulations. For snowmelt modeling terrain parameters such as slope, aspect, vegetation and elevation play an important role in the timing and quantity of snowmelt that serves as an input to hydrologic runoff generation processes. In general, higher resolution enhances the accuracy of the simulation since fine meshes represent and preserve the spatial variability of atmospheric and surface characteristics better than coarse resolution. However, this increases computational cost and there may be a scale beyond which the model response does not improve due to diminishing sensitivity to variability and irreducible uncertainty associated with the spatial interpolation of inputs. This paper examines the influence of spatial resolution on the snowmelt process using simulations of and data from the Animas River watershed, an alpine mountainous area in Colorado, USA, using an unstructured distributed physically based hydrological model developed for a parallel computing environment, ADHydro. Five spatial resolutions (30 m, 100 m, 250 m, 500 m, and 1 km) were used to investigate the variations in hydrologic response. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of ADHydro to obtain a balance between representing spatial variability and the computational cost. According to the results, variation in the input variables and parameters due to using different spatial resolution resulted in changes in the obtained hydrological variables, especially snowmelt, both at the basin-scale and distributed across the model mesh.

Comparison of spatiotemporal prediction models of daily exposure of individuals to ambient nitrogen dioxide and ozone in Montreal, Canada.

PubMed

Buteau, Stephane; Hatzopoulou, Marianne; Crouse, Dan L; Smargiassi, Audrey; Burnett, Richard T; Logan, Travis; Cavellin, Laure Deville; Goldberg, Mark S

2017-07-01

In previous studies investigating the short-term health effects of ambient air pollution the exposure metric that is often used is the daily average across monitors, thus assuming that all individuals have the same daily exposure. Studies that incorporate space-time exposures of individuals are essential to further our understanding of the short-term health effects of ambient air pollution. As part of a longitudinal cohort study of the acute effects of air pollution that incorporated subject-specific information and medical histories of subjects throughout the follow-up, the purpose of this study was to develop and compare different prediction models using data from fixed-site monitors and other monitoring campaigns to estimate daily, spatially-resolved concentrations of ozone (O 3 ) and nitrogen dioxide (NO 2 ) of participants' residences in Montreal, 1991-2002. We used the following methods to predict spatially-resolved daily concentrations of O 3 and NO 2 for each geographic region in Montreal (defined by three-character postal code areas): (1) assigning concentrations from the nearest monitor; (2) spatial interpolation using inverse-distance weighting; (3) back-extrapolation from a land-use regression model from a dense monitoring survey, and; (4) a combination of a land-use and Bayesian maximum entropy model. We used a variety of indices of agreement to compare estimates of exposure assigned from the different methods, notably scatterplots of pairwise predictions, distribution of differences and computation of the absolute agreement intraclass correlation (ICC). For each pairwise prediction, we also produced maps of the ICCs by these regions indicating the spatial variability in the degree of agreement. We found some substantial differences in agreement across pairs of methods in daily mean predicted concentrations of O 3 and NO 2 . On a given day and postal code area the difference in the concentration assigned could be as high as 131ppb for O 3 and 108ppb for NO 2 . For both pollutants, better agreement was found between predictions from the nearest monitor and the inverse-distance weighting interpolation methods, with ICCs of 0.89 (95% confidence interval (CI): 0.89, 0.89) for O 3 and 0.81 (95%CI: 0.80, 0.81) for NO 2 , respectively. For this pair of methods the maximum difference on a given day and postal code area was 36ppb for O 3 and 74ppb for NO 2 . The back-extrapolation method showed a higher degree of disagreement with the nearest monitor approach, inverse-distance weighting interpolation, and the Bayesian maximum entropy model, which were strongly constrained by the sparse monitoring network. The maps showed that the patterns of agreement differed across the postal code areas and the variability depended on the pair of methods compared and the pollutants. For O 3 , but not NO 2 , postal areas showing greater disagreement were mostly located near the city centre and along highways, especially in maps involving the back-extrapolation method. In view of the substantial differences in daily concentrations of O 3 and NO 2 predicted by the different methods, we suggest that analyses of the health effects from air pollution should make use of multiple exposure assessment methods. Although we cannot make any recommendations as to which is the most valid method, models that make use of higher spatially resolved data, such as from dense exposure surveys or from high spatial resolution satellite data, likely provide the most valid estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

NASA Astrophysics Data System (ADS)

Qu, Jiahui; Li, Yunsong; Dong, Wenqian

2018-01-01

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component (PC1) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wahid, Ali, E-mail: ali.wahid@live.com; Salim, Ahmed Mohamed Ahmed, E-mail: mohamed.salim@petronas.com.my; Yusoff, Wan Ismail Wan, E-mail: wanismail-wanyusoff@petronas.com.my

2016-02-01

Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rockmore » properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.« less

Is substrate composition a suitable predictor for deep-water coral occurrence on fine scales?

NASA Astrophysics Data System (ADS)

Bennecke, Swaantje; Metaxas, Anna

2017-06-01

Species distribution modelling can be applied to identify potentially suitable habitat for species with largely unknown distributions, such as many deep-water corals. Important variables influencing species occurrence in the deep sea, e.g. substrate composition, are often not included in these modelling approaches because high-resolution data are unavailable. We investigated the relationship between substrate composition and the occurrence of the two deep-water octocoral species Primnoa resedaeformis and Paragorgia arborea, which require hard substrate for attachment. On a scale of 10s of metres, we analysed images of the seafloor taken at two locations inside the Northeast Channel Coral Conservation Area in the Northwest Atlantic. We interpolated substrate composition over the sampling areas and determined the contribution of substrate classes, depth and slope to describe habitat suitability using maximum entropy modelling (Maxent). Substrate composition was similar at both sites - dominated by pebbles in a matrix of sand (>80%) with low percentages of suitable substrate for coral occurrence. Coral abundance was low at site 1 (0.9 colonies of P. resedaeformis per 100 m2) and high at site 2 (63 colonies of P. resedaeformis per 100 m2) indicating that substrate alone is not sufficient to explain varying patterns in coral occurrence. Spatial interpolations of substrate classes revealed the difficulty to accurately resolve sparsely distributed boulders (3-5% of substrate). Boulders were by far the most important variable in the habitat suitability model (HSM) for P. resedaeformis at site 1, indicating the fundamental influence of a substrate class that is the least abundant. At site 2, HSMs identified cobbles and sand/pebble as the most important variables for habitat suitability. However, substrate classes were correlated making it difficult to determine the influence of individual variables. To provide accurate information on habitat suitability for the two coral species, substrate composition needs to be quantified so that small fractions (<20% contribution of certain substrate class) of suitable substrate are resolved. While the collection and analysis of high-resolution data is costly and spatially limited, the required resolution is unlikely to be achieved in coarse-scale interpolations of substrate data.

Estimation of geotechnical parameters on the basis of geophysical methods and geostatistics

NASA Astrophysics Data System (ADS)

Brom, Aleksander; Natonik, Adrianna

2017-12-01

The paper presents possible implementation of ordinary cokriging and geophysical investigation on humidity data acquired in geotechnical studies. The Author describes concept of geostatistics, terminology of geostatistical modelling, spatial correlation functions, principles of solving cokriging systems, advantages of (co-)kriging in comparison with other interpolation methods, obstacles in this type of attempt. Cross validation and discussion of results was performed with an indication of prospect of applying similar procedures in various researches..

Quasi 3D modelling of water flow in the sandy soil

NASA Astrophysics Data System (ADS)

Rezaei, Meisam; Seuntjens, Piet; Joris, Ingeborg; Boënne, Wesley; De Pue, Jan; Cornelis, Wim

2016-04-01

Monitoring and modeling tools may improve irrigation strategies in precision agriculture. Spatial interpolation is required for analyzing the effects of soil hydraulic parameters, soil layer thickness and groundwater level on irrigation management using hydrological models at field scale. We used non-invasive soil sensor, a crop growth (LINGRA-N) and a soil hydrological model (Hydrus-1D) to predict soil-water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. In the first step, the sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models at one soil column. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. In the second step, to predict Ks over the whole field, the spatial distributions of Ks and its relationship between co-located soil ECa measured by a DUALEM-21S sensor were investigated. Measured groundwater levels and soil layer thickness were interpolated using ordinary point kriging (OK) to a 0.5 by 0.5 m in aim of digital elevation maps. In the third step, a quasi 3D modelling approach was conducted using interpolated data as input hydraulic parameter, geometric information and boundary conditions in the integrated model. In addition, three different irrigation scenarios namely current, no irrigation and optimized irrigations were carried out to find out the most efficient irrigation regime. In this approach, detailed field scale maps of soil water stress, water storage and crop yield were produced at each specific time interval to evaluate the best and most efficient distribution of water using standard gun sprinkler irrigation. The results show that the effect of the position of the groundwater level was dominant in soil-water content prediction and associated water stress. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity Ks in a two-layered soil. Results demonstrated the large spatial variability of Ks (CV = 86.21%). A significant negative correlation was found between ln Ks and ECa (r = 0.83; P≤0.01). This site-specific relation between ln Ks and ECa was used to predict Ks for the whole field after validation using an independent dataset of measured Ks. Result showed that this approach can accurately determine the field scale irrigation requirements, taking into account variations in boundary conditions and spatial variations of model parameters across the field. We found that uniform distribution of water using standard gun sprinkler irrigation is not an efficient approach since at locations with shallow groundwater, the amount of water applied will be excessive as compared to the crop requirements, while in locations with a deeper groundwater table, the crop irrigation requirements will not be met during crop water stress. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to ~25% irrigation water as compared to the current irrigation regime. This resulted in a yield increase of ~7%, simulated by the crop growth model.

Optimal design of the first stage of the plate-fin heat exchanger for the EAST cryogenic system

NASA Astrophysics Data System (ADS)

Qingfeng, JIANG; Zhigang, ZHU; Qiyong, ZHANG; Ming, ZHUANG; Xiaofei, LU

2018-03-01

The size of the heat exchanger is an important factor determining the dimensions of the cold box in helium cryogenic systems. In this paper, a counter-flow multi-stream plate-fin heat exchanger is optimized by means of a spatial interpolation method coupled with a hybrid genetic algorithm. Compared with empirical correlations, this spatial interpolation algorithm based on a kriging model can be adopted to more precisely predict the Colburn heat transfer factors and Fanning friction factors of offset-strip fins. Moreover, strict computational fluid dynamics simulations can be carried out to predict the heat transfer and friction performance in the absence of reliable experimental data. Within the constraints of heat exchange requirements, maximum allowable pressure drop, existing manufacturing techniques and structural strength, a mathematical model of an optimized design with discrete and continuous variables based on a hybrid genetic algorithm is established in order to minimize the volume. The results show that for the first-stage heat exchanger in the EAST refrigerator, the structural size could be decreased from the original 2.200 × 0.600 × 0.627 (m3) to the optimized 1.854 × 0.420 × 0.340 (m3), with a large reduction in volume. The current work demonstrates that the proposed method could be a useful tool to achieve optimization in an actual engineering project during the practical design process.

Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought.

PubMed

Chen, Hui; Fan, Li; Wu, Wei; Liu, Hong-Bin

2017-09-26

Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010-2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2 , MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

Research on the optimization of air quality monitoring station layout based on spatial grid statistical analysis method.

PubMed

Li, Tianxin; Zhou, Xing Chen; Ikhumhen, Harrison Odion; Difei, An

2018-05-01

In recent years, with the significant increase in urban development, it has become necessary to optimize the current air monitoring stations to reflect the quality of air in the environment. Highlighting the spatial representation of some air monitoring stations using Beijing's regional air monitoring station data from 2012 to 2014, the monthly mean particulate matter concentration (PM10) in the region was calculated and through the IDW interpolation method and spatial grid statistical method using GIS, the spatial distribution of PM10 concentration in the whole region was deduced. The spatial distribution variation of districts in Beijing using the gridding model was performed, and through the 3-year spatial analysis, PM10 concentration data including the variation and spatial overlay (1.5 km × 1.5 km cell resolution grid), the spatial distribution result obtained showed that the total PM10 concentration frequency variation exceeded the standard. It is very important to optimize the layout of the existing air monitoring stations by combining the concentration distribution of air pollutants with the spatial region using GIS.

Voxel inversion of airborne electromagnetic data

NASA Astrophysics Data System (ADS)

Auken, E.; Fiandaca, G.; Kirkegaard, C.; Vest Christiansen, A.

2013-12-01

Inversion of electromagnetic data usually refers to a model space being linked to the actual observation points, and for airborne surveys the spatial discretization of the model space reflects the flight lines. On the contrary, geological and groundwater models most often refer to a regular voxel grid, not correlated to the geophysical model space. This means that incorporating the geophysical data into the geological and/or hydrological modelling grids involves a spatial relocation of the models, which in itself is a subtle process where valuable information is easily lost. Also the integration of prior information, e.g. from boreholes, is difficult when the observation points do not coincide with the position of the prior information, as well as the joint inversion of airborne and ground-based surveys. We developed a geophysical inversion algorithm working directly in a voxel grid disconnected from the actual measuring points, which then allows for informing directly geological/hydrogeological models, for easier incorporation of prior information and for straightforward integration of different data types in joint inversion. The new voxel model space defines the soil properties (like resistivity) on a set of nodes, and the distribution of the properties is computed everywhere by means of an interpolation function f (e.g. inverse distance or kriging). The position of the nodes is fixed during the inversion and is chosen to sample the soil taking into account topography and inversion resolution. Given this definition of the voxel model space, both 1D and 2D/3D forward responses can be computed. The 1D forward responses are computed as follows: A) a 1D model subdivision, in terms of model thicknesses and direction of the "virtual" horizontal stratification, is defined for each 1D data set. For EM soundings the "virtual" horizontal stratification is set up parallel to the topography at the sounding position. B) the "virtual" 1D models are constructed by interpolating the soil properties in the medium point of the "virtual" layers. For 2D/3D forward responses the algorithm operates similarly, simply filling the 2D/3D meshes of the forward responses by computing the interpolation values in the centres of the mesh cells. The new definition of the voxel model space allows for incorporating straightforwardly the geophysical information into geological and/or hydrological models, just by using for defining the geophysical model space a voxel (hydro)geological grid. This simplify also the propagation of the uncertainty of geophysical parameters into the (hydro)geological models. Furthermore, prior information from boreholes, like resistivity logs, can be applied directly to the voxel model space, even if the borehole positions do not coincide with the actual observation points. In fact, the prior information is constrained to the model parameters through the interpolation function at the borehole locations. The presented algorithm is a further development of the AarhusInv program package developed at Aarhus University (formerly em1dinv), which manages both large scale AEM surveys and ground-based data. This work has been carried out as part of the HyGEM project, supported by the Danish Council of Strategic Research under grant number DSF 11-116763.

Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model.

PubMed

Afzali, Maryam; Fatemizadeh, Emad; Soltanian-Zadeh, Hamid

2015-09-30

Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure and can be used to evaluate fiber bundles. However, due to practical constraints, DWI data acquired in clinics are low resolution. This paper proposes a method for interpolation of orientation distribution functions (ODFs). To this end, fuzzy clustering is applied to segment ODFs based on the principal diffusion directions (PDDs). Next, a cluster is modeled by a tensor so that an ODF is represented by a mixture of tensors. For interpolation, each tensor is rotated separately. The method is applied on the synthetic and real DWI data of control and epileptic subjects. Both experiments illustrate capability of the method in increasing spatial resolution of the data in the ODF field properly. The real dataset show that the method is capable of reliable identification of differences between temporal lobe epilepsy (TLE) patients and normal subjects. The method is compared to existing methods. Comparison studies show that the proposed method generates smaller angular errors relative to the existing methods. Another advantage of the method is that it does not require an iterative algorithm to find the tensors. The proposed method is appropriate for increasing resolution in the ODF field and can be applied to clinical data to improve evaluation of white matter fibers in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.

Landscape genomics of Sphaeralcea ambigua in the Mojave Desert: a multivariate, spatially-explicit approach to guide ecological restoration

USGS Publications Warehouse

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2015-01-01

Local adaptation influences plant species’ responses to climate change and their performance in ecological restoration. Fine-scale physiological or phenological adaptations that direct demographic processes may drive intraspecific variability when baseline environmental conditions change. Landscape genomics characterize adaptive differentiation by identifying environmental drivers of adaptive genetic variability and mapping the associated landscape patterns. We applied such an approach to Sphaeralcea ambigua, an important restoration plant in the arid southwestern United States, by analyzing variation at 153 amplified fragment length polymorphism loci in the context of environmental gradients separating 47 Mojave Desert populations. We identified 37 potentially adaptive loci through a combination of genome scan approaches. We then used a generalized dissimilarity model (GDM) to relate variability in potentially adaptive loci with spatial gradients in temperature, precipitation, and topography. We identified non-linear thresholds in loci frequencies driven by summer maximum temperature and water stress, along with continuous variation corresponding to temperature seasonality. Two GDM-based approaches for mapping predicted patterns of local adaptation are compared. Additionally, we assess uncertainty in spatial interpolations through a novel spatial bootstrapping approach. Our study presents robust, accessible methods for deriving spatially-explicit models of adaptive genetic variability in non-model species that will inform climate change modelling and ecological restoration.

Geospatial Predictive Modelling for Climate Mapping of Selected Severe Weather Phenomena Over Poland: A Methodological Approach

NASA Astrophysics Data System (ADS)

Walawender, Ewelina; Walawender, Jakub P.; Ustrnul, Zbigniew

2017-02-01

The main purpose of the study is to introduce methods for mapping the spatial distribution of the occurrence of selected atmospheric phenomena (thunderstorms, fog, glaze and rime) over Poland from 1966 to 2010 (45 years). Limited in situ observations as well the discontinuous and location-dependent nature of these phenomena make traditional interpolation inappropriate. Spatially continuous maps were created with the use of geospatial predictive modelling techniques. For each given phenomenon, an algorithm identifying its favourable meteorological and environmental conditions was created on the basis of observations recorded at 61 weather stations in Poland. Annual frequency maps presenting the probability of a day with a thunderstorm, fog, glaze or rime were created with the use of a modelled, gridded dataset by implementing predefined algorithms. Relevant explanatory variables were derived from NCEP/NCAR reanalysis and downscaled with the use of a Regional Climate Model. The resulting maps of favourable meteorological conditions were found to be valuable and representative on the country scale but at different correlation ( r) strength against in situ data (from r = 0.84 for thunderstorms to r = 0.15 for fog). A weak correlation between gridded estimates of fog occurrence and observations data indicated the very local nature of this phenomenon. For this reason, additional environmental predictors of fog occurrence were also examined. Topographic parameters derived from the SRTM elevation model and reclassified CORINE Land Cover data were used as the external, explanatory variables for the multiple linear regression kriging used to obtain the final map. The regression model explained 89 % of annual frequency of fog variability in the study area. Regression residuals were interpolated via simple kriging.

Spatio-temporal modeling of chronic PM 10 exposure for the Nurses' Health Study

NASA Astrophysics Data System (ADS)

Yanosky, Jeff D.; Paciorek, Christopher J.; Schwartz, Joel; Laden, Francine; Puett, Robin; Suh, Helen H.

2008-06-01

Chronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or county-wide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM10 concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM10 emissions, elevation, wind speed, and precipitation were found to be important determinants of PM10 concentrations and were included in the final model. Final model performance was strong (cross-validation R2=0.62), with little bias (-0.4 μg m-3) and high precision (6.4 μg m-3). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R2=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R2=0.51) or inverse distance weighted interpolation (cross-validation R2=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM10 exposures for large populations.

Validating the Kinematic Wave Approach for Rapid Soil Erosion Assessment and Improved BMP Site Selection to Enhance Training Land Sustainability

DTIC Science & Technology

2014-02-01

installation based on a Euclidean distance allocation and assigned that installation’s threshold values. The second approach used a thin - plate spline ...installation critical nLS+ thresholds involved spatial interpolation. A thin - plate spline radial basis functions (RBF) was selected as the...the interpolation of installation results using a thin - plate spline radial basis function technique. 6.5 OBJECTIVE #5: DEVELOP AND

Mapping soil particle-size fractions: A comparison of compositional kriging and log-ratio kriging

NASA Astrophysics Data System (ADS)

Wang, Zong; Shi, Wenjiao

2017-03-01

Soil particle-size fractions (psf) as basic physical variables need to be accurately predicted for regional hydrological, ecological, geological, agricultural and environmental studies frequently. Some methods had been proposed to interpolate the spatial distributions of soil psf, but the performance of compositional kriging and different log-ratio kriging methods is still unclear. Four log-ratio transformations, including additive log-ratio (alr), centered log-ratio (clr), isometric log-ratio (ilr), and symmetry log-ratio (slr), combined with ordinary kriging (log-ratio kriging: alr_OK, clr_OK, ilr_OK and slr_OK) were selected to be compared with compositional kriging (CK) for the spatial prediction of soil psf in Tianlaochi of Heihe River Basin, China. Root mean squared error (RMSE), Aitchison's distance (AD), standardized residual sum of squares (STRESS) and right ratio of the predicted soil texture types (RR) were chosen to evaluate the accuracy for different interpolators. The results showed that CK had a better accuracy than the four log-ratio kriging methods. The RMSE (sand, 9.27%; silt, 7.67%; clay, 4.17%), AD (0.45), STRESS (0.60) of CK were the lowest and the RR (58.65%) was the highest in the five interpolators. The clr_OK achieved relatively better performance than the other log-ratio kriging methods. In addition, CK presented reasonable and smooth transition on mapping soil psf according to the environmental factors. The study gives insights for mapping soil psf accurately by comparing different methods for compositional data interpolation. Further researches of methods combined with ancillary variables are needed to be implemented to improve the interpolation performance.

Spatial and temporal allocation of ship exhaust emissions in Australian coastal waters using AIS data: Analysis and treatment of data gaps

NASA Astrophysics Data System (ADS)

Goldsworthy, Brett

2017-08-01

Ship exhaust emissions need to be allocated accurately in both space and time in order to examine many of the associated impacts, including on air quality and health. Data on ship activity from the Automatic Identification System (AIS) allow ship exhaust emissions to be calculated with fine spatial and temporal resolution. However, there are spatial gaps in the coverage afforded by the coastal network of ground stations that are used to collect the AIS data. This paper focuses on the problem of allocating emissions to the coastal gap regions. Allocating emissions to these regions involves generating interpolated ship tracks that both span the gaps and avoid coming too close to land. In most cases, a simple shortest path or straight line interpolation produces tracks that do not overlap or come too close to land. Where the simple method does not produce acceptable results, vessel tracks are steered around land on shortest available paths using a combination of visibility graphs and Dijkstra's algorithm. A geographical cluster analysis is first used to identify the boundary regions of the data gaps. The properties of the data gaps are summarised in terms of the length, duration and speed of the spanning tracks. The interpolation methods are used to improve the spatial distribution of emissions. It is also shown that emissions in the gap regions can contribute substantially to the total ship exhaust emissions in close proximity to highly populated areas.

Remote sensing of evapotranspiration using automated calibration: Development and testing in the state of Florida

NASA Astrophysics Data System (ADS)

Evans, Aaron H.

Thermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and retrieved surface temperature. Disadvantage of these calibrations are 1) user must manually identify extremely dry and wet pixels in image 2) each calibration is only applicable over limited spatial extent. Producing larger maps is operationally limited due to time required to manually calibrate multiple spatial extents over multiple days. This dissertation develops techniques which automatically detect dry and wet pixels. LANDSAT imagery is used because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5) Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available energy, roughness length and wind speed is tested. A technique for temporally interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and tested. The automated algorithm is successful at detecting wet and dry pixels (if they exist). Including wet pixels in calibration and assuming constant atmospheric conductance significantly improved results for all but Big Cypress and Gainesville. Evaporative fraction is not very sensitive to instantaneous available energy but it is sensitive to temperature when wet pixels are included because temperature is required for estimating wet pixel evapotranspiration. Data fusion techniques only slightly outperformed linear interpolation. Eddy covariance comparison and temporal interpolation produced acceptable bias error for most cases suggesting automated calibration and interpolation could be used to predict monthly or annual ET. Maps demonstrating spatial patterns of evapotranspiration at field scale were successfully produced, but only for limited spatial extents. A framework has been established for producing larger maps by creating a mosaic of smaller individual maps.

Comparison of interpolation functions to improve a rebinning-free CT-reconstruction algorithm.

PubMed

de las Heras, Hugo; Tischenko, Oleg; Xu, Yuan; Hoeschen, Christoph

2008-01-01

The robust algorithm OPED for the reconstruction of images from Radon data has been recently developed. This reconstructs an image from parallel data within a special scanning geometry that does not need rebinning but only a simple re-ordering, so that the acquired fan data can be used directly for the reconstruction. However, if the number of rays per fan view is increased, there appear empty cells in the sinogram. These cells need to be filled by interpolation before the reconstruction can be carried out. The present paper analyzes linear interpolation, cubic splines and parametric (or "damped") splines for the interpolation task. The reconstruction accuracy in the resulting images was measured by the Normalized Mean Square Error (NMSE), the Hilbert Angle, and the Mean Relative Error. The spatial resolution was measured by the Modulation Transfer Function (MTF). Cubic splines were confirmed to be the most recommendable method. The reconstructed images resulting from cubic spline interpolation show a significantly lower NMSE than the ones from linear interpolation and have the largest MTF for all frequencies. Parametric splines proved to be advantageous only for small sinograms (below 50 fan views).

Spatial dynamics of the invasive defoliator amber-marked birch leafminer across the Anchorage landscape

Treesearch

J.E. Lundquist; R.M. Reich; M. Tuffly

2012-01-01

The amber-marked birch leafminer has caused severe infestations of birch species in Anchorage, AK, since 2002. Its spatial distribution has been monitored since 2006 and summarized using interpolated surfaces based on simple kriging. In this study, we developed methods of assessing and describing spatial distribution of the leafminer as they vary from year to year, and...

Error Estimation in an Optimal Interpolation Scheme for High Spatial and Temporal Resolution SST Analyses

NASA Technical Reports Server (NTRS)

Rigney, Matt; Jedlovec, Gary; LaFontaine, Frank; Shafer, Jaclyn

2010-01-01

Heat and moisture exchange between ocean surface and atmosphere plays an integral role in short-term, regional NWP. Current SST products lack both spatial and temporal resolution to accurately capture small-scale features that affect heat and moisture flux. NASA satellite is used to produce high spatial and temporal resolution SST analysis using an OI technique.

Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations

NASA Astrophysics Data System (ADS)

Fan, Ying; Miguez-Macho, Gonzalo; Weaver, Christopher P.; Walko, Robert; Robock, Alan

2007-05-01

Soil moisture is a key participant in land-atmosphere interactions and an important determinant of terrestrial climate. In regions where the water table is shallow, soil moisture is coupled to the water table. This paper is the first of a two-part study to quantify this coupling and explore its implications in the context of climate modeling. We examine the observed water table depth in the lower 48 states of the United States in search of salient spatial and temporal features that are relevant to climate dynamics. As a means to interpolate and synthesize the scattered observations, we use a simple two-dimensional groundwater flow model to construct an equilibrium water table as a result of long-term climatic and geologic forcing. Model simulations suggest that the water table depth exhibits spatial organization at watershed, regional, and continental scales, which may have implications for the spatial organization of soil moisture at similar scales. The observations suggest that water table depth varies at diurnal, event, seasonal, and interannual scales, which may have implications for soil moisture memory at these scales.

Spatiotemporal predictions of soil properties and states in variably saturated landscapes

NASA Astrophysics Data System (ADS)

Franz, Trenton E.; Loecke, Terrance D.; Burgin, Amy J.; Zhou, Yuzhen; Le, Tri; Moscicki, David

2017-07-01

Understanding greenhouse gas (GHG) fluxes from landscapes with variably saturated soil conditions is challenging given the highly dynamic nature of GHG fluxes in both space and time, dubbed hot spots, and hot moments. On one hand, our ability to directly monitor these processes is limited by sparse in situ and surface chamber observational networks. On the other hand, remote sensing approaches provide spatial data sets but are limited by infrequent imaging over time. We use a robust statistical framework to merge sparse sensor network observations with reconnaissance style hydrogeophysical mapping at a well-characterized site in Ohio. We find that combining time-lapse electromagnetic induction surveys with empirical orthogonal functions provides additional environmental covariates related to soil properties and states at high spatial resolutions ( 5 m). A cross-validation experiment using eight different spatial interpolation methods versus 120 in situ soil cores indicated an 30% reduction in root-mean-square error for soil properties (clay weight percent and total soil carbon weight percent) using hydrogeophysical derived environmental covariates with regression kriging. In addition, the hydrogeophysical derived environmental covariates were found to be good predictors of soil states (soil temperature, soil water content, and soil oxygen). The presented framework allows for temporal gap filling of individual sensor data sets as well as provides flexible geometric interpolation to complex areas/volumes. We anticipate that the framework, with its flexible temporal and spatial monitoring options, will be useful in designing future monitoring networks as well as support the next generation of hyper-resolution hydrologic and biogeochemical models.

Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

DOE PAGES

McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.; ...

2015-02-03

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection modelmore » departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less

Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection modelmore » departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less

Development and validation of segmentation and interpolation techniques in sinograms for metal artifact suppression in CT.

PubMed

Veldkamp, Wouter J H; Joemai, Raoul M S; van der Molen, Aart J; Geleijns, Jacob

2010-02-01

Metal prostheses cause artifacts in computed tomography (CT) images. The purpose of this work was to design an efficient and accurate metal segmentation in raw data to achieve artifact suppression and to improve CT image quality for patients with metal hip or shoulder prostheses. The artifact suppression technique incorporates two steps: metal object segmentation in raw data and replacement of the segmented region by new values using an interpolation scheme, followed by addition of the scaled metal signal intensity. Segmentation of metal is performed directly in sinograms, making it efficient and different from current methods that perform segmentation in reconstructed images in combination with Radon transformations. Metal signal segmentation is achieved by using a Markov random field model (MRF). Three interpolation methods are applied and investigated. To provide a proof of concept, CT data of five patients with metal implants were included in the study, as well as CT data of a PMMA phantom with Teflon, PVC, and titanium inserts. Accuracy was determined quantitatively by comparing mean Hounsfield (HU) values and standard deviation (SD) as a measure of distortion in phantom images with titanium (original and suppressed) and without titanium insert. Qualitative improvement was assessed by comparing uncorrected clinical images with artifact suppressed images. Artifacts in CT data of a phantom and five patients were automatically suppressed. The general visibility of structures clearly improved. In phantom images, the technique showed reduced SD close to the SD for the case where titanium was not inserted, indicating improved image quality. HU values in corrected images were different from expected values for all interpolation methods. Subtle differences between interpolation methods were found. The new artifact suppression design is efficient, for instance, in terms of preserving spatial resolution, as it is applied directly to original raw data. It successfully reduced artifacts in CT images of five patients and in phantom images. Sophisticated interpolation methods are needed to obtain reliable HU values close to the prosthesis.

Climatological Downscaling and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.

NASA Astrophysics Data System (ADS)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.

2007-05-01

The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS-based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002). It is demonstrated that the incorporation of climatological information in a downscaling procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for military and civilian customer applications.

An analysis of simulated and observed storm characteristics

NASA Astrophysics Data System (ADS)

Benestad, R. E.

2010-09-01

A calculus-based cyclone identification (CCI) method has been applied to the most recent re-analysis (ERAINT) from the European Centre for Medium-range Weather Forecasts and results from regional climate model (RCM) simulations. The storm frequency for events with central pressure below a threshold value of 960-990hPa were examined, and the gradient wind from the simulated storm systems were compared with corresponding estimates from the re-analysis. The analysis also yielded estimates for the spatial extent of the storm systems, which was also included in the regional climate model cyclone evaluation. A comparison is presented between a number of RCMs and the ERAINT re-analysis in terms of their description of the gradient winds, number of cyclones, and spatial extent. Furthermore, a comparison between geostrophic wind estimated though triangules of interpolated or station measurements of SLP is presented. Wind still represents one of the more challenging variables to model realistically.

The Canadian Precipitation Analysis (CaPA): Evaluation of the statistical interpolation scheme

NASA Astrophysics Data System (ADS)

Evans, Andrea; Rasmussen, Peter; Fortin, Vincent

2013-04-01

CaPA (Canadian Precipitation Analysis) is a data assimilation system which employs statistical interpolation to combine observed precipitation with gridded precipitation fields produced by Environment Canada's Global Environmental Multiscale (GEM) climate model into a final gridded precipitation analysis. Precipitation is important in many fields and applications, including agricultural water management projects, flood control programs, and hydroelectric power generation planning. Precipitation is a key input to hydrological models, and there is a desire to have access to the best available information about precipitation in time and space. The principal goal of CaPA is to produce this type of information. In order to perform the necessary statistical interpolation, CaPA requires the estimation of a semi-variogram. This semi-variogram is used to describe the spatial correlations between precipitation innovations, defined as the observed precipitation amounts minus the GEM forecasted amounts predicted at the observation locations. Currently, CaPA uses a single isotropic variogram across the entire analysis domain. The present project investigates the implications of this choice by first conducting a basic variographic analysis of precipitation innovation data across the Canadian prairies, with specific interest in identifying and quantifying potential anisotropy within the domain. This focus is further expanded by identifying the effect of storm type on the variogram. The ultimate goal of the variographic analysis is to develop improved semi-variograms for CaPA that better capture the spatial complexities of precipitation over the Canadian prairies. CaPA presently applies a Box-Cox data transformation to both the observations and the GEM data, prior to the calculation of the innovations. The data transformation is necessary to satisfy the normal distribution assumption, but introduces a significant bias. The second part of the investigation aims at devising a bias correction scheme based on a moving-window averaging technique. For both the variogram and bias correction components of this investigation, a series of trial runs are conducted to evaluate the impact of these changes on the resulting CaPA precipitation analyses.

A MAP-based image interpolation method via Viterbi decoding of Markov chains of interpolation functions.

PubMed

Vedadi, Farhang; Shirani, Shahram

2014-01-01

A new method of image resolution up-conversion (image interpolation) based on maximum a posteriori sequence estimation is proposed. Instead of making a hard decision about the value of each missing pixel, we estimate the missing pixels in groups. At each missing pixel of the high resolution (HR) image, we consider an ensemble of candidate interpolation methods (interpolation functions). The interpolation functions are interpreted as states of a Markov model. In other words, the proposed method undergoes state transitions from one missing pixel position to the next. Accordingly, the interpolation problem is translated to the problem of estimating the optimal sequence of interpolation functions corresponding to the sequence of missing HR pixel positions. We derive a parameter-free probabilistic model for this to-be-estimated sequence of interpolation functions. Then, we solve the estimation problem using a trellis representation and the Viterbi algorithm. Using directional interpolation functions and sequence estimation techniques, we classify the new algorithm as an adaptive directional interpolation using soft-decision estimation techniques. Experimental results show that the proposed algorithm yields images with higher or comparable peak signal-to-noise ratios compared with some benchmark interpolation methods in the literature while being efficient in terms of implementation and complexity considerations.

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States.

PubMed

Lee, Seung-Jae; Serre, Marc L; van Donkelaar, Aaron; Martin, Randall V; Burnett, Richard T; Jerrett, Michael

2012-12-01

A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

Blotch removal for old movie restoration using epitome analysis

NASA Astrophysics Data System (ADS)

Rashwan, Abdullah M.

2011-10-01

Automatic blotch removal in old movies is important in film restoration. Blotches are black or white spots randomly occurring along the movie frames. Removing these spots are obtained by first automatically detecting the blotches then interpolating them using the spatial and temporal information in current, succeeding, and preceding frames. In this paper, simplified Rank Order Detector (sROD) is used with tweaked parameters to over detect the blotches, Epitome Analysis is used for interpolating the detected blotches.

MOD3D: a model for incorporating MODTRAN radiative transfer into 3D simulations

NASA Astrophysics Data System (ADS)

Berk, Alexander; Anderson, Gail P.; Gossage, Brett N.

2001-08-01

MOD3D, a rapid and accurate radiative transport algorithm, is being developed for application to 3D simulations. MOD3D couples to optical property databases generated by the MODTRAN4 Correlated-k (CK) band model algorithm. The Beer's Law dependence of the CK algorithm provides for proper coupling of illumination and line-of-sight paths. Full 3D spatial effects are modeled by scaling and interpolating optical data to local conditions. A C++ version of MOD3D has been integrated into JMASS for calculation of path transmittances, thermal emission and single scatter solar radiation. Results from initial validation efforts are presented.

The inclusion of ocean-current effects in a tidal-current model as forcing in the convection term and its application to the mesoscale fate of CO2 seeping from the seafloor

NASA Astrophysics Data System (ADS)

Sakaizawa, Ryosuke; Kawai, Takaya; Sato, Toru; Oyama, Hiroyuki; Tsumune, Daisuke; Tsubono, Takaki; Goto, Koichi

2018-03-01

The target seas of tidal-current models are usually semi-closed bays, minimally affected by ocean currents. For these models, tidal currents are simulated in computational domains with a spatial scale of a couple hundred kilometers or less, by setting tidal elevations at their open boundaries. However, when ocean currents cannot be ignored in the sea areas of interest, such as in open seas near coastlines, it is necessary to include ocean-current effects in these tidal-current models. In this study, we developed a numerical method to analyze tidal currents near coasts by incorporating pre-calculated ocean-current velocities. First, a large regional-scale simulation with a spatial scale of several thousand kilometers was conducted and temporal changes in the ocean-current velocity at each grid point were stored. Next, the spatially and temporally interpolated ocean-current velocity was incorporated as forcing into the cross terms of the convection term of a tidal-current model having computational domains with spatial scales of hundreds of kilometers or less. Then, we applied this method to the diffusion of dissolved CO2 in a sea area off Tomakomai, Japan, and compared the numerical results and measurements to validate the proposed method.

Linking Meteorology, Air Quality Models and Observations to ...

EPA Pesticide Factsheets

Epidemiologic studies are critical in establishing the association between exposure to air pollutants and adverse health effects. Results of epidemiologic studies are used by U.S. EPA in developing air quality standards to protect the public from the health effects of air pollutants. A major challenge in environmental epidemiology is adequate exposure characterization. Numerous health studies have used measurements from a few central-site ambient monitors to characterize air pollution exposures. Relying solely on central-site ambient monitors does not account for the spatial-heterogeneity of ambient air pollution patterns, the temporal variability in ambient concentrations, nor the influence of infiltration and indoor sources. Central-site monitoring becomes even more problematic for certain air pollutants that exhibit significant spatial heterogeneity. Statistical interpolation techniques and passive monitoring methods can provide additional spatial resolution in ambient concentration estimates. In addition, spatio-temporal models, which integrate GIS data and other factors, such as meteorology, have also been developed to produce more resolved estimates of ambient concentrations. Models, such as the Community Multi-Scale Air Quality (CMAQ) model, estimate ambient concentrations by combining information on meteorology, source emissions, and chemical-fate and transport. Hybrid modeling approaches, which integrate regional scale models with local scale dispersion

Reconstruction of a Three Hourly 1-km Land Surface Air Temperature Dataset in the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.; Ding, L.

2017-12-01

Land surface air temperature (SAT) is an important parameter in the modeling of radiation balance and energy budget of the earth surface. Generally, SAT is measured at ground meteorological stations; then SAT mapping is possible though a spatial interpolation process. The interpolated SAT map relies on the spatial distribution of ground stations, the terrain, and many other factors; thus, it has great uncertainties in regions with complicated terrain. Instead, SAT map can also be obtained through physical modeling of interactions between the land surface and the atmosphere. Such dataset generally has coarse spatial resolution (e.g. coarser than 0.1°) and cannot satisfy the applications at fine scales, e.g. 1 km. This presentation reports the reconstruction of a three hourly 1-km SAT dataset from 2001 to 2015 over the Qinghai-Tibet Plateau. The terrain in the Qinghai-Tibet Plateau, especially in the eastern part, is extremely complicated. Two SAT datasets with good qualities are used in this study. The first one is from the 3h China Meteorological Forcing Dataset with a 0.1° resolution released by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang et al., 2010); the second one is from the ERA-Interim product with the same temporal resolution and a 0.125° resolution. A statistical approach is developed to downscale the spatial resolution of the derived SAT to 1-km. The elevation and the normalized difference vegetation index (NDVI) are selected as two scaling factors in the downscaling approach. Results demonstrate there is significantly negative correlation between the SAT and elevation in all seasons; there is also significantly negative correlation between the SAT and NDVI in the vegetation growth seasons, while the correlation decreases in the other seasons. Therefore, a temporally dynamic downscaling approach is feasible to enhance the spatial resolution of the SAT. Compared with the SAT at the 0.1° or 0.125°, the reconstructed 1-km SAT can provide much more spatial details in areas with complicated terrain. Additionally, the 1-km SAT agrees well with the ground measured air temperatures as well as the SAT before downscaling. The reconstructed SAT will be beneficial for the modeling of surface radiation balance and energy budget over the Qinghai-Tibet Plateau.

Space Weather Activities of IONOLAB Group: TEC Mapping

NASA Astrophysics Data System (ADS)

Arikan, F.; Yilmaz, A.; Arikan, O.; Sayin, I.; Gurun, M.; Akdogan, K. E.; Yildirim, S. A.

2009-04-01

Being a key player in Space Weather, ionospheric variability affects the performance of both communication and navigation systems. To improve the performance of these systems, ionosphere has to be monitored. Total Electron Content (TEC), line integral of the electron density along a ray path, is an important parameter to investigate the ionospheric variability. A cost-effective way of obtaining TEC is by using dual-frequency GPS receivers. Since these measurements are sparse in space, accurate and robust interpolation techniques are needed to interpolate (or map) the TEC distribution for a given region in space. However, the TEC data derived from GPS measurements contain measurement noise, model and computational errors. Thus, it is necessary to analyze the interpolation performance of the techniques on synthetic data sets that can represent various ionospheric states. By this way, interpolation performance of the techniques can be compared over many parameters that can be controlled to represent the desired ionospheric states. In this study, Multiquadrics, Inverse Distance Weighting (IDW), Cubic Splines, Ordinary and Universal Kriging, Random Field Priors (RFP), Multi-Layer Perceptron Neural Network (MLP-NN), and Radial Basis Function Neural Network (RBF-NN) are employed as the spatial interpolation algorithms. These mapping techniques are initially tried on synthetic TEC surfaces for parameter and coefficient optimization and determination of error bounds. Interpolation performance of these methods are compared on synthetic TEC surfaces over the parameters of sampling pattern, number of samples, the variability of the surface and the trend type in the TEC surfaces. By examining the performance of the interpolation methods, it is observed that both Kriging, RFP and NN have important advantages and possible disadvantages depending on the given constraints. It is also observed that the determining parameter in the error performance is the trend in the Ionosphere. Optimization of the algorithms in terms of their performance parameters (like the choice of the semivariogram function for Kriging algorithms and the hidden layer and neuron numbers for MLP-NN) mostly depend on the behavior of the ionosphere at that given time instant for the desired region. The sampling pattern and number of samples are the other important parameters that may contribute to the higher errors in reconstruction. For example, for all of the above listed algorithms, hexagonal regular sampling of the ionosphere provides the lowest reconstruction error and the performance significantly degrades as the samples in the region become sparse and clustered. The optimized models and coefficients are applied to regional GPS-TEC mapping using the IONOLAB-TEC data (www.ionolab.org). Both Kriging combined with Kalman Filter and dynamic modeling of NN are also implemented as first trials of TEC and space weather predictions.

Short term spatio-temporal variability of soil water-extractable calcium and magnesium after a low severity grassland fire in Lithuania.

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Martin, David

2014-05-01

Fire has important impacts on soil nutrient spatio-temporal distribution (Outeiro et al., 2008). This impact depends on fire severity, topography of the burned area, type of soil and vegetation affected, and the meteorological conditions post-fire. Fire produces a complex mosaic of impacts in soil that can be extremely variable at small plot scale in the space and time. In order to assess and map such a heterogeneous distribution, the test of interpolation methods is fundamental to identify the best estimator and to have a better understanding of soil nutrients spatial distribution. The objective of this work is to identify the short-term spatial variability of water-extractable calcium and magnesium after a low severity grassland fire. The studied area is located near Vilnius (Lithuania) at 54° 42' N, 25° 08 E, 158 masl. Four days after the fire, it was designed in a burned area a plot with 400 m2 (20 x 20 m with 5 m space between sampling points). Twenty five samples from top soil (0-5 cm) were collected immediately after the fire (IAF), 2, 5, 7 and 9 months after the fire (a total of 125 in all sampling dates). The original data of water-extractable calcium and magnesium did not respected the Gaussian distribution, thus a neperian logarithm (ln) was applied in order to normalize data. Significant differences of water-extractable calcium and magnesium among sampling dates were carried out with the Anova One-way test using the ln data. In order to assess the spatial variability of water-extractable calcium and magnesium, we tested several interpolation methods as Ordinary Kriging (OK), Inverse Distance to a Weight (IDW) with the power of 1, 2, 3 and 4, Radial Basis Functions (RBF) - Inverse Multiquadratic (IMT), Multilog (MTG), Multiquadratic (MTQ) Natural Cubic Spline (NCS) and Thin Plate Spline (TPS) - and Local Polynomial (LP) with the power of 1 and 2. Interpolation tests were carried out with Ln data. The best interpolation method was assessed using the cross validation method. Cross-validation was obtained by taking each observation in turn out of the sample pool and estimating from the remaining ones. The errors produced (observed-predicted) are used to evaluate the performance of each method. With these data, the mean error (ME) and root mean square error (RMSE) were calculated. The best method was the one which had the lower RMSE (Pereira et al. in press). The results shown significant differences among sampling dates in the water-extractable calcium (F= 138.78, p< 0.001) and extractable magnesium (F= 160.66; p< 0.001). Water-extractable calcium and magnesium was high IAF decreasing until 7 months after the fire, rising in the last sampling date. Among the tested methods, the most accurate to interpolate the water-extractable calcium were: IAF-IDW1; 2 Months-IDW1; 5 months-OK; 7 Months-IDW4 and 9 Months-IDW3. In relation to water-extractable magnesium the best interpolation techniques were: IAF-IDW2; 2 Months-IDW1; 5 months- IDW3; 7 Months-TPS and 9 Months-IDW1. These results suggested that the spatial variability of these water-extractable is variable with the time. The causes of this variability will be discussed during the presentation. References Outeiro, L., Aspero, F., Ubeda, X. (2008) Geostatistical methods to study spatial variability of soil cation after a prescribed fire and rainfall. Catena, 74: 310-320. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195

[Monitoring the thermal plume from coastal nuclear power plant using satellite remote sensing data: modeling, and validation].

PubMed

Zhu, Li; Zhao, Li-Min; Wang, Qiao; Zhang, Ai-Ling; Wu, Chuan-Qing; Li, Jia-Guo; Shi, Ji-Xiang

2014-11-01

Thermal plume from coastal nuclear power plant is a small-scale human activity, mornitoring of which requires high-frequency and high-spatial remote sensing data. The infrared scanner (IRS), on board of HJ-1B, has an infrared channel IRS4 with 300 m and 4-days as its spatial and temporal resolution. Remote sensing data aquired using IRS4 is an available source for mornitoring thermal plume. Retrieval pattern for coastal sea surface temperature (SST) was built to monitor the thermal plume from nuclear power plant. The research area is located near Guangdong Daya Bay Nuclear Power Station (GNPS), where synchronized validations were also implemented. The National Centers for Environmental Prediction (NCEP) data was interpolated spatially and temporally. The interpolated data as well as surface weather conditions were subsequently employed into radiative transfer model for the atmospheric correction of IRS4 thermal image. A look-up-table (LUT) was built for the inversion between IRS4 channel radiance and radiometric temperature, and a fitted function was also built from the LUT data for the same purpose. The SST was finally retrieved based on those preprocessing procedures mentioned above. The bulk temperature (BT) of 84 samples distributed near GNPS was shipboard collected synchronically using salinity-temperature-deepness (CTD) instruments. The discrete sample data was surface interpolated and compared with the satellite retrieved SST. Results show that the average BT over the study area is 0.47 degrees C higher than the retrieved skin temperature (ST). For areas far away from outfall, the ST is higher than BT, with differences less than 1.0 degrees C. The main driving force for temperature variations in these regions is solar radiation. For areas near outfall, on the contrary, the retrieved ST is lower than BT, and greater differences between the two (meaning > 1.0 degrees C) happen when it gets closer to the outfall. Unlike the former case, the convective heat transfer resulting from the thermal plume is the primary reason leading to the temperature variations. Temperature rising (TR) distributions obtained from remote sensing data and in-situ measurements are consistent, except that the interpolated BT shows more level details (> 5 levels) than that of the ST (up to 4 levels). The areas with higher TR levels (> 2) are larger on BT maps, while for lower TR levels (≤ 2), the two methods perform with no obvious differences. Minimal errors for satellite-derived SST occur regularly around local time 10 a. m. This makes the remote sensing results to be substitutes for in-situ measurements. Therefore, for operational applications of HJ-1B IRS4, remote sensing technique can be a practical approach to monitoring the nuclear plant thermal pollution around this time period.

An Environmental Data Set for Vector-Borne Disease Modeling and Epidemiology

PubMed Central

Chabot-Couture, Guillaume; Nigmatulina, Karima; Eckhoff, Philip

2014-01-01

Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling. PMID:24755954

WegenerNet 1km-scale sub-daily rainfall data and their application: a hydrological modeling study on the sensitivity of small-catchment runoff to spatial rainfall variability

NASA Astrophysics Data System (ADS)

Oh, Sungmin; Hohmann, Clara; Foelsche, Ulrich; Fuchsberger, Jürgen; Rieger, Wolfgang; Kirchengast, Gottfried

2017-04-01

WegenerNet Feldbach region (WEGN), a pioneering experiment for weather and climate observations, has recently completed its first 10-year precipitation measurement cycle. The WEGN has measured precipitation, temperature, humidity, and other parameters since the beginning of 2007, supporting local-level monitoring and modeling studies, over an area of about 20 km x 15 km centered near the City of Feldbach (46.93 ˚ N, 15.90 ˚ E) in the Alpine forelands of southeast Austria. All the 151 stations in the network are now equipped with high-quality Meteoservis sensors as of August 2016, following an equipment with Friedrichs sensors at most stations before, and continue to provide high-resolution (2 km2/5-min) gauge based precipitation measurements for interested users in hydro-meteorological communities. Here we will present overall characteristics of the WEGN, with a focus on sub-daily precipitation measurements, from the data processing (data quality control, gridded data products generation, etc.) to data applications (e.g., ground validation of satellite estimates). The latter includes our recent study on the propagation of uncertainty from rainfall to runoff. The study assesses responses of small-catchment runoff to spatial rainfall variability in the WEGN region over the Raab valley, using a physics-based distributed hydrological model; Water Flow and Balance Simulation Model (WaSiM), developed at ETH Zurich (Schulla, ETH Zurich, 1997). Given that uncertainty due to resolution of rainfall measurements is believed to be a significant source of error in hydrologic modeling especially for convective rainfall that dominates in the region during summer, the high-resolution of WEGN data furnishes a great opportunity to analyze effects of rainfall events on the runoff at different spatial resolutions. Furthermore, the assessment can be conducted not only for the lower Raab catchment (area of about 500 km2) but also for its sub-catchments (areas of about 30-70 km2). Beside the question how many stations are necessary for reliable hydrological modeling, different interpolation methods like Inverse Distance Interpolation, Elevation Dependent Regression, and combinations will be tested. This presentation will show the first results from a scale-depending analysis of spatial and temporal structures of heavy rainfall events and responses of simulated runoff at the event scale in the WEGN region.

An optimized data fusion method and its application to improve lateral boundary conditions in winter for Pearl River Delta regional PM2.5 modeling, China

NASA Astrophysics Data System (ADS)

Huang, Zhijiong; Hu, Yongtao; Zheng, Junyu; Zhai, Xinxin; Huang, Ran

2018-05-01

Lateral boundary conditions (LBCs) are essential for chemical transport models to simulate regional transport; however they often contain large uncertainties. This study proposes an optimized data fusion approach to reduce the bias of LBCs by fusing gridded model outputs, from which the daughter domain's LBCs are derived, with ground-level measurements. The optimized data fusion approach follows the framework of a previous interpolation-based fusion method but improves it by using a bias kriging method to correct the spatial bias in gridded model outputs. Cross-validation shows that the optimized approach better estimates fused fields in areas with a large number of observations compared to the previous interpolation-based method. The optimized approach was applied to correct LBCs of PM2.5 concentrations for simulations in the Pearl River Delta (PRD) region as a case study. Evaluations show that the LBCs corrected by data fusion improve in-domain PM2.5 simulations in terms of the magnitude and temporal variance. Correlation increases by 0.13-0.18 and fractional bias (FB) decreases by approximately 3%-15%. This study demonstrates the feasibility of applying data fusion to improve regional air quality modeling.

Spatial Patterns of High Aedes aegypti Oviposition Activity in Northwestern Argentina

PubMed Central

Estallo, Elizabet Lilia; Más, Guillermo; Vergara-Cid, Carolina; Lanfri, Mario Alberto; Ludueña-Almeida, Francisco; Scavuzzo, Carlos Marcelo; Introini, María Virginia; Zaidenberg, Mario; Almirón, Walter Ricardo

2013-01-01

Background In Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively. Methodology Oviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005–2007). Spatial autocorrelation was measured with Moran’s Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity. Principal Findings Both cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC = 0.77), obtaining 99% of sensitivity and 75.29% of specificity. Conclusions Distance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively. PMID:23349813

Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System

PubMed Central

Jan, Shau-Shiun; Yeh, Shuo-Ju; Liu, Ya-Wen

2015-01-01

The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging. PMID:26343673

Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System.

PubMed

Jan, Shau-Shiun; Yeh, Shuo-Ju; Liu, Ya-Wen

2015-08-28

The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.

Spatial interpolation of solar irradiation data over complex orography: Solar map of Canaries Islands

NASA Astrophysics Data System (ADS)

Ortegón Gallego, A.

2010-09-01

In this paper, we describe the calculation methodology we used to determine the spatial structure of solar irradiation over a very complex orography, such as the Canary archipelago, that is broken in seven islands, with only 7500 km2, and with heights in some of the islands upper than 1800 m, that reach to 3718 m in the case of Tenerife island. Starting with the method of Cumulative Semivariograms1, already used to face the irradiation spatial interpolation problem, although not for a complex orography. In this sense, some major modifications are introduced to deal with our needs, which can be summarized as: a) interpolation of clearness index data (Kcd, defined as the division of the global horizontal data, between the corresponding clear sky global horizontal values, obtained from a suitable model) instead of solar irradiation data; b) topographic considerations are included in the clear sky model, such as topographics shadows. This impacts directly over direct component of solar irradiation, and has a minor effect over the diffuse component, arising from a non plane visible horizon; c) the meteorological stations are selected by a criteria of weather proximity, instead of geographic proximity as it was proposed in the original methodology of Cumulative Semivariograms; d) the final result is obtained as the composition of various maps obtained from error minimization within a neighborhood of each available station, instead of using irradiation isolines. A preliminary result with data registered only by Canary Islands Institute of Technology's stations, spread over the whole archipelago, is showed. From our results we can see both, the power of the developed methodology and some limitations due to the extremely complex orography as it is the case of Canary Islands, which consists of a wide variety of microclimate regions. Whenever additional information is available, either in the form of empiric knowledge of the local weather, or in the form of other available radiometric data sources, the results do improve. In the case that all available stations are already used, the empirical knowledge of weather conditions can be introduced in our model by means of a strain parameter that modifies the statistical weight associated to the corresponding station in a given neighborhood. On the other hand, whenever it is possible to increase the spatial density of data sources, that is, to incorporate data from others stations, the result improves provided that data quality is good enough. To validate the results obtained with our methodology, we calculate the error between the estimated solar irradiation at the location of the stations with the records obtained by them.

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

Development of one-minute rain-rate and rain-attenuation contour maps for satellite propagation system planning in a subtropical country: South Africa

NASA Astrophysics Data System (ADS)

Ojo, J. S.; Owolawi, P. A.

2014-10-01

Millimeter and microwave system design at higher frequencies require as input a 1-min rain-rate cumulative distribution function for estimating the level of degradation that can be encountered at such frequency bands. Owing to the lack of 1-min rain-rate data in South Africa and the availability of 5-min and hourly rainfall data, we have used rain-rate conversion models and the refined Moupfouma model to convert the available data into 1-min rain-rate statistics. The attenuation caused by these rain rates was predicted using the International Telecommunication Union (ITU) recommendations model. The Kriging interpolation method was used to draw contour maps over different percentages of time for spatial interpolation of rain-rate values into a regular grid in order to obtain a highly consistent and predictable inter-gauge rain-rate variation over South Africa. The present results will be useful for system designers of modern broadband wireless access (BWA) and high-density cell-based Ku/Ka, Q/V band satellite systems, over the desired area of coverage in order to determine the appropriate effective isotropically radiated power (EIRP) and receiver characteristics of this region.

Impacts of urban and industrial development on Arctic land surface temperature in Lower Yenisei River Region.

NASA Astrophysics Data System (ADS)

Li, Z.; Shiklomanov, N. I.

2015-12-01

Urbanization and industrial development have significant impacts on arctic climate that in turn controls settlement patterns and socio-economic processes. In this study we have analyzed the anthropogenic influences on regional land surface temperature of Lower Yenisei River Region of the Russia Arctic. The study area covers two consecutive Landsat scenes and includes three major cities: Norilsk, Igarka and Dudingka. Norilsk industrial region is the largest producer of nickel and palladium in the world, and Igarka and Dudingka are important ports for shipping. We constructed a spatio-temporal interpolated temperature model by including 1km MODIS LST, field-measured climate, Modern Era Retrospective-analysis for Research and Applications (MERRA), DEM, Landsat NDVI and Landsat Land Cover. Those fore-mentioned spatial data have various resolution and coverage in both time and space. We analyzed their relationships and created a monthly spatio-temporal interpolated surface temperature model at 1km resolution from 1980 to 2010. The temperature model then was used to examine the characteristic seasonal LST signatures, related to several representative assemblages of Arctic urban and industrial infrastructure in order to quantify anthropogenic influence on regional surface temperature.

Spatiotemporal interpolation of discharge across a river network by using synthetic SWOT satellite data

NASA Astrophysics Data System (ADS)

Paiva, Rodrigo C. D.; Durand, Michael T.; Hossain, Faisal

2015-01-01

Recent efforts have sought to estimate river discharge and other surface water-related quantities using spaceborne sensors, with better spatial coverage but worse temporal sampling as compared with in situ measurements. The Surface Water and Ocean Topography (SWOT) mission will provide river discharge estimates globally from space. However, questions on how to optimally use the spatially distributed but asynchronous satellite observations to generate continuous fields still exist. This paper presents a statistical model (River Kriging-RK), for estimating discharge time series in a river network in the context of the SWOT mission. RK uses discharge estimates at different locations and times to produce a continuous field using spatiotemporal kriging. A key component of RK is the space-time river discharge covariance, which was derived analytically from the diffusive wave approximation of Saint Venant's equations. The RK covariance also accounts for the loss of correlation at confluences. The model performed well in a case study on Ganges-Brahmaputra-Meghna (GBM) River system in Bangladesh using synthetic SWOT observations. The correlation model reproduced empirically derived values. RK (R2=0.83) outperformed other kriging-based methods (R2=0.80), as well as a simple time series linear interpolation (R2=0.72). RK was used to combine discharge from SWOT and in situ observations, improving estimates when the latter is included (R2=0.91). The proposed statistical concepts may eventually provide a feasible framework to estimate continuous discharge time series across a river network based on SWOT data, other altimetry missions, and/or in situ data.

Accounting for irregular support in spatial interpolation - analysing the effect of using alternative distance measures

NASA Astrophysics Data System (ADS)

Skøien, J. O.; Gottschalk, L.; Leblois, E.

2009-04-01

Whereas geostatistical and objective methods mostly have been developed for observations with point support or a regular support, e.g. runoff related data can be assumed to have an irregular support in space, and sometimes also a temporal support. The correlations between observations and between observations and the prediction location are found through an integration of a point variogram or point correlation function, a method known as regularisation. Being a relatively simple method for observations with equal and regular support, it can be computationally demanding if the observations have irregular support. With improved speed of computers, solving such integrations has become easier, but there can still be numerical problems that are not easily solved even with high-resolution computations. This can particularly be a problem in hydrological sciences where catchments are overlapping, the correlations are high, and small numerical errors can give ill-posed covariance matrices. The problem increases with increasing number of spatial and/or temporal dimensions. Gottschalk [1993a; 1993b] suggested to replace the integration by a Taylor expansion, hence reducing the computation time considerably, and also expecting less numerical problems with the covariance matrices. In practice, the integrated correlation/semivariance between observations are replaced by correlations/semivariances using the so called Ghosh-distance. Although Gottschalk and collaborators have used the Ghosh-distance also in other papers [Sauquet, et al., 2000a; Sauquet, et al., 2000b], the properties of the simplification have not been examined in detail. Hence, we will here analyse the replacement of the integration by the use of Ghosh-distances, both in sense of the ability to reproduce regularised semivariogram and correlation values, and the influence on the final interpolated maps. Comparisons will be performed both for real observations with a support (hydrological data) and for more hypothetical observations with regular supports where analytical expressions for the regularised semivariances/correlations in some cases can be derived. The results indicate that the simplification is useful for spatial interpolation when the support of the observations has to be taken into account. The difference in semivariogram value or correlation value between the simplified method and the full integration is limited on short distances, increasing for larger distances. However, this is to some degree taken into account while fitting a model for the point process, so that the results after interpolation are less affected by the simplification. The method is of particular use if computation time is of importance, e.g. in the case of real-time mapping procedures. Gottschalk, L. (1993a) Correlation and covariance of runoff, Stochastic Hydrology and Hydraulics, 7, 85-101. Gottschalk, L. (1993b) Interpolation of runoff applying objective methods, Stochastic Hydrology and Hydraulics, 7, 269-281. Sauquet, E., L. Gottschalk, and E. Leblois (2000a) Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme, Hydrological Sciences Journal, 45, 799-815. Sauquet, E., I. Krasovskaia, and E. Leblois (2000b) Mapping mean monthly runoff pattern using EOF analysis, Hydrology and Earth System Sciences, 4, 79-93.

Remote-sensing supported monitoring of global biodiversity change

NASA Astrophysics Data System (ADS)

Jetz, W.; Tuanmu, M. N.; W, A.; Melton, F. S.; Parmentier, B.; Amatulli, G.; Guzman, A.

2016-12-01

Remote sensing combined with biodiversity observation offers an unrivalled tool for understanding and predicting species distributions and their changes at the planetary scale. I will illustrate recently developed high-resolution remote-sensing based layers targeted for spatiotemporal biodiversity modeling, addressing climate, environment, topography, and habitat heterogeneity. In particular, I will illustrate the development and use of global MODIS-derived environmental layers for biodiversity assessment and change monitoring. Remote-sensing based capture of these putative predictors of biodiversity dynamics provides more a reliable signal than spatially interpolated layers and avoids inflated spatial autocorrelation. The layers result in more accurate models of species occurrence and are more readily able to address the scale of processes underpinning species distributions, e.g. when combined with emerging hierarchical, cross-scale models. I illustrate the multiple ways in which this type of information, based on continuously collected data, supports the prediction of not just spatial but also temporal variation in biodiversity. Using implementations in the Map of Life infrastructure I will showcase new indicators of species distribution and change that demonstrate these new opportunities.

Compact cell-centered discretization stencils at fine-coarse block structured grid interfaces

NASA Astrophysics Data System (ADS)

Pletzer, Alexander; Jamroz, Ben; Crockett, Robert; Sides, Scott

2014-03-01

Different strategies for coupling fine-coarse grid patches are explored in the context of the adaptive mesh refinement (AMR) method. We show that applying linear interpolation to fill in the fine grid ghost values can produce a finite volume stencil of comparable accuracy to quadratic interpolation provided the cell volumes are adjusted. The volume of fine cells expands whereas the volume of neighboring coarse cells contracts. The amount by which the cells contract/expand depends on whether the interface is a face, an edge, or a corner. It is shown that quadratic or better interpolation is required when the conductivity is spatially varying, anisotropic, the refinement ratio is other than two, or when the fine-coarse interface is concave.

Hydrogeologic Framework of Bedrock Units and Initial Salinity Distribution for a Simulation of Groundwater Flow for the Lake Michigan Basin

USGS Publications Warehouse

Lampe, David C.

2009-01-01

The U.S. Geological Survey is assessing groundwater availability in the Lake Michigan Basin. As part of the assessment, a variable-density groundwater-flow model is being developed to simulate the effects of groundwater use on water availability throughout the basin. The hydrogeologic framework for the Lake Michigan Basin model was developed by grouping the bedrock geology of the study area into hydrogeologic units on the basis of the functioning of each unit as an aquifer or confining layer within the basin. Available data were evaluated based on the areal extent of coverage within the study area, and procedures were established to characterize areas with sparse data coverage. Top and bottom altitudes for each hydrogeologic unit were interpolated in a geographic information system for input to the model and compared with existing maps of subsurface formations. Fourteen bedrock hydrogeologic units, making up 17 bedrock model layers, were defined, and they range in age from the Jurassic Period red beds of central Michigan to the Cambrian Period Mount Simon Sandstone. Information on groundwater salinity in the Lake Michigan Basin was compiled to create an input dataset for the variable-density groundwater-flow simulation. Data presented in this report are referred to as 'salinity data' and are reported in terms of total dissolved solids. Salinity data were not available for each hydrogeologic unit. Available datasets were assigned to a hydrogeologic unit, entered into a spatial database, and data quality was visually evaluated. A geographic information system was used to interpolate salinity distributions for each hydrogeologic unit with available data. Hydrogeologic units with no available data either were set equal to neighboring units or were vertically interpolated by use of values from units above and below.

Geostatistical screening of flood events in the groundwater levels of the diverted inner delta of the Danube River: implications for river bed clogging

NASA Astrophysics Data System (ADS)

Trásy, Balázs; Garamhegyi, Tamás; Laczkó-Dobos, Péter; Kovács, József; Hatvani, István Gábor

2018-04-01

The efficient operation of shallow groundwater (SGW) monitoring networks is crucial to water supply, in-land water protection, agriculture and nature conservation. In the present study, the spatial representativity of such a monitoring network in an area that has been thoroughly impacted by anthropogenic activity (river diversion/damming) is assessed, namely the Szigetköz adjacent to the River Danube. The main aims were to assess the spatial representativity of the SGW monitoring network in different discharge scenarios, and investigate the directional characteristics of this representativity, i.e. establish whether geostatistical anisotropy is present, and investigate how this changes with flooding. After the subtraction of a spatial trend from the time series of 85 shallow groundwater monitoring wells tracking flood events from 2006, 2009 and 2013, variography was conducted on the residuals, and the degree of anisotropy was assessed to explore the spatial autocorrelation structure of the network. Since the raw data proved to be insufficient, an interpolated grid was derived, and the final results were scaled to be representative of the original raw data. It was found that during floods the main direction of the spatial variance of the shallow groundwater monitoring wells alters, from perpendicular to the river to parallel with it for over a period of about two week. However, witht the passing of the flood, this returns to its original orientation in 2 months. It is likely that this process is related first to the fast removal of clogged riverbed strata by the flood, then to their slower replacement. In addition, the study highlights the importance of assessing the direction of the spatial autocorrelation structure of shallow groundwater monitoring networks, especially if the aim is to derive interpolated maps for the further investigation or modeling of flow.

Interpolation bias for the inverse compositional Gauss-Newton algorithm in digital image correlation

NASA Astrophysics Data System (ADS)

Su, Yong; Zhang, Qingchuan; Xu, Xiaohai; Gao, Zeren; Wu, Shangquan

2018-01-01

It is believed that the classic forward additive Newton-Raphson (FA-NR) algorithm and the recently introduced inverse compositional Gauss-Newton (IC-GN) algorithm give rise to roughly equal interpolation bias. Questioning the correctness of this statement, this paper presents a thorough analysis of interpolation bias for the IC-GN algorithm. A theoretical model is built to analytically characterize the dependence of interpolation bias upon speckle image, target image interpolation, and reference image gradient estimation. The interpolation biases of the FA-NR algorithm and the IC-GN algorithm can be significantly different, whose relative difference can exceed 80%. For the IC-GN algorithm, the gradient estimator can strongly affect the interpolation bias; the relative difference can reach 178%. Since the mean bias errors are insensitive to image noise, the theoretical model proposed remains valid in the presence of noise. To provide more implementation details, source codes are uploaded as a supplement.

Markov random field model-based edge-directed image interpolation.

PubMed

Li, Min; Nguyen, Truong Q

2008-07-01

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.

Scale effects on the evapotranspiration estimation over a water-controlled Mediterranean ecosystem and its influence on hydrological modelling

NASA Astrophysics Data System (ADS)

Carpintero, Elisabet; González-Dugo, María P.; José Polo, María; Hain, Christopher; Nieto, Héctor; Gao, Feng; Andreu, Ana; Kustas, William; Anderson, Martha

2017-04-01

The integration of currently available satellite data into surface energy balance models can provide estimates of evapotranspiration (ET) with spatial and temporal resolutions determined by sensor characteristics. The use of data fusion techniques may increase the temporal resolution of these estimates using multiple satellites, providing a more frequent ET monitoring for hydrological purposes. The objective of this work is to analyze the effects of pixel resolution on the estimation of evapotranspiration using different remote sensing platforms, and to provide continuous monitoring of ET over a water-controlled ecosystem, the Holm oak savanna woodland known as dehesa. It is an agroforestry system with a complex canopy structure characterized by widely-spaced oak trees combined with crops, pasture and shrubs. The study was carried out during two years, 2013 and 2014, combining ET estimates at different spatial and temporal resolutions and applying data fusion techniques for a frequent monitoring of water use at fine spatial resolution. A global and daily ET product at 5 km resolution, developed with the ALEXI model using MODIS day-night temperature difference (Anderson et al., 2015a) was used as a starting point. The associated flux disaggregation scheme, DisALEXI (Norman et al., 2003), was later applied to constrain higher resolution ET from both MODIS and Landsat 7/8 images. The Climate Forecast System Reanalysis (CFSR) provided the meteorological data. Finally, a data fusion technique, the STARFM model (Gao et al., 2006), was applied to fuse MODIS and Landsat ET maps in order to obtain daily ET at 30 m resolution. These estimates were validated and analyzed at two different scales: at local scale over a dehesa experimental site and at watershed scale with a predominant Mediterranean oak savanna landscape, both located in Southern Spain. Local ET estimates from the modeling system were validated with measurements provided by an eddy covariance tower installed in the dehesa (38 ° 12 'N, 4 ° 17' W, 736 m a.s.l.). The results supported the ability of ALEXI/DisALEXI model to accurately estimate turbulent and radiative fluxes over this complex landscape, both at 1 Km and at 30 m spatial resolution. The application of the STARFM model gave significant improvement in capturing the spatio-temporal heterogeneity of ET over the different seasons, compared with traditional interpolation methods using MODIS and Landsat ET data. At basin scale, the physically-based distributed hydrological model WiMMed has been applied to evaluate ET estimates. This model focuses on the spatial interpolation of the meteorological variables and the physical modelling of the daily water balance at the cell and watershed scale, using daily streamflow rates measured at the watershed outlet for final comparison.

Conditional probability of rainfall extremes across multiple durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2017-04-01

The conditional probability that extreme rainfall will occur at one location given that it is occurring at another location is critical in engineering design and management circumstances including planning of evacuation routes and the sitting of emergency infrastructure. A challenge with this conditional simulation is that in many situations the interest is not so much the conditional distributions of rainfall of the same duration at two locations, but rather the conditional distribution of flooding in two neighbouring catchments, which may be influenced by rainfall of different critical durations. To deal with this challenge, a model that can consider both spatial and duration dependence of extremes is required. The aim of this research is to develop a model that can take account both spatial dependence and duration dependence into the dependence structure of extreme rainfalls. To achieve this aim, this study is a first attempt at combining extreme rainfall for multiple durations within a spatial extreme model framework based on max-stable process theory. Max-stable processes provide a general framework for modelling multivariate extremes with spatial dependence for just a single duration extreme rainfall. To achieve dependence across multiple timescales, this study proposes a new approach that includes addition elements representing duration dependence of extremes to the covariance matrix of max-stable model. To improve the efficiency of calculation, a re-parameterization proposed by Koutsoyiannis et al. (1998) is used to reduce the number of parameters necessary to be estimated. This re-parameterization enables the GEV parameters to be represented as a function of timescale. A stepwise framework has been adopted to achieve the overall aims of this research. Firstly, the re-parameterization is used to define a new set of common parameters for marginal distribution across multiple durations. Secondly, spatial interpolation of the new parameter set is used to estimate marginal parameters across the full spatial domain. Finally, spatial interpolation result is used as initial condition to estimate dependence parameters via a likelihood function of max-stable model for multiple durations. The Hawkesbury-Nepean catchment near Sydney in Australia was selected as case study for this research. This catchment has 25 sub-daily rain gauges with the minimum record length of 24 years over a region of 300 km × 300 km area. The re-parameterization was applied for each station for durations from 1 hour to 24 hours and then is evaluated by comparing with the at-site fitted GEV. The evaluation showed that the average R2 for all station is around 0.80 with the range from 0.26 to 1.0. The output of re-parameterization then was used to construct the spatial surface based on covariates including longitude, latitude, and elevation. The dependence model showed good agreements between empirical extremal coefficient and theoretical extremal coefficient for multiple durations. For the overall model, a leave-one-out cross-validation for all stations showed it works well for 20 out of 25 stations. The potential application of this model framework was illustrated through a conditional map of return period and return level across multiple durations, both of which are important for engineering design and management.

Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2018-03-01

Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.

Extracting Hydrologic Understanding from the Unique Space-time Sampling of the Surface Water and Ocean Topography (SWOT) Mission

NASA Astrophysics Data System (ADS)

Nickles, C.; Zhao, Y.; Beighley, E.; Durand, M. T.; David, C. H.; Lee, H.

2017-12-01

The Surface Water and Ocean Topography (SWOT) satellite mission is jointly developed by NASA, the French space agency (CNES), with participation from the Canadian and UK space agencies to serve both the hydrology and oceanography communities. The SWOT mission will sample global surface water extents and elevations (lakes/reservoirs, rivers, estuaries, oceans, sea and land ice) at a finer spatial resolution than is currently possible enabling hydrologic discovery, model advancements and new applications that are not currently possible or likely even conceivable. Although the mission will provide global cover, analysis and interpolation of the data generated from the irregular space/time sampling represents a significant challenge. In this study, we explore the applicability of the unique space/time sampling for understanding river discharge dynamics throughout the Ohio River Basin. River network topology, SWOT sampling (i.e., orbit and identified SWOT river reaches) and spatial interpolation concepts are used to quantify the fraction of effective sampling of river reaches each day of the three-year mission. Streamflow statistics for SWOT generated river discharge time series are compared to continuous daily river discharge series. Relationships are presented to transform SWOT generated streamflow statistics to equivalent continuous daily discharge time series statistics intended to support hydrologic applications using low-flow and annual flow duration statistics.

Spatial co-distribution of neglected tropical diseases in the East African Great Lakes region: revisiting the justification for integrated control

PubMed Central

Clements, Archie C. A.; Deville, Marie-Alice; Ndayishimiye, Onésime; Brooker, Simon; Fenwick, Alan

2010-01-01

Summary OBJECTIVE To determine spatial patterns of co-endemicity of schistosomiasis mansoni and the soil-transmitted helminths (STHs) Ascaris lumbricoides, Trichuris trichiura and hookworm in the Great Lakes region of East Africa, to help plan integrated neglected tropical disease programmes in this region. METHOD Parasitological surveys were conducted in Uganda, Tanzania, Kenya and Burundi in 28 213 children in 404 schools. Bayesian geostatistical models were used to interpolate prevalence of these infections across the study area. Interpolated prevalence maps were overlaid to determine areas of co-endemicity. RESULTS In the Great Lakes region, prevalence was 18.1% for Schistosoma mansoni, 50.0% for hookworm, 6.8% for A. lumbricoides and 6.8% for T. trichiura. Hookworm infection was ubiquitous, whereas S. mansoni, A. lumbricoides and T. trichiura were highly focal. Most areas were endemic (prevalence ≥10%) or hyperendemic (prevalence ≥50%) for one or more STHs, whereas endemic areas for schistosomiasis mansoni were restricted to foci adjacent large perennial water bodies. CONCLUSION Because of the ubiquity of hookworm, treatment programmes are required for STH throughout the region but efficient schistosomiasis control should only be targeted at limited high-risk areas. Therefore, integration of schistosomiasis with STH control is only indicated in limited foci in East Africa. PMID:20409287

An adaptive moving finite volume scheme for modeling flood inundation over dry and complex topography

NASA Astrophysics Data System (ADS)

Zhou, Feng; Chen, Guoxian; Huang, Yuefei; Yang, Jerry Zhijian; Feng, Hui

2013-04-01

A new geometrical conservative interpolation on unstructured meshes is developed for preserving still water equilibrium and positivity of water depth at each iteration of mesh movement, leading to an adaptive moving finite volume (AMFV) scheme for modeling flood inundation over dry and complex topography. Unlike traditional schemes involving position-fixed meshes, the iteration process of the AFMV scheme moves a fewer number of the meshes adaptively in response to flow variables calculated in prior solutions and then simulates their posterior values on the new meshes. At each time step of the simulation, the AMFV scheme consists of three parts: an adaptive mesh movement to shift the vertices position, a geometrical conservative interpolation to remap the flow variables by summing the total mass over old meshes to avoid the generation of spurious waves, and a partial differential equations(PDEs) discretization to update the flow variables for a new time step. Five different test cases are presented to verify the computational advantages of the proposed scheme over nonadaptive methods. The results reveal three attractive features: (i) the AMFV scheme could preserve still water equilibrium and positivity of water depth within both mesh movement and PDE discretization steps; (ii) it improved the shock-capturing capability for handling topographic source terms and wet-dry interfaces by moving triangular meshes to approximate the spatial distribution of time-variant flood processes; (iii) it was able to solve the shallow water equations with a relatively higher accuracy and spatial-resolution with a lower computational cost.

Correction of image drift and distortion in a scanning electron microscopy.

PubMed

Jin, P; Li, X

2015-12-01

Continuous research on small-scale mechanical structures and systems has attracted strong demand for ultrafine deformation and strain measurements. Conventional optical microscope cannot meet such requirements owing to its lower spatial resolution. Therefore, high-resolution scanning electron microscope has become the preferred system for high spatial resolution imaging and measurements. However, scanning electron microscope usually is contaminated by distortion and drift aberrations which cause serious errors to precise imaging and measurements of tiny structures. This paper develops a new method to correct drift and distortion aberrations of scanning electron microscope images, and evaluates the effect of correction by comparing corrected images with scanning electron microscope image of a standard sample. The drift correction is based on the interpolation scheme, where a series of images are captured at one location of the sample and perform image correlation between the first image and the consequent images to interpolate the drift-time relationship of scanning electron microscope images. The distortion correction employs the axial symmetry model of charged particle imaging theory to two images sharing with the same location of one object under different imaging fields of view. The difference apart from rigid displacement between the mentioned two images will give distortion parameters. Three-order precision is considered in the model and experiment shows that one pixel maximum correction is obtained for the employed high-resolution electron microscopic system. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Mapping local and global variability in plant trait distributions

DOE PAGES

Butler, Ethan E.; Datta, Abhirup; Flores-Moreno, Habacuc; ...

2017-12-01

Accurate trait-environment relationships and global maps of plant trait distributions represent a needed stepping stone in global biogeography and are critical constraints of key parameters for land models. Here, we use a global data set of plant traits to map trait distributions closely coupled to photosynthesis and foliar respiration: specific leaf area (SLA), and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm); We propose two models to extrapolate geographically sparse point data to continuous spatial surfaces. The first is a categorical model using species mean trait values, categorized into plant functional types (PFTs) and extrapolating to PFT occurrencemore » ranges identified by remote sensing. The second is a Bayesian spatial model that incorporates information about PFT, location and environmental covariates to estimate trait distributions. Both models are further stratified by varying the number of PFTs; The performance of the models was evaluated based on their explanatory and predictive ability. The Bayesian spatial model leveraging the largest number of PFTs produced the best maps; The interpolation of full trait distributions enables a wider diversity of vegetation to be represented across the land surface. These maps may be used as input to Earth System Models and to evaluate other estimates of functional diversity.« less

Mapping local and global variability in plant trait distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Butler, Ethan E.; Datta, Abhirup; Flores-Moreno, Habacuc

Accurate trait-environment relationships and global maps of plant trait distributions represent a needed stepping stone in global biogeography and are critical constraints of key parameters for land models. Here, we use a global data set of plant traits to map trait distributions closely coupled to photosynthesis and foliar respiration: specific leaf area (SLA), and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm); We propose two models to extrapolate geographically sparse point data to continuous spatial surfaces. The first is a categorical model using species mean trait values, categorized into plant functional types (PFTs) and extrapolating to PFT occurrencemore » ranges identified by remote sensing. The second is a Bayesian spatial model that incorporates information about PFT, location and environmental covariates to estimate trait distributions. Both models are further stratified by varying the number of PFTs; The performance of the models was evaluated based on their explanatory and predictive ability. The Bayesian spatial model leveraging the largest number of PFTs produced the best maps; The interpolation of full trait distributions enables a wider diversity of vegetation to be represented across the land surface. These maps may be used as input to Earth System Models and to evaluate other estimates of functional diversity.« less

Estimation of available global solar radiation using sunshine duration over South Korea

NASA Astrophysics Data System (ADS)

Das, Amrita; Park, Jin-ki; Park, Jong-hwa

2015-11-01

Besides designing a solar energy system, accurate insolation data is also a key component for many biological and atmospheric studies. But solar radiation stations are not widely available due to financial and technical limitations; this insufficient number affects the spatial resolution whenever an attempt is made to construct a solar radiation map. There are several models in literature for estimating incoming solar radiation using sunshine fraction. Seventeen of such models among which 6 are linear and 11 non-linear, have been chosen for studying and estimating solar radiation on a horizontal surface over South Korea. The better performance of a non-linear model signifies the fact that the relationship between sunshine duration and clearness index does not follow a straight line. With such a model solar radiation over 79 stations measuring sunshine duration is computed and used as input for spatial interpolation. Finally monthly solar radiation maps are constructed using the Ordinary Kriging method. The cross validation results show good agreement between observed and predicted data.

Wet-season spatial variability of N2O emissions from a tea field in subtropical central China

NASA Astrophysics Data System (ADS)

Fu, X.; Liu, X.; Li, Y.; Shen, J.; Wang, Y.; Zou, G.; Li, H.; Song, L.; Wu, J.

2015-01-01

Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate estimations of N2O emissions from tea-planted soils is challenging due to strong spatial variability. We examined the spatial variability of N2O emissions from a red-soil tea field in Hunan province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers approximately regular gridded in a 4.0 ha tea field. The N2O fluxes for a 30 min snapshot (10-10.30 a.m.) ranged from -1.73 to 1659.11 g N ha-1 d-1 and were positively skewed with an average flux of 102.24 g N ha-1 d-1. The N2O flux data were transformed to a normal distribution by using a logit function. The geostatistical analyses of our data indicated that the logit-transformed N2O fluxes (FLUX30t) exhibited strong spatial autocorrelation, which was characterized by an exponential semivariogram model with an effective range of 25.2 m. As observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt), soil nitrate-N (NO3Nt), soil organic carbon (SOCt), total soil nitrogen (TSNt) were all found to be significantly correlated with FLUX30t (r=0.57-0.71, p<0.001). Three spatial interpolation methods (ordinary kriging, regression kriging and cokriging) were applied to estimate the spatial distribution of N2O emissions over the study area. Cokriging with NH4Nt and NO3Nt as covariables (r= 0.74 and RMSE =1.18) outperformed ordinary kriging (r= 0.18 and RMSE =1.74), regression kriging with the sample position as a predictor (r= 0.49 and RMSE =1.55) and cokriging with SOCt as a covariable (r= 0.58 and RMSE =1.44). The predictions of the three kriging interpolation methods for the total N2O emissions of the 4.0 ha tea field ranged from 148.2 to 208.1 g N d-1, based on the 30 min snapshots obtained during the wet season. Our findings suggested that to accurately estimate the total N2O emissions over a region, the environmental variables (e.g., soil properties) and the current land use pattern (e.g., tea row transects in the present study) must be included in spatial interpolation. Additionally, compared with other kriging approaches, the cokriging prediction approach showed great advantages in being easily deployed, and more importantly providing accurate regional estimation of N2O emissions from tea-planted soils.

Wet-season spatial variability in N2O emissions from a tea field in subtropical central China

NASA Astrophysics Data System (ADS)

Fu, X.; Liu, X.; Li, Y.; Shen, J.; Wang, Y.; Zou, G.; Li, H.; Song, L.; Wu, J.

2015-06-01

Tea fields emit large amounts of nitrous oxide (N2O) to the atmosphere. Obtaining accurate estimations of N2O emissions from tea-planted soils is challenging due to strong spatial variability. We examined the spatial variability in N2O emissions from a red-soil tea field in Hunan Province, China, on 22 April 2012 (in a wet season) using 147 static mini chambers approximately regular gridded in a 4.0 ha tea field. The N2O fluxes for a 30 min snapshot (10:00-10:30 a.m.) ranged from -1.73 to 1659.11 g N ha-1 d-1 and were positively skewed with an average flux of 102.24 g N ha-1 d-1. The N2O flux data were transformed to a normal distribution by using a logit function. The geostatistical analyses of our data indicated that the logit-transformed N2O fluxes (FLUX30t) exhibited strong spatial autocorrelation, which was characterized by an exponential semivariogram model with an effective range of 25.2 m. As observed in the wet season, the logit-transformed soil ammonium-N (NH4Nt), soil nitrate-N (NO3Nt), soil organic carbon (SOCt) and total soil nitrogen (TSNt) were all found to be significantly correlated with FLUX30t (r = 0.57-0.71, p < 0.001). Three spatial interpolation methods (ordinary kriging, regression kriging and cokriging) were applied to estimate the spatial distribution of N2O emissions over the study area. Cokriging with NH4Nt and NO3Nt as covariables (r = 0.74 and RMSE = 1.18) outperformed ordinary kriging (r = 0.18 and RMSE = 1.74), regression kriging with the sample position as a predictor (r = 0.49 and RMSE = 1.55) and cokriging with SOCt as a covariable (r = 0.58 and RMSE = 1.44). The predictions of the three kriging interpolation methods for the total N2O emissions of 4.0 ha tea field ranged from 148.2 to 208.1 g N d-1, based on the 30 min snapshots obtained during the wet season. Our findings suggested that to accurately estimate the total N2O emissions over a region, the environmental variables (e.g., soil properties) and the current land use pattern (e.g., tea row transects in the present study) must be included in spatial interpolation. Additionally, compared with other kriging approaches, the cokriging prediction approach showed great advantages in being easily deployed and, more importantly, providing accurate regional estimation of N2O emissions from tea-planted soils.

Mapping the spatial distribution of chloride deposition across Australia

NASA Astrophysics Data System (ADS)

Davies, P. J.; Crosbie, R. S.

2018-06-01

The high solubility and conservative behaviour of chloride make it ideal for use as an environmental tracer of water and salt movement through the hydrologic cycle. For such use the spatial distribution of chloride deposition in rainfall at a suitable scale must be known. A number of authors have used point data acquired from field studies of chloride deposition around Australia to construct relationships to characterise chloride deposition as a function of distance from the coast; these relationships have allowed chloride deposition to be interpolated in different regions around Australia. In this paper we took this a step further and developed a chloride deposition map for all of Australia which includes a quantification of uncertainty. A previously developed four parameter model of chloride deposition as a function of distance from the coast for Australia was used as the basis for producing a continental scale chloride deposition map. Each of the four model parameters were made spatially variable by creating parameter surfaces that were interpolated using a pilot point regularisation approach within a parameter estimation software. The observations of chloride deposition were drawn from a literature review that identified 291 point measurements of chloride deposition over a period of 80 years spread unevenly across all Australian States and Territories. A best estimate chloride deposition map was developed from the resulting surfaces on a 0.05 degree grid. The uncertainty in the chloride deposition map was quantified as the 5th and 95th percentile of 1000 calibrated models produced via Null Space Monte Carlo analysis and the spatial variability of chloride deposition across the continent was consistent with landscape morphology. The temporal variability in chloride deposition on a decadal scale was investigated in the Murray-Darling Basin, this highlighted the need for long-term monitoring of chloride deposition if the uncertainty of the continental scale map is to be reduced. Use of the derived chloride deposition map was demonstrated for a probabilistic estimation of groundwater recharge for the southeast of South Australia using the chloride mass balance method.

Assessing Fire Weather Index using statistical downscaling and spatial interpolation techniques in Greece

NASA Astrophysics Data System (ADS)

Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana

2013-04-01

Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical downscaling and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical downscaling methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical downscaling portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/downscaling) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to downscale non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue downscaling technique was directly performed to the FWI index values and secondly the same downscaling technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical downscaling portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index. Results from this method were compared with the statistical downscaling results obtained from the portal. Finally, FWI was computed using weather observations obtained from the Hellenic National Meteorological Service, mainly in the south continental part of Greece and a comparison with the previous results was performed.

Modelling the spatial distribution of Fasciola hepatica in dairy cattle in Europe.

PubMed

Ducheyne, Els; Charlier, Johannes; Vercruysse, Jozef; Rinaldi, Laura; Biggeri, Annibale; Demeler, Janina; Brandt, Christina; De Waal, Theo; Selemetas, Nikolaos; Höglund, Johan; Kaba, Jaroslaw; Kowalczyk, Slawomir J; Hendrickx, Guy

2015-03-26

A harmonized sampling approach in combination with spatial modelling is required to update current knowledge of fasciolosis in dairy cattle in Europe. Within the scope of the EU project GLOWORM, samples from 3,359 randomly selected farms in 849 municipalities in Belgium, Germany, Ireland, Poland and Sweden were collected and their infection status assessed using an indirect bulk tank milk (BTM) enzyme-linked immunosorbent assay (ELISA). Dairy farms were considered exposed when the optical density ratio (ODR) exceeded the 0.3 cut-off. Two ensemble-modelling techniques, Random Forests (RF) and Boosted Regression Trees (BRT), were used to obtain the spatial distribution of the probability of exposure to Fasciola hepatica using remotely sensed environmental variables (1-km spatial resolution) and interpolated values from meteorological stations as predictors. The median ODRs amounted to 0.31, 0.12, 0.54, 0.25 and 0.44 for Belgium, Germany, Ireland, Poland and southern Sweden, respectively. Using the 0.3 threshold, 571 municipalities were categorized as positive and 429 as negative. RF was seen as capable of predicting the spatial distribution of exposure with an area under the receiver operation characteristic (ROC) curve (AUC) of 0.83 (0.96 for BRT). Both models identified rainfall and temperature as the most important factors for probability of exposure. Areas of high and low exposure were identified by both models, with BRT better at discriminating between low-probability and high-probability exposure; this model may therefore be more useful in practise. Given a harmonized sampling strategy, it should be possible to generate robust spatial models for fasciolosis in dairy cattle in Europe to be used as input for temporal models and for the detection of deviations in baseline probability. Further research is required for model output in areas outside the eco-climatic range investigated.

A Quadtree-gridding LBM with Immersed Boundary for Two-dimension Viscous Flows

NASA Astrophysics Data System (ADS)

Yao, Jieke; Feng, Wenliang; Chen, Bin; Zhou, Wei; Cao, Shikun

2017-07-01

An un-uniform quadtree grids lattice Boltzmann method (LBM) with immersed boundary is presented in this paper. In overlapping for different level grids, temporal and spatial interpolation are necessary to ensure the continuity of physical quantity. In order to take advantage of the equation for temporal and spatial step in the same level grids, equal interval interpolation, which is simple to apply to any refined boundary grids in the LBM, is adopted in temporal and spatial aspects to obtain second-order accuracy. The velocity correction, which can guarantee more preferably no-slip boundary condition than the direct forcing method and the momentum exchange method in the traditional immersed-boundary LBM, is used for solid boundary to make the best of Cartesian grid. In present quadtree-gridding immersed-boundary LBM, large eddy simulation (LES) is adopted to simulate the flows over obstacle in higher Reynolds number (Re). The incompressible viscous flows over circular cylinder are carried out, and a great agreement is obtained.

Interpolative modeling of GaAs FET S-parameter data bases for use in Monte Carlo simulations

NASA Technical Reports Server (NTRS)

Campbell, L.; Purviance, J.

1992-01-01

A statistical interpolation technique is presented for modeling GaAs FET S-parameter measurements for use in the statistical analysis and design of circuits. This is accomplished by interpolating among the measurements in a GaAs FET S-parameter data base in a statistically valid manner.

Quadratic polynomial interpolation on triangular domain

NASA Astrophysics Data System (ADS)

Li, Ying; Zhang, Congcong; Yu, Qian

2018-04-01

In the simulation of natural terrain, the continuity of sample points are not in consonance with each other always, traditional interpolation methods often can't faithfully reflect the shape information which lie in data points. So, a new method for constructing the polynomial interpolation surface on triangular domain is proposed. Firstly, projected the spatial scattered data points onto a plane and then triangulated them; Secondly, A C1 continuous piecewise quadric polynomial patch was constructed on each vertex, all patches were required to be closed to the line-interpolation one as far as possible. Lastly, the unknown quantities were gotten by minimizing the object functions, and the boundary points were treated specially. The result surfaces preserve as many properties of data points as possible under conditions of satisfying certain accuracy and continuity requirements, not too convex meantime. New method is simple to compute and has a good local property, applicable to shape fitting of mines and exploratory wells and so on. The result of new surface is given in experiments.

Voxel inversion of airborne electromagnetic data for improved model integration

NASA Astrophysics Data System (ADS)

Fiandaca, Gianluca; Auken, Esben; Kirkegaard, Casper; Vest Christiansen, Anders

2014-05-01

Inversion of electromagnetic data has migrated from single site interpretations to inversions including entire surveys using spatial constraints to obtain geologically reasonable results. Though, the model space is usually linked to the actual observation points. For airborne electromagnetic (AEM) surveys the spatial discretization of the model space reflects the flight lines. On the contrary, geological and groundwater models most often refer to a regular voxel grid, not correlated to the geophysical model space, and the geophysical information has to be relocated for integration in (hydro)geological models. We have developed a new geophysical inversion algorithm working directly in a voxel grid disconnected from the actual measuring points, which then allows for informing directly geological/hydrogeological models. The new voxel model space defines the soil properties (like resistivity) on a set of nodes, and the distribution of the soil properties is computed everywhere by means of an interpolation function (e.g. inverse distance or kriging). Given this definition of the voxel model space, the 1D forward responses of the AEM data are computed as follows: 1) a 1D model subdivision, in terms of model thicknesses, is defined for each 1D data set, creating "virtual" layers. 2) the "virtual" 1D models at the sounding positions are finalized by interpolating the soil properties (the resistivity) in the center of the "virtual" layers. 3) the forward response is computed in 1D for each "virtual" model. We tested the new inversion scheme on an AEM survey carried out with the SkyTEM system close to Odder, in Denmark. The survey comprises 106054 dual mode AEM soundings, and covers an area of approximately 13 km X 16 km. The voxel inversion was carried out on a structured grid of 260 X 325 X 29 xyz nodes (50 m xy spacing), for a total of 2450500 inversion parameters. A classical spatially constrained inversion (SCI) was carried out on the same data set, using 106054 spatially constrained 1D models with 29 layers. For comparison, the SCI inversion models have been gridded on the same grid of the voxel inversion. The new voxel inversion and the classic SCI give similar data fit and inversion models. The voxel inversion decouples the geophysical model from the position of acquired data, and at the same time fits the data as well as the classic SCI inversion. Compared to the classic approach, the voxel inversion is better suited for informing directly (hydro)geological models and for sequential/Joint/Coupled (hydro)geological inversion. We believe that this new approach will facilitate the integration of geophysics, geology and hydrology for improved groundwater and environmental management.

Volumetric three-dimensional intravascular ultrasound visualization using shape-based nonlinear interpolation

PubMed Central

2013-01-01

Background Intravascular ultrasound (IVUS) is a standard imaging modality for identification of plaque formation in the coronary and peripheral arteries. Volumetric three-dimensional (3D) IVUS visualization provides a powerful tool to overcome the limited comprehensive information of 2D IVUS in terms of complex spatial distribution of arterial morphology and acoustic backscatter information. Conventional 3D IVUS techniques provide sub-optimal visualization of arterial morphology or lack acoustic information concerning arterial structure due in part to low quality of image data and the use of pixel-based IVUS image reconstruction algorithms. In the present study, we describe a novel volumetric 3D IVUS reconstruction algorithm to utilize IVUS signal data and a shape-based nonlinear interpolation. Methods We developed an algorithm to convert a series of IVUS signal data into a fully volumetric 3D visualization. Intermediary slices between original 2D IVUS slices were generated utilizing the natural cubic spline interpolation to consider the nonlinearity of both vascular structure geometry and acoustic backscatter in the arterial wall. We evaluated differences in image quality between the conventional pixel-based interpolation and the shape-based nonlinear interpolation methods using both virtual vascular phantom data and in vivo IVUS data of a porcine femoral artery. Volumetric 3D IVUS images of the arterial segment reconstructed using the two interpolation methods were compared. Results In vitro validation and in vivo comparative studies with the conventional pixel-based interpolation method demonstrated more robustness of the shape-based nonlinear interpolation algorithm in determining intermediary 2D IVUS slices. Our shape-based nonlinear interpolation demonstrated improved volumetric 3D visualization of the in vivo arterial structure and more realistic acoustic backscatter distribution compared to the conventional pixel-based interpolation method. Conclusions This novel 3D IVUS visualization strategy has the potential to improve ultrasound imaging of vascular structure information, particularly atheroma determination. Improved volumetric 3D visualization with accurate acoustic backscatter information can help with ultrasound molecular imaging of atheroma component distribution. PMID:23651569

Spatial interpolation of forest conditions using co-conditional geostatistical simulation

Treesearch

H. Todd Mowrer

2000-01-01

In recent work the author used the geostatistical Monte Carlo technique of sequential Gaussian simulation (s.G.s.) to investigate uncertainty in a GIS analysis of potential old-growth forest areas. The current study compares this earlier technique to that of co-conditional simulation, wherein the spatial cross-correlations between variables are included. As in the...

A general method for generating bathymetric data for hydrodynamic computer models

USGS Publications Warehouse

Burau, J.R.; Cheng, R.T.

1989-01-01

To generate water depth data from randomly distributed bathymetric data for numerical hydrodymamic models, raw input data from field surveys, water depth data digitized from nautical charts, or a combination of the two are sorted to given an ordered data set on which a search algorithm is used to isolate data for interpolation. Water depths at locations required by hydrodynamic models are interpolated from the bathymetric data base using linear or cubic shape functions used in the finite-element method. The bathymetric database organization and preprocessing, the search algorithm used in finding the bounding points for interpolation, the mathematics of the interpolation formulae, and the features of the automatic generation of water depths at hydrodynamic model grid points are included in the analysis. This report includes documentation of two computer programs which are used to: (1) organize the input bathymetric data; and (2) to interpolate depths for hydrodynamic models. An example of computer program operation is drawn from a realistic application to the San Francisco Bay estuarine system. (Author 's abstract)

On the sub-model errors of a generalized one-way coupling scheme for linking models at different scales

NASA Astrophysics Data System (ADS)

Zeng, Jicai; Zha, Yuanyuan; Zhang, Yonggen; Shi, Liangsheng; Zhu, Yan; Yang, Jinzhong

2017-11-01

Multi-scale modeling of the localized groundwater flow problems in a large-scale aquifer has been extensively investigated under the context of cost-benefit controversy. An alternative is to couple the parent and child models with different spatial and temporal scales, which may result in non-trivial sub-model errors in the local areas of interest. Basically, such errors in the child models originate from the deficiency in the coupling methods, as well as from the inadequacy in the spatial and temporal discretizations of the parent and child models. In this study, we investigate the sub-model errors within a generalized one-way coupling scheme given its numerical stability and efficiency, which enables more flexibility in choosing sub-models. To couple the models at different scales, the head solution at parent scale is delivered downward onto the child boundary nodes by means of the spatial and temporal head interpolation approaches. The efficiency of the coupling model is improved either by refining the grid or time step size in the parent and child models, or by carefully locating the sub-model boundary nodes. The temporal truncation errors in the sub-models can be significantly reduced by the adaptive local time-stepping scheme. The generalized one-way coupling scheme is promising to handle the multi-scale groundwater flow problems with complex stresses and heterogeneity.

Real-time image-based B-mode ultrasound image simulation of needles using tensor-product interpolation.

PubMed

Zhu, Mengchen; Salcudean, Septimiu E

2011-07-01

In this paper, we propose an interpolation-based method for simulating rigid needles in B-mode ultrasound images in real time. We parameterize the needle B-mode image as a function of needle position and orientation. We collect needle images under various spatial configurations in a water-tank using a needle guidance robot. Then we use multidimensional tensor-product interpolation to simulate images of needles with arbitrary poses and positions using collected images. After further processing, the interpolated needle and seed images are superimposed on top of phantom or tissue image backgrounds. The similarity between the simulated and the real images is measured using a correlation metric. A comparison is also performed with in vivo images obtained during prostate brachytherapy. Our results, carried out for both the convex (transverse plane) and linear (sagittal/para-sagittal plane) arrays of a trans-rectal transducer indicate that our interpolation method produces good results while requiring modest computing resources. The needle simulation method we present can be extended to the simulation of ultrasound images of other wire-like objects. In particular, we have shown that the proposed approach can be used to simulate brachytherapy seeds.

Effects of sampling interval on spatial patterns and statistics of watershed nitrogen concentration

USGS Publications Warehouse

Wu, S.-S.D.; Usery, E.L.; Finn, M.P.; Bosch, D.D.

2009-01-01

This study investigates how spatial patterns and statistics of a 30 m resolution, model-simulated, watershed nitrogen concentration surface change with sampling intervals from 30 m to 600 m for every 30 m increase for the Little River Watershed (Georgia, USA). The results indicate that the mean, standard deviation, and variogram sills do not have consistent trends with increasing sampling intervals, whereas the variogram ranges remain constant. A sampling interval smaller than or equal to 90 m is necessary to build a representative variogram. The interpolation accuracy, clustering level, and total hot spot areas show decreasing trends approximating a logarithmic function. The trends correspond to the nitrogen variogram and start to level at a sampling interval of 360 m, which is therefore regarded as a critical spatial scale of the Little River Watershed. Copyright ?? 2009 by Bellwether Publishing, Ltd. All right reserved.

Data assimilation and model evaluation experiment datasets

NASA Technical Reports Server (NTRS)

Lai, Chung-Cheng A.; Qian, Wen; Glenn, Scott M.

1994-01-01

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Interpolation of property-values between electron numbers is inconsistent with ensemble averaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miranda-Quintana, Ramón Alain; Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario L8S 4M1; Ayers, Paul W.

2016-06-28

In this work we explore the physical foundations of models that study the variation of the ground state energy with respect to the number of electrons (E vs. N models), in terms of general grand-canonical (GC) ensemble formulations. In particular, we focus on E vs. N models that interpolate the energy between states with integer number of electrons. We show that if the interpolation of the energy corresponds to a GC ensemble, it is not differentiable. Conversely, if the interpolation is smooth, then it cannot be formulated as any GC ensemble. This proves that interpolation of electronic properties between integermore » electron numbers is inconsistent with any form of ensemble averaging. This emphasizes the role of derivative discontinuities and the critical role of a subsystem’s surroundings in determining its properties.« less

Modelling the Velocity Field in a Regular Grid in the Area of Poland on the Basis of the Velocities of European Permanent Stations

NASA Astrophysics Data System (ADS)

Bogusz, Janusz; Kłos, Anna; Grzempowski, Piotr; Kontny, Bernard

2014-06-01

The paper presents the results of testing the various methods of permanent stations' velocity residua interpolation in a regular grid, which constitutes a continuous model of the velocity field in the territory of Poland. Three packages of software were used in the research from the point of view of interpolation: GMT ( The Generic Mapping Tools), Surfer and ArcGIS. The following methods were tested in the softwares: the Nearest Neighbor, Triangulation (TIN), Spline Interpolation, Surface, Inverse Distance to a Power, Minimum Curvature and Kriging. The presented research used the absolute velocities' values expressed in the ITRF2005 reference frame and the intraplate velocities related to the NUVEL model of over 300 permanent reference stations of the EPN and ASG-EUPOS networks covering the area of Europe. Interpolation for the area of Poland was done using data from the whole area of Europe to make the results at the borders of the interpolation area reliable. As a result of this research, an optimum method of such data interpolation was developed. All the mentioned methods were tested for being local or global, for the possibility to compute errors of the interpolated values, for explicitness and fidelity of the interpolation functions or the smoothing mode. In the authors' opinion, the best data interpolation method is Kriging with the linear semivariogram model run in the Surfer programme because it allows for the computation of errors in the interpolated values and it is a global method (it distorts the results in the least way). Alternately, it is acceptable to use the Minimum Curvature method. Empirical analysis of the interpolation results obtained by means of the two methods showed that the results are identical. The tests were conducted using the intraplate velocities of the European sites. Statistics in the form of computing the minimum, maximum and mean values of the interpolated North and East components of the velocity residuum were prepared for all the tested methods, and each of the resulting continuous velocity fields was visualized by means of the GMT programme. The interpolated components of the velocities and their residua are presented in the form of tables and bar diagrams.

Missing RRI interpolation for HRV analysis using locally-weighted partial least squares regression.

PubMed

Kamata, Keisuke; Fujiwara, Koichi; Yamakawa, Toshiki; Kano, Manabu

2016-08-01

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV). Since HRV reflects autonomic nervous function, HRV-based health monitoring services, such as stress estimation, drowsy driving detection, and epileptic seizure prediction, have been proposed. In these HRV-based health monitoring services, precise R wave detection from ECG is required; however, R waves cannot always be detected due to ECG artifacts. Missing RRI data should be interpolated appropriately for HRV analysis. The present work proposes a missing RRI interpolation method by utilizing using just-in-time (JIT) modeling. The proposed method adopts locally weighted partial least squares (LW-PLS) for RRI interpolation, which is a well-known JIT modeling method used in the filed of process control. The usefulness of the proposed method was demonstrated through a case study of real RRI data collected from healthy persons. The proposed JIT-based interpolation method could improve the interpolation accuracy in comparison with a static interpolation method.

Calibration of the 2D Hydrodynamic Model Floodos and Implications of Distributed Friction on Sediment Transport Capacity

NASA Astrophysics Data System (ADS)

Croissant, T.; Lague, D.; Davy, P.

2014-12-01

Numerical models of floodplain dynamics often use a simplified 1D description of flow hydraulics and sediment transport that cannot fully account for differential friction between vegetated banks and low friction in the main channel. Key parameters of such models are the friction coefficient and the description of the channel bathymetry which strongly influence predicted water depth and velocity, and therefore sediment transport capacity. In this study, we use a newly developed 2D hydrodynamic model, Floodos, whose efficiency is a major advantage for exploring channel morphodynamics from a flood event to millennial time scales. We evaluate the quality of Floodos predictions in the Whataroa river, New Zealand and assess the effect of a spatially distributed friction coefficient (SDFC) on long term sediment transport. Predictions from the model are compared to water depth data from a gauging station located on the Whataroa River in Southern Alps, New Zealand. The Digital Elevation Model (DEM) of the 2.5 km long studied reach is derived from a 2010 LiDAR acquisition with 2 m resolution and an interpolated bathymetry. The several large floods experienced by this river during 2010 allow us to access water depth for a wide range of possible river discharges and to retrieve the scaling between these two parameters. The high resolution DEM used has a non-negligible part of submerged bathymetry that airborne LiDAR was not able to capture. Bathymetry can be reconstructed by interpolation methods that introduce several uncertainties concerning water depth predictions. We address these uncertainties inherent to the interpolation using a simplified channel with a geometry (slope and width) similar to the Whataroa river. We then explore the effect of a SDFC on velocity pattern, water depth and sediment transport capacity and discuss its relevance on long term predictions of sediment transport and channel morphodynamics.

An integral conservative gridding--algorithm using Hermitian curve interpolation.

PubMed

Volken, Werner; Frei, Daniel; Manser, Peter; Mini, Roberto; Born, Ernst J; Fix, Michael K

2008-11-07

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).

Single-Image Super-Resolution Based on Rational Fractal Interpolation.

PubMed

Zhang, Yunfeng; Fan, Qinglan; Bao, Fangxun; Liu, Yifang; Zhang, Caiming

2018-08-01

This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.

Implementation of higher-order vertical finite elements in ISSM v4.13 for improved ice sheet flow modeling over paleoclimate timescales

NASA Astrophysics Data System (ADS)

Cuzzone, Joshua K.; Morlighem, Mathieu; Larour, Eric; Schlegel, Nicole; Seroussi, Helene

2018-05-01

Paleoclimate proxies are being used in conjunction with ice sheet modeling experiments to determine how the Greenland ice sheet responded to past changes, particularly during the last deglaciation. Although these comparisons have been a critical component in our understanding of the Greenland ice sheet sensitivity to past warming, they often rely on modeling experiments that favor minimizing computational expense over increased model physics. Over Paleoclimate timescales, simulating the thermal structure of the ice sheet has large implications on the modeled ice viscosity, which can feedback onto the basal sliding and ice flow. To accurately capture the thermal field, models often require a high number of vertical layers. This is not the case for the stress balance computation, however, where a high vertical resolution is not necessary. Consequently, since stress balance and thermal equations are generally performed on the same mesh, more time is spent on the stress balance computation than is otherwise necessary. For these reasons, running a higher-order ice sheet model (e.g., Blatter-Pattyn) over timescales equivalent to the paleoclimate record has not been possible without incurring a large computational expense. To mitigate this issue, we propose a method that can be implemented within ice sheet models, whereby the vertical interpolation along the z axis relies on higher-order polynomials, rather than the traditional linear interpolation. This method is tested within the Ice Sheet System Model (ISSM) using quadratic and cubic finite elements for the vertical interpolation on an idealized case and a realistic Greenland configuration. A transient experiment for the ice thickness evolution of a single-dome ice sheet demonstrates improved accuracy using the higher-order vertical interpolation compared to models using the linear vertical interpolation, despite having fewer degrees of freedom. This method is also shown to improve a model's ability to capture sharp thermal gradients in an ice sheet particularly close to the bed, when compared to models using a linear vertical interpolation. This is corroborated in a thermal steady-state simulation of the Greenland ice sheet using a higher-order model. In general, we find that using a higher-order vertical interpolation decreases the need for a high number of vertical layers, while dramatically reducing model runtime for transient simulations. Results indicate that when using a higher-order vertical interpolation, runtimes for a transient ice sheet relaxation are upwards of 5 to 7 times faster than using a model which has a linear vertical interpolation, and this thus requires a higher number of vertical layers to achieve a similar result in simulated ice volume, basal temperature, and ice divide thickness. The findings suggest that this method will allow higher-order models to be used in studies investigating ice sheet behavior over paleoclimate timescales at a fraction of the computational cost than would otherwise be needed for a model using a linear vertical interpolation.

Analysis of spatial and temporal rainfall trends in Sicily during the 1921-2012 period

NASA Astrophysics Data System (ADS)

Liuzzo, Lorena; Bono, Enrico; Sammartano, Vincenzo; Freni, Gabriele

2016-10-01

Precipitation patterns worldwide are changing under the effects of global warming. The impacts of these changes could dramatically affect the hydrological cycle and, consequently, the availability of water resources. In order to improve the quality and reliability of forecasting models, it is important to analyse historical precipitation data to account for possible future changes. For these reasons, a large number of studies have recently been carried out with the aim of investigating the existence of statistically significant trends in precipitation at different spatial and temporal scales. In this paper, the existence of statistically significant trends in rainfall from observational datasets, which were measured by 245 rain gauges over Sicily (Italy) during the 1921-2012 period, was investigated. Annual, seasonal and monthly time series were examined using the Mann-Kendall non-parametric statistical test to detect statistically significant trends at local and regional scales, and their significance levels were assessed. Prior to the application of the Mann-Kendall test, the historical dataset was completed using a geostatistical spatial interpolation technique, the residual ordinary kriging, and then processed to remove the influence of serial correlation on the test results, applying the procedure of trend-free pre-whitening. Once the trends at each site were identified, the spatial patterns of the detected trends were examined using spatial interpolation techniques. Furthermore, focusing on the 30 years from 1981 to 2012, the trend analysis was repeated with the aim of detecting short-term trends or possible changes in the direction of the trends. Finally, the effect of climate change on the seasonal distribution of rainfall during the year was investigated by analysing the trend in the precipitation concentration index. The application of the Mann-Kendall test to the rainfall data provided evidence of a general decrease in precipitation in Sicily during the 1921-2012 period. Downward trends frequently occurred during the autumn and winter months. However, an increase in total annual precipitation was detected during the period from 1981 to 2012.

Big Data Geo-Analytical Tool Development for Spatial Analysis Uncertainty Visualization and Quantification Needs

NASA Astrophysics Data System (ADS)

Rose, K.; Bauer, J. R.; Baker, D. V.

2015-12-01

As big data computing capabilities are increasingly paired with spatial analytical tools and approaches, there is a need to ensure uncertainty associated with the datasets used in these analyses is adequately incorporated and portrayed in results. Often the products of spatial analyses, big data and otherwise, are developed using discontinuous, sparse, and often point-driven data to represent continuous phenomena. Results from these analyses are generally presented without clear explanations of the uncertainty associated with the interpolated values. The Variable Grid Method (VGM) offers users with a flexible approach designed for application to a variety of analyses where users there is a need to study, evaluate, and analyze spatial trends and patterns while maintaining connection to and communicating the uncertainty in the underlying spatial datasets. The VGM outputs a simultaneous visualization representative of the spatial data analyses and quantification of underlying uncertainties, which can be calculated using data related to sample density, sample variance, interpolation error, uncertainty calculated from multiple simulations. In this presentation we will show how we are utilizing Hadoop to store and perform spatial analysis through the development of custom Spark and MapReduce applications that incorporate ESRI Hadoop libraries. The team will present custom 'Big Data' geospatial applications that run on the Hadoop cluster and integrate with ESRI ArcMap with the team's probabilistic VGM approach. The VGM-Hadoop tool has been specially built as a multi-step MapReduce application running on the Hadoop cluster for the purpose of data reduction. This reduction is accomplished by generating multi-resolution, non-overlapping, attributed topology that is then further processed using ESRI's geostatistical analyst to convey a probabilistic model of a chosen study region. Finally, we will share our approach for implementation of data reduction and topology generation via custom multi-step Hadoop applications, performance benchmarking comparisons, and Hadoop-centric opportunities for greater parallelization of geospatial operations. The presentation includes examples of the approach being applied to a range of subsurface, geospatial studies (e.g. induced seismicity risk).

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Evaluating interception of larval pallid sturgeon on the Lower Missouri River- data acquisition, interpolation, and visualization

NASA Astrophysics Data System (ADS)

Bulliner, E. A., IV; Erwin, S. O.; Anderson, B. J.; Wilson, H.; Jacobson, R. B.

2016-12-01

The transition from endogenous to exogenous feeding is an important life-stage transition for many riverine fish larvae. On the Missouri River, U.S., riverine alteration has decreased connectivity between the navigation channel and complex, food-producing and foraging areas on the channel margins, namely shallow side channels and sandbar complexes. A favored hypothesis, the interception hypothesis, for recruitment failure of pallid sturgeon is that drifting larvae are not able to exit the highly engineered navigation channel, and therefore starve. We present work exploring measures of hydraulic connectivity between the navigation channel and channel margins using multiple data-collection protocols with acoustic Doppler current profilers (ADCPs). As ADCP datasets alone often do not have high enough spatial resolution to characterize interception and connectivity sufficiently at the scale of drifting sturgeon larvae, they are often supplemented with physical and empirical models. Using boat-mounted ADCPs, we collected 3-dimensional current velocities with a variety of driving techniques (specifically, regularly spaced transects, reciprocal transects, and irregular patterns) around areas of potential larval interception. We then used toolkits based in Python to interpolate 3-dimensional velocity fields at spatial scales finer than the original measurements, and visualized resultant velocity vectors and flowlines in the software package Paraview. Using these visualizations, we investigated the necessary resolution of field measurements required to model connectivity with channel margin areas on large, highly engineered river ecosystems such as the Missouri River. We anticipate that results from this work will be used to help inform models of larval interception under current conditions. Furthermore, results from this work will be useful in developing monitoring strategies to evaluate the restoration of channel complexity to support ecological functions.

A novel interpolation approach for the generation of 3D-geometric digital bone models from image stacks

PubMed Central

Mittag, U.; Kriechbaumer, A.; Rittweger, J.

2017-01-01

The authors propose a new 3D interpolation algorithm for the generation of digital geometric 3D-models of bones from existing image stacks obtained by peripheral Quantitative Computed Tomography (pQCT) or Magnetic Resonance Imaging (MRI). The technique is based on the interpolation of radial gray value profiles of the pQCT cross sections. The method has been validated by using an ex-vivo human tibia and by comparing interpolated pQCT images with images from scans taken at the same position. A diversity index of <0.4 (1 meaning maximal diversity) even for the structurally complex region of the epiphysis, along with the good agreement of mineral-density-weighted cross-sectional moment of inertia (CSMI), demonstrate the high quality of our interpolation approach. Thus the authors demonstrate that this interpolation scheme can substantially improve the generation of 3D models from sparse scan sets, not only with respect to the outer shape but also with respect to the internal gray-value derived material property distribution. PMID:28574415

Spatial response surface modelling in the presence of data paucity for the evaluation of potential human health risk due to the contamination of potable water resources.

PubMed

Liu, Shen; McGree, James; Hayes, John F; Goonetilleke, Ashantha

2016-10-01

Potential human health risk from waterborne diseases arising from unsatisfactory performance of on-site wastewater treatment systems is driven by landscape factors such as topography, soil characteristics, depth to water table, drainage characteristics and the presence of surface water bodies. These factors are present as random variables which are spatially distributed across a region. A methodological framework is presented that can be applied to model and evaluate the influence of various factors on waterborne disease potential. This framework is informed by spatial data and expert knowledge. For prediction at unsampled sites, interpolation methods were used to derive a spatially smoothed surface of disease potential which takes into account the uncertainty due to spatial variation at any pre-determined level of significance. This surface was constructed by accounting for the influence of multiple variables which appear to contribute to disease potential. The framework developed in this work strengthens the understanding of the characteristics of disease potential and provides predictions of this potential across a region. The study outcomes presented constitutes an innovative approach to environmental monitoring and management in the face of data paucity. Copyright © 2016 Elsevier B.V. All rights reserved.

Creative use of pilot points to address site and regional scale heterogeneity in a variable-density model

USGS Publications Warehouse

Dausman, Alyssa M.; Doherty, John; Langevin, Christian D.

2010-01-01

Pilot points for parameter estimation were creatively used to address heterogeneity at both the well field and regional scales in a variable-density groundwater flow and solute transport model designed to test multiple hypotheses for upward migration of fresh effluent injected into a highly transmissive saline carbonate aquifer. Two sets of pilot points were used within in multiple model layers, with one set of inner pilot points (totaling 158) having high spatial density to represent hydraulic conductivity at the site, while a second set of outer points (totaling 36) of lower spatial density was used to represent hydraulic conductivity further from the site. Use of a lower spatial density outside the site allowed (1) the total number of pilot points to be reduced while maintaining flexibility to accommodate heterogeneity at different scales, and (2) development of a model with greater areal extent in order to simulate proper boundary conditions that have a limited effect on the area of interest. The parameters associated with the inner pilot points were log transformed hydraulic conductivity multipliers of the conductivity field obtained by interpolation from outer pilot points. The use of this dual inner-outer scale parameterization (with inner parameters constituting multipliers for outer parameters) allowed smooth transition of hydraulic conductivity from the site scale, where greater spatial variability of hydraulic properties exists, to the regional scale where less spatial variability was necessary for model calibration. While the model is highly parameterized to accommodate potential aquifer heterogeneity, the total number of pilot points is kept at a minimum to enable reasonable calibration run times.

Space-Time Modelling of Groundwater Level Using Spartan Covariance Function

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil; Hristopulos, Dionissios

2014-05-01

Geostatistical models often need to handle variables that change in space and in time, such as the groundwater level of aquifers. A major advantage of space-time observations is that a higher number of data supports parameter estimation and prediction. In a statistical context, space-time data can be considered as realizations of random fields that are spatially extended and evolve in time. The combination of spatial and temporal measurements in sparsely monitored watersheds can provide very useful information by incorporating spatiotemporal correlations. Spatiotemporal interpolation is usually performed by applying the standard Kriging algorithms extended in a space-time framework. Spatiotemoral covariance functions for groundwater level modelling, however, have not been widely developed. We present a new non-separable theoretical spatiotemporal variogram function which is based on the Spartan covariance family and evaluate its performance in spatiotemporal Kriging (STRK) interpolation. The original spatial expression (Hristopulos and Elogne 2007) that has been successfully used for the spatial interpolation of groundwater level (Varouchakis and Hristopulos 2013) is modified by defining the following space-time normalized distance h = °h2r-+-α h2τ, hr=r- ξr, hτ=τ- ξτ; where r is the spatial lag vector, τ the temporal lag vector, ξr is the correlation length in position space (r) and ξτ in time (τ), h the normalized space-time lag vector, h = |h| is its Euclidean norm of the normalized space-time lag and α the coefficient that determines the relative weight of the time lag. The space-time experimental semivariogram is determined from the biannual (wet and dry period) time series of groundwater level residuals (obtained from the original series after trend removal) between the years 1981 and 2003 at ten sampling stations located in the Mires hydrological basin in the island of Crete (Greece). After the hydrological year 2002-2003 there is a significant groundwater level increase during the wet period of 2003-2004 and a considerable drop during the dry period of 2005-2006. Both periods are associated with significant annual changes in the precipitation compared to the basin average, i.e., a 40% increase and 65% decrease, respectively. We use STRK to 'predict' the groundwater level for the two selected hydrological periods (wet period of 2003-2004 and dry period of 2005-2006) at each sampling station. The predictions are validated using the respective measured values. The novel Spartan spatiotemporal covariance function gives a mean absolute relative prediction error of 12%. This is 45% lower than the respective value obtained with the commonly used product-sum covariance function, and 31% lower than the respective value obtained with a non-separable function based on the diffusion equation (Kolovos et al. 2010). The advantage of the Spartan space-time covariance model is confirmed with statistical measures such as the root mean square standardized error (RMSSE), the modified coefficient of model efficiency, E' (Legates and McCabe, 1999) and the modified Index of Agreement, IoA'(Janssen and Heuberger, 1995). Hristopulos, D. T. and Elogne, S. N. 2007. Analytic properties and covariance functions for a new class of generalized Gibbs random fields. IEEE Transactions on Information Theory, 53, 4667-4467. Janssen, P.H.M. and Heuberger P.S.C. 1995. Calibration of process-oriented models. Ecological Modelling, 83, 55-66. Kolovos, A., Christakos, G., Hristopulos, D. T. and Serre, M. L. 2004. Methods for generating non-separable spatiotemporal covariance models with potential environmental applications. Advances in Water Resources, 27 (8), 815-830. Legates, D.R. and McCabe Jr., G.J. 1999. Evaluating the use of 'goodness-of-fit' measures in hydrologic and hydro climatic model validation. Water Resources Research, 35, 233-241. Varouchakis, E. A. and Hristopulos, D. T. 2013. Improvement of groundwater level prediction in sparsely gauged basins using physical laws and local geographic features as auxiliary variables. Advances in Water Resources, 52, 34-49.

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Interpolation of extensive routine water pollution monitoring datasets: methodology and discussion of implications for aquifer management

NASA Astrophysics Data System (ADS)

Yuval; Rimon, Y.; Graber, E. R.; Furman, A.

2013-07-01

A large fraction of the fresh water available for human use is stored in groundwater aquifers. Since human activities such as mining, agriculture, industry and urbanization often result in incursion of various pollutants to groundwater, routine monitoring of water quality is an indispensable component of judicious aquifer management. Unfortunately, groundwater pollution monitoring is expensive and usually cannot cover an aquifer with the spatial resolution necessary for making adequate management decisions. Interpolation of monitoring data between points is thus an important tool for supplementing measured data. However, interpolating routine groundwater pollution data poses a special problem due to the nature of the observations. The data from a producing aquifer usually includes many zero pollution concentration values from the clean parts of the aquifer but may span a wide range (up to a few orders of magnitude) of values in the polluted areas. This manuscript presents a methodology that can cope with such datasets and use them to produce maps that present the pollution plumes but also delineates the clean areas that are fit for production. A method for assessing the quality of mapping in a way which is suitable to the data's dynamic range of values is also presented. Local variant of inverse distance weighting is employed to interpolate the data. Inclusion zones around the interpolation points ensure that only relevant observations contribute to each interpolated concentration. Using inclusion zones improves the accuracy of the mapping but results in interpolation grid points which are not assigned a value. That inherent trade-off between the interpolation accuracy and coverage is demonstrated using both circular and elliptical inclusion zones. A leave-one-out cross testing is used to assess and compare the performance of the interpolations. The methodology is demonstrated using groundwater pollution monitoring data from the Coastal aquifer along the Israeli shoreline.

Interpolation of extensive routine water pollution monitoring datasets: methodology and discussion of implications for aquifer management.

PubMed

Yuval, Yuval; Rimon, Yaara; Graber, Ellen R; Furman, Alex

2014-08-01

A large fraction of the fresh water available for human use is stored in groundwater aquifers. Since human activities such as mining, agriculture, industry and urbanisation often result in incursion of various pollutants to groundwater, routine monitoring of water quality is an indispensable component of judicious aquifer management. Unfortunately, groundwater pollution monitoring is expensive and usually cannot cover an aquifer with the spatial resolution necessary for making adequate management decisions. Interpolation of monitoring data is thus an important tool for supplementing monitoring observations. However, interpolating routine groundwater pollution data poses a special problem due to the nature of the observations. The data from a producing aquifer usually includes many zero pollution concentration values from the clean parts of the aquifer but may span a wide range of values (up to a few orders of magnitude) in the polluted areas. This manuscript presents a methodology that can cope with such datasets and use them to produce maps that present the pollution plumes but also delineates the clean areas that are fit for production. A method for assessing the quality of mapping in a way which is suitable to the data's dynamic range of values is also presented. A local variant of inverse distance weighting is employed to interpolate the data. Inclusion zones around the interpolation points ensure that only relevant observations contribute to each interpolated concentration. Using inclusion zones improves the accuracy of the mapping but results in interpolation grid points which are not assigned a value. The inherent trade-off between the interpolation accuracy and coverage is demonstrated using both circular and elliptical inclusion zones. A leave-one-out cross testing is used to assess and compare the performance of the interpolations. The methodology is demonstrated using groundwater pollution monitoring data from the coastal aquifer along the Israeli shoreline. The implications for aquifer management are discussed.

Interpreting the Geochemistry of the Northern Peninsula Ranges Batholith Using Principle Component Analysis and Spatial Interpolation

NASA Astrophysics Data System (ADS)

Pompe, L.; Clausen, B. L.; Morton, D. M.

2014-12-01

The Cretaceous northern Peninsular Ranges batholith (PRB) exemplifies emplacement in a combination oceanic arc / continental margin arc setting. Two approaches that can aid in understanding its statistical and spatial geochemistry variation are principle component analysis (PCA) and GIS interpolation mapping. The data analysis primarily used 287 samples from the large granitoid geochemical data set systematically collected by Baird and Welday. Of these, 80 points fell in the western Santa Ana block, 108 in the transitional Perris block, and 99 in the eastern San Jacinto block. In the statistical analysis, multivariate outliers were identified using Mahalanobis distance and excluded. A centered log ratio transformation was used to facilitate working with geochemical concentration values that range over many orders of magnitude. The data was then analyzed using PCA with IBM SPSS 21 reducing 40 geochemical variables to 4 components which are approximately related to the compatible, HFS, HRE, and LIL elements. The 4 components were interpreted as follows: (1) compatible [and negatively correlated incompatible] elements indicate extent of differentiation as typified by SiO2, (2) HFS elements indicate crustal contamination as typified by Sri and Nb/Yb ratios, (3) HRE elements indicate source depth as typified by Sr/Y and Gd/Yb ratios, and (4) LIL elements indicate alkalinity as typified by the K2O/SiO2ratio. Spatial interpolation maps of the 4 components were created with Esri ArcGIS for Desktop 10.2 by interpolating between the sample points using kriging and inverse distance weighting. Across-arc trends on the interpolation maps indicate a general increase from west to east for each of the 4 components, but with local exceptions as follows. The 15km offset on the San Jacinto Fault may be affecting the contours. South of San Jacinto is a west-east band of low Nb/Yb, Gd/Yb, and Sr/Y ratios. The highest Sr/Y ratios in the north central area that decrease further east may be due to the far eastern granitoids being transported above a shear zone. Along the western edge of the PRB, high SiO2 and K2O/SiO2 are interpreted to result from sampling shallow levels in the batholith (2-3 kb), as compared to deeper levels in the central (5-6 kb) and eastern (4.5 kb) areas.

Repeated surveys by acoustic Doppler current profiler for flow and sediment dynamics in a tidal river

USGS Publications Warehouse

Dinehart, R.L.; Burau, J.R.

2005-01-01

A strategy of repeated surveys by acoustic Doppler current profiler (ADCP) was applied in a tidal river to map velocity vectors and suspended-sediment indicators. The Sacramento River at the junction with the Delta Cross Channel at Walnut Grove, California, was surveyed over several tidal cycles in the Fall of 2000 and 2001 with a vessel-mounted ADCP. Velocity profiles were recorded along flow-defining survey paths, with surveys repeated every 27 min through a diurnal tidal cycle. Velocity vectors along each survey path were interpolated to a three-dimensional Cartesian grid that conformed to local bathymetry. A separate array of vectors was interpolated onto a grid from each survey. By displaying interpolated vector grids sequentially with computer animation, flow dynamics of the reach could be studied in three-dimensions as flow responded to the tidal cycle. Velocity streamtraces in the grid showed the upwelling of flow from the bottom of the Sacramento River channel into the Delta Cross Channel. The sequential display of vector grids showed that water in the canal briefly returned into the Sacramento River after peak flood tides, which had not been known previously. In addition to velocity vectors, ADCP data were processed to derive channel bathymetry and a spatial indicator for suspended-sediment concentration. Individual beam distances to bed, recorded by the ADCP, were transformed to yield bathymetry accurate enough to resolve small bedforms within the study reach. While recording velocity, ADCPs also record the intensity of acoustic backscatter from particles suspended in the flow. Sequential surveys of backscatter intensity were interpolated to grids and animated to indicate the spatial movement of suspended sediment through the study reach. Calculation of backscatter flux through cross-sectional grids provided a first step for computation of suspended-sediment discharge, the second step being a calibrated relation between backscatter intensity and sediment concentration. Spatial analyses of ADCP data showed that a strategy of repeated surveys and flow-field interpolation has the potential to simplify computation of flow and sediment discharge through complex waterways. The use of trade, product, industry, or firm names in this report is for descriptive purposes only and does not constitute endorsement of products by the US Government. ?? 2005 Elsevier B.V. All rights reserved.

Finite grid instability and spectral fidelity of the electrostatic Particle-In-Cell algorithm

DOE PAGES

Huang, C. -K.; Zeng, Y.; Wang, Y.; ...

2016-10-01

The origin of the Finite Grid Instability (FGI) is studied by resolving the dynamics in the 1D electrostatic Particle-In-Cell (PIC) model in the spectral domain at the single particle level and at the collective motion level. The spectral fidelity of the PIC model is contrasted with the underlying physical system or the gridless model. The systematic spectral phase and amplitude errors from the charge deposition and field interpolation are quantified for common particle shapes used in the PIC models. Lastly, it is shown through such analysis and in simulations that the lack of spectral fidelity relative to the physical systemmore » due to the existence of aliased spatial modes is the major cause of the FGI in the PIC model.« less

Finite grid instability and spectral fidelity of the electrostatic Particle-In-Cell algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, C. -K.; Zeng, Y.; Wang, Y.

The origin of the Finite Grid Instability (FGI) is studied by resolving the dynamics in the 1D electrostatic Particle-In-Cell (PIC) model in the spectral domain at the single particle level and at the collective motion level. The spectral fidelity of the PIC model is contrasted with the underlying physical system or the gridless model. The systematic spectral phase and amplitude errors from the charge deposition and field interpolation are quantified for common particle shapes used in the PIC models. Lastly, it is shown through such analysis and in simulations that the lack of spectral fidelity relative to the physical systemmore » due to the existence of aliased spatial modes is the major cause of the FGI in the PIC model.« less

Surface modeling of soil antibiotics.

PubMed

Shi, Wen-jiao; Yue, Tian-xiang; Du, Zheng-ping; Wang, Zong; Li, Xue-wen

2016-02-01

Large numbers of livestock and poultry feces are continuously applied into soils in intensive vegetable cultivation areas, and then some veterinary antibiotics are persistent existed in soils and cause health risk. For the spatial heterogeneity of antibiotic residues, developing a suitable technique to interpolate soil antibiotic residues is still a challenge. In this study, we developed an effective interpolator, high accuracy surface modeling (HASM) combined vegetable types, to predict the spatial patterns of soil antibiotics, using 100 surface soil samples collected from an intensive vegetable cultivation area located in east of China, and the fluoroquinolones (FQs), including ciprofloxacin (CFX), enrofloxacin (EFX) and norfloxacin (NFX), were analyzed as the target antibiotics. The results show that vegetable type is an effective factor to be combined to improve the interpolator performance. HASM achieves less mean absolute errors (MAEs) and root mean square errors (RMSEs) for total FQs (NFX+CFX+EFX), NFX, CFX and EFX than kriging with external drift (KED), stratified kriging (StK), ordinary kriging (OK) and inverse distance weighting (IDW). The MAE of HASM for FQs is 55.1 μg/kg, and the MAEs of KED, StK, OK and IDW are 99.0 μg/kg, 102.8 μg/kg, 106.3 μg/kg and 108.7 μg/kg, respectively. Further, RMSE simulated by HASM for FQs (CFX, EFX and NFX) are 106.2 μg/kg (88.6 μg/kg, 20.4 μg/kg and 39.2 μg/kg), and less 30% (27%, 22% and 36%), 33% (27%, 27% and 43%), 38% (34%, 23% and 41%) and 42% (32%, 35% and 51%) than the ones by KED, StK, OK and IDW, respectively. HASM also provides better maps with more details and more consistent maximum and minimum values of soil antibiotics compared with the measured data. The better performance can be concluded that HASM takes the vegetable type information as global approximate information, and takes local sampling data as its optimum control constraints. Copyright © 2015 Elsevier B.V. All rights reserved.

Interpolation strategies for reducing IFOV artifacts in microgrid polarimeter imagery.

PubMed

Ratliff, Bradley M; LaCasse, Charles F; Tyo, J Scott

2009-05-25

Microgrid polarimeters are composed of an array of micro-polarizing elements overlaid upon an FPA sensor. In the past decade systems have been designed and built in all regions of the optical spectrum. These systems have rugged, compact designs and the ability to obtain a complete set of polarimetric measurements during a single image capture. However, these systems acquire the polarization measurements through spatial modulation and each measurement has a varying instantaneous field-of-view (IFOV). When these measurements are combined to estimate the polarization images, strong edge artifacts are present that severely degrade the estimated polarization imagery. These artifacts can be reduced when interpolation strategies are first applied to the intensity data prior to Stokes vector estimation. Here we formally study IFOV error and the performance of several bilinear interpolation strategies used for reducing it.

An optical systems analysis approach to image resampling

NASA Technical Reports Server (NTRS)

Lyon, Richard G.

1997-01-01

All types of image registration require some type of resampling, either during the registration or as a final step in the registration process. Thus the image(s) must be regridded into a spatially uniform, or angularly uniform, coordinate system with some pre-defined resolution. Frequently the ending resolution is not the resolution at which the data was observed with. The registration algorithm designer and end product user are presented with a multitude of possible resampling methods each of which modify the spatial frequency content of the data in some way. The purpose of this paper is threefold: (1) to show how an imaging system modifies the scene from an end to end optical systems analysis approach, (2) to develop a generalized resampling model, and (3) empirically apply the model to simulated radiometric scene data and tabulate the results. A Hanning windowed sinc interpolator method will be developed based upon the optical characterization of the system. It will be discussed in terms of the effects and limitations of sampling, aliasing, spectral leakage, and computational complexity. Simulated radiometric scene data will be used to demonstrate each of the algorithms. A high resolution scene will be "grown" using a fractal growth algorithm based on mid-point recursion techniques. The result scene data will be convolved with a point spread function representing the optical response. The resultant scene will be convolved with the detection systems response and subsampled to the desired resolution. The resultant data product will be subsequently resampled to the correct grid using the Hanning windowed sinc interpolator and the results and errors tabulated and discussed.

Bayesian hierarchical models for regional climate reconstructions of the last glacial maximum

NASA Astrophysics Data System (ADS)

Weitzel, Nils; Hense, Andreas; Ohlwein, Christian

2017-04-01

Spatio-temporal reconstructions of past climate are important for the understanding of the long term behavior of the climate system and the sensitivity to forcing changes. Unfortunately, they are subject to large uncertainties, have to deal with a complex proxy-climate structure, and a physically reasonable interpolation between the sparse proxy observations is difficult. Bayesian Hierarchical Models (BHMs) are a class of statistical models that is well suited for spatio-temporal reconstructions of past climate because they permit the inclusion of multiple sources of information (e.g. records from different proxy types, uncertain age information, output from climate simulations) and quantify uncertainties in a statistically rigorous way. BHMs in paleoclimatology typically consist of three stages which are modeled individually and are combined using Bayesian inference techniques. The data stage models the proxy-climate relation (often named transfer function), the process stage models the spatio-temporal distribution of the climate variables of interest, and the prior stage consists of prior distributions of the model parameters. For our BHMs, we translate well-known proxy-climate transfer functions for pollen to a Bayesian framework. In addition, we can include Gaussian distributed local climate information from preprocessed proxy records. The process stage combines physically reasonable spatial structures from prior distributions with proxy records which leads to a multivariate posterior probability distribution for the reconstructed climate variables. The prior distributions that constrain the possible spatial structure of the climate variables are calculated from climate simulation output. We present results from pseudoproxy tests as well as new regional reconstructions of temperatures for the last glacial maximum (LGM, ˜ 21,000 years BP). These reconstructions combine proxy data syntheses with information from climate simulations for the LGM that were performed in the PMIP3 project. The proxy data syntheses consist either of raw pollen data or of normally distributed climate data from preprocessed proxy records. Future extensions of our method contain the inclusion of other proxy types (transfer functions), the implementation of other spatial interpolation techniques, the use of age uncertainties, and the extension to spatio-temporal reconstructions of the last deglaciation. Our work is part of the PalMod project funded by the German Federal Ministry of Education and Science (BMBF).

Improving emissions inventories in North America through systematic analysis of model performance during ICARTT and MILAGRO

NASA Astrophysics Data System (ADS)

Mena, Marcelo Andres

During 2004 and 2006 the University of Iowa provided air quality forecast support for flight planning of the ICARTT and MILAGRO field campaigns. A method for improvement of model performance in comparison to observations is showed. The method allows identifying sources of model error from boundary conditions and emissions inventories. Simultaneous analysis of horizontal interpolation of model error and error covariance showed that error in ozone modeling is highly correlated to the error of its precursors, and that there is geographical correlation also. During ICARTT ozone modeling error was improved by updating from the National Emissions Inventory from 1999 and 2001, and furthermore by updating large point source emissions from continuous monitoring data. Further improvements were achieved by reducing area emissions of NOx y 60% for states in the Southeast United States. Ozone error was highly correlated to NOy error during this campaign. Also ozone production in the United States was most sensitive to NOx emissions. During MILAGRO model performance in terms of correlation coefficients was higher, but model error in ozone modeling was high due overestimation of NOx and VOC emissions in Mexico City during forecasting. Large model improvements were shown by decreasing NOx emissions in Mexico City by 50% and VOC by 60%. Recurring ozone error is spatially correlated to CO and NOy error. Sensitivity studies show that Mexico City aerosol can reduce regional photolysis rates by 40% and ozone formation by 5-10%. Mexico City emissions can enhance NOy and O3 concentrations over the Gulf of Mexico in up to 10-20%. Mexico City emissions can convert regional ozone production regimes from VOC to NOx limited. A method of interpolation of observations along flight tracks is shown, which can be used to infer on the direction of outflow plumes. The use of ratios such as O3/NOy and NOx/NOy can be used to provide information on chemical characteristics of the plume, such as age, and ozone production regime. Interpolated MTBE observations can be used as a tracer of urban mobile source emissions. Finally procedures for estimating and gridding emissions inventories in Brazil and Mexico are presented.

Interpolation and analyses of EURO-Cordex data for the characterization of local and regional climate change impact

NASA Astrophysics Data System (ADS)

Fink, Manfred; Pfannschmidt, Kai; Knevels, Raphel; Fischer, Christian; Brenning, Alexander

2017-04-01

Decisions on measures for adapting to possible climate impacts are critical at both regional and local levels of authority. Currently, the data from EURO-CORDEX is only provided at resolutions (0.11 and 0.44 degrees) that are sufficient for climate analysis in larger scale regions. Therefore, there is a need for more detailed climate information that can assist decision making at the county and town levels. To tackle this challenge, we have developed a tool for the Just Another Modelling System (JAMS; Kralisch et al. 2007) that produces approx. 50 climate characterizing parameters (e.g. average temperature, ice days, climatic water balance, among others) for different time intervals. This tool is combined within the JAMS environment with the J2000g distributed conceptual hydrological model (Krause and Hanisch 2009) to additionally calculate hydro-meteorological parameters, such as actual evapotranspiration, ground water recharge and runoff generation. The resolution of the data was transformed to a higher resolution (250 m) by applying an inverse distance weights (IDW) interpolation. The IDW was combined with an altitude regression approach using digital elevation model data to represent more detailed information of the land surface. We applied this downscaling approach for the federal state of Thuringia, Germany, which is represented by 371206 model units. An ensemble of 10 different EURO-CORDEX models (0.11 degree resolution) in a time period from 1961 to 2100 and measured data from 1960 to 1990 were analyzed. The climate change impacts were estimated by analyzing the changes between historical periods (1960 - 1990) and future periods (2020 - 2050, 2070 -2100) within the modeled EURO-CORDEX ensemble members. We also improved our interpolation approach by replacing IDW with kriging; this approach was especially an advantage for the interpolation of irregularly distributed measurement stations. The results were used to estimate the effects of climate change for the federal state of Thuringia and to support Thuringian climate-change mitigation and adaptation strategies. Future work will concentrate on bias correction of the ensemble members using the measured data. References Kralisch, S., P. Krause, M. Fink, C. Fischer, and W. Flügel (2007): Component based environmental modelling using the JAMS framework, in Proceedings of the MODSIM 2007 International Congress on Modelling and Simulation, edited by D. Kulasiri and L. Oxley, Christchurch, New Zealand Krause P, Hanisch S (2009): Simulation and analysis of the impact of projected climate change on the spatially distributed water balance in Thuringia, Germany. Adv Geosci 21:33-48. doi:10.5194/adgeo-21-33-2009

Efficient Craig Interpolation for Linear Diophantine (Dis)Equations and Linear Modular Equations

DTIC Science & Technology

2008-02-01

Craig interpolants has enabled the development of powerful hardware and software model checking techniques. Efficient algorithms are known for computing...interpolants in rational and real linear arithmetic. We focus on subsets of integer linear arithmetic. Our main results are polynomial time algorithms ...congruences), and linear diophantine disequations. We show the utility of the proposed interpolation algorithms for discovering modular/divisibility predicates

Operational Interoperable Web Coverage Service for Earth Observing Satellite Data: Issues and Lessons Learned

NASA Astrophysics Data System (ADS)

Yang, W.; Min, M.; Bai, Y.; Lynnes, C.; Holloway, D.; Enloe, Y.; di, L.

2008-12-01

In the past few years, there have been growing interests, among major earth observing satellite (EOS) data providers, in serving data through the interoperable Web Coverage Service (WCS) interface protocol, developed by the Open Geospatial Consortium (OGC). The interface protocol defined in WCS specifications allows client software to make customized requests of multi-dimensional EOS data, including spatial and temporal subsetting, resampling and interpolation, and coordinate reference system (CRS) transformation. A WCS server describes an offered coverage, i.e., a data product, through a response to a client's DescribeCoverage request. The description includes the offered coverage's spatial/temporal extents and resolutions, supported CRSs, supported interpolation methods, and supported encoding formats. Based on such information, a client can request the entire or a subset of coverage in any spatial/temporal resolutions and in any one of the supported CRSs, formats, and interpolation methods. When implementing a WCS server, a data provider has different approaches to present its data holdings to clients. One of the most straightforward, and commonly used, approaches is to offer individual physical data files as separate coverages. Such implementation, however, will result in too many offered coverages for large data holdings and it also cannot fully present the relationship among different, but spatially and/or temporally associated, data files. It is desirable to disconnect offered coverages from physical data files so that the former is more coherent, especially in spatial and temporal domains. Therefore, some servers offer one single coverage for a set of spatially coregistered time series data files such as a daily global precipitation coverage linked to many global single- day precipitation files; others offer one single coverage for multiple temporally coregistered files together forming a large spatial extent. In either case, a server needs to assemble an output coverage real-time by combining potentially large number of physical files, which can be operationally difficult. The task becomes more challenging if an offered coverage involves spatially and temporally un-registered physical files. In this presentation, we will discuss issues and lessons learned in providing NASA's AIRS Level 2 atmospheric products, which are in satellite swath CRS and in 6-minute segment granule files, as virtual global coverages. We"ll discuss the WCS server's on- the-fly georectification, mosaicking, quality screening, performance, and scalability.

GRID3D-v2: An updated version of the GRID2D/3D computer program for generating grid systems in complex-shaped three-dimensional spatial domains

NASA Technical Reports Server (NTRS)

Steinthorsson, E.; Shih, T. I-P.; Roelke, R. J.

1991-01-01

In order to generate good quality systems for complicated three-dimensional spatial domains, the grid-generation method used must be able to exert rather precise controls over grid-point distributions. Several techniques are presented that enhance control of grid-point distribution for a class of algebraic grid-generation methods known as the two-, four-, and six-boundary methods. These techniques include variable stretching functions from bilinear interpolation, interpolating functions based on tension splines, and normalized K-factors. The techniques developed in this study were incorporated into a new version of GRID3D called GRID3D-v2. The usefulness of GRID3D-v2 was demonstrated by using it to generate a three-dimensional grid system in the coolent passage of a radial turbine blade with serpentine channels and pin fins.

Lung Cancer Mortality and Radon Concentration in a Chronically Exposed Neighborhood in Chihuahua, Mexico: A Geospatial Analysis

PubMed Central

Hinojosa de la Garza, Octavio R.; Sanín, Luz H.; Montero Cabrera, María Elena; Serrano Ramirez, Korina Ivette; Martínez Meyer, Enrique; Reyes Cortés, Manuel

2014-01-01

This study correlated lung cancer (LC) mortality with statistical data obtained from government public databases. In order to asses a relationship between LC deaths and radon accumulation in dwellings, indoor radon concentrations were measured with passive detectors randomly distributed in Chihuahua City. Kriging (K) and Inverse-Distance Weighting (IDW) spatial interpolations were carried out. Deaths were georeferenced and Moran's I correlation coefficients were calculated. The mean values (over n = 171) of the interpolation of radon concentrations of deceased's dwellings were 247.8 and 217.1 Bq/m3, for K and IDW, respectively. Through the Moran's I values obtained, correspondingly equal to 0.56 and 0.61, it was evident that LC mortality was directly associated with locations with high levels of radon, considering a stable population for more than 25 years, suggesting spatial clustering of LC deaths due to indoor radon concentrations. PMID:25165752

The Atmospheric Data Acquisition And Interpolation Process For Center-TRACON Automation System

NASA Technical Reports Server (NTRS)

Jardin, M. R.; Erzberger, H.; Denery, Dallas G. (Technical Monitor)

1995-01-01

The Center-TRACON Automation System (CTAS), an advanced new air traffic automation program, requires knowledge of spatial and temporal atmospheric conditions such as the wind speed and direction, the temperature and the pressure in order to accurately predict aircraft trajectories. Real-time atmospheric data is available in a grid format so that CTAS must interpolate between the grid points to estimate the atmospheric parameter values. The atmospheric data grid is generally not in the same coordinate system as that used by CTAS so that coordinate conversions are required. Both the interpolation and coordinate conversion processes can introduce errors into the atmospheric data and reduce interpolation accuracy. More accurate algorithms may be computationally expensive or may require a prohibitively large amount of data storage capacity so that trade-offs must be made between accuracy and the available computational and data storage resources. The atmospheric data acquisition and processing employed by CTAS will be outlined in this report. The effects of atmospheric data processing on CTAS trajectory prediction will also be analyzed, and several examples of the trajectory prediction process will be given.

Delimiting Areas of Endemism through Kernel Interpolation

PubMed Central

Oliveira, Ubirajara; Brescovit, Antonio D.; Santos, Adalberto J.

2015-01-01

We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units. PMID:25611971

Wavefront reconstruction method based on wavelet fractal interpolation for coherent free space optical communication

NASA Astrophysics Data System (ADS)

Zhang, Dai; Hao, Shiqi; Zhao, Qingsong; Zhao, Qi; Wang, Lei; Wan, Xiongfeng

2018-03-01

Existing wavefront reconstruction methods are usually low in resolution, restricted by structure characteristics of the Shack Hartmann wavefront sensor (SH WFS) and the deformable mirror (DM) in the adaptive optics (AO) system, thus, resulting in weak homodyne detection efficiency for free space optical (FSO) communication. In order to solve this problem, we firstly validate the feasibility of liquid crystal spatial light modulator (LC SLM) using in an AO system. Then, wavefront reconstruction method based on wavelet fractal interpolation is proposed after self-similarity analysis of wavefront distortion caused by atmospheric turbulence. Fast wavelet decomposition is operated to multiresolution analyze the wavefront phase spectrum, during which soft threshold denoising is carried out. The resolution of estimated wavefront phase is then improved by fractal interpolation. Finally, fast wavelet reconstruction is taken to recover wavefront phase. Simulation results reflect the superiority of our method in homodyne detection. Compared with minimum variance estimation (MVE) method based on interpolation techniques, the proposed method could obtain superior homodyne detection efficiency with lower operation complexity. Our research findings have theoretical significance in the design of coherent FSO communication system.

When Distractors and To-Be-Remembered Items Compete for the Control of Action: A New Perspective on Serial Memory for Spatial Information

ERIC Educational Resources Information Center

Guerard, Katherine; Tremblay, Sebastien

2011-01-01

In serial memory for spatial information, performance is impaired when distractors are interpolated between to-be-remembered (TBR) stimuli (Tremblay, Nicholls, Parmentier, & Jones, 2005). The so-called sandwich effect, combined with the use of eye tracking, served as a tool for examining the role of the oculomotor system in serial memory for…

Sparse representation based image interpolation with nonlocal autoregressive modeling.

PubMed

Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming

2013-04-01

Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.

Rapidity distribution of photons from an anisotropic quark-gluon plasma

NASA Astrophysics Data System (ADS)

Bhattacharya, Lusaka; Roy, Pradip

2010-05-01

We calculate rapidity distribution of photons due to Compton and annihilation processes from quark gluon plasma with pre-equilibrium momentum-space anisotropy. We also include contributions from hadronic matter with late-stage transverse expansion. A phenomenological model has been used for the time evolution of hard momentum scale, phard(τ), and anisotropy parameter, ξ(τ). As a result of pre-equilibrium momentum-space anisotropy, we find significant modification of photons rapidity distribution. For example, with the fixed initial condition (FIC) free-streaming (δ=2) interpolating model we observe significant enhancement of photon rapidity distribution at fixed pT, where as for FIC collisionally broadened (δ=2/3) interpolating model the yield increases till y~1. Beyond that suppression is observed. With fixed final multiplicity (FFM) free-streaming interpolating model we predict enhancement of photon yield which is less than the case of FIC. Suppression is always observed for FFM collisionally broadened interpolating model.

Comparison of alternative spatial resolutions in the application of a spatially distributed biogeochemical model over complex terrain

USGS Publications Warehouse

Turner, D.P.; Dodson, R.; Marks, D.

1996-01-01

Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the study of the potential impacts of climate change.

Spacetime emergence of the robertson-walker universe from a matrix model.

PubMed

Erdmenger, Johanna; Meyer, René; Park, Jeong-Hyuck

2007-06-29

Using a novel, string theory-inspired formalism based on a Hamiltonian constraint, we obtain a conformal mechanical system for the spatially flat four-dimensional Robertson-Walker Universe. Depending on parameter choices, this system describes either a relativistic particle in the Robertson-Walker background or metric fluctuations of the Robertson-Walker geometry. Moreover, we derive a tree-level M theory matrix model in this time-dependent background. Imposing the Hamiltonian constraint forces the spacetime geometry to be fuzzy near the big bang, while the classical Robertson-Walker geometry emerges as the Universe expands. From our approach, we also derive the temperature of the Universe interpolating between the radiation and matter dominated eras.

Subgrid geoelectric field specification for GIC modeling

NASA Astrophysics Data System (ADS)

Butala, M.; Grawe, M.; Kamalabadi, F.; Makela, J. J.

2017-12-01

Geomagnetically induced currents (GICs) result from surface geomagnetic field (ěc{B}) variation driven by space weather disturbances. For the most intense disturbances, the consequences can range from power grid instability to even widespread failure. Modeling GICs to assess vulnerability requires the specification of the surface geoelectric field (ěc{E}) at all spatial locations coincident with the electric power system. In this study, we investigate how to best reproduce ěc{E} given the available sparse, irregularly spaced magnetometer measurements of ěc{B} and suitable electromagnetic transfer functions (EMTFs) to transform the local ěc{B} to ěc{E}. The assessment is made against ground truth from publicly available ěc{E} measurements provided by the EarthScope magnetotelluric (MT) array, a set of 7 fixed and several transportable joint ěc{B} and ěc{E} sensors. The scope of this study spans several dimensions: geomagnetic disturbance intensity, spatial interpolation scheme, and EMTF type, i.e., 1-D models based on studies of local geology and 3-D models derived from the EarthScope MT data.

Voxel modelling of sands and gravels of Pleistocene Rhine and Meuse deposits in Flanders (Belgium)

NASA Astrophysics Data System (ADS)

van Haren, Tom; Dirix, Katrijn; De Koninck, Roel

2017-04-01

Voxel modelling or 3D volume modelling of Quaternary raw materials is VITO's next step in the geological layer modelling of the Flanders and Brussels Capital Region in Belgium (G3D - Matthijs et al., 2013). The aim is to schematise deposits as voxels ('volumetric pixels') that represent lithological information on a grid in three-dimensional space (25 x 25 x 0.5 m). A new voxel model on Pleistocene Meuse and Rhine sands and gravels will be illustrated succeeding a voxel model on loess resources (van Haren et al., 2016). The model methodology is based on a geological 'skeleton' extracted from the regional geological layer model of Flanders. This framework holds the 3D interpolated lithological information of 5.000 boreholes. First a check on quality and spatial location filtered out significant and usable lithological information. Subsequently a manual geological interpretation was performed to analyse stratigraphical arrangement and identify the raw materials of interest. Finally, a workflow was developed that automatically encodes and classifies the borehole descriptions in a standardized manner. This workflow was implemented by combining Microsoft Access® and ArcMap® and is able to convert borehole descriptions into specific geological parameters. An analysis of the conversed lithological data prior to interpolation improves the understanding of the spatial distribution, to fine tune the modelling process and to know the limitations of the data. The converted lithological data were 3D interpolated in Voxler using IDW and resulted in a model containing 52 million voxels. It gives an overview on the regional distribution and thickness variation of interesting Pleistocene aggregates of Meuse and Rhine. Much effort has been put in setting up a database structure in Microsoft Access® and Microsoft SQL Server® in order to arrange and analyse the lithological information, link the voxel model with the geological layer model and handle and analyse the resulting voxelmodel data. The database structure allows to analyse and set certain preconditions (minimal thickness or maximum depth of aggregates, maximum thickness of intercalating clays) on the model in order to calculate and view distributions of deposits which meet these preconditions. These results are interesting for pre-prospective purposes, illustrating the distribution of lithological information and making the end user more aware of the potential economic value of the subsurface. References van Haren T. et al (2016) - An interactive voxel model for mineral resources: loess deposits in Flanders (Belgium). Zeitschrift der Deutschen Gesellschaft für Geowissenschaften, Volume 167, Number 4, pp. 363-376(14). Matthijs J. et al. (2013) - Geological 3D layer model of the Flanders Region and Brussels-Capital Region - 2nd version. Study performed in order of the Ministery of the Flemish Community. VITO report 2013/R/ETE/43, 24p. (in Dutch)

Progress on Discrete Fracture Network models with implications on the predictions of permeability and flow channeling structure

NASA Astrophysics Data System (ADS)

Darcel, C.; Davy, P.; Le Goc, R.; Maillot, J.; Selroos, J. O.

2017-12-01

We present progress on Discrete Fracture Network (DFN) flow modeling, including realistic advanced DFN spatial structures and local fracture transmissivity properties, through an application to the Forsmark site in Sweden. DFN models are a framework to combine fracture datasets from different sources and scales and to interpolate them in combining statistical distributions and stereological relations. The resulting DFN upscaling function - size density distribution - is a model component key to extrapolating fracture size densities between data gaps, from borehole core up to site scale. Another important feature of DFN models lays in the spatial correlations between fractures, with still unevaluated consequences on flow predictions. Indeed, although common Poisson (i.e. spatially random) models are widely used, they do not reflect these geological evidences for more complex structures. To model them, we define a DFN growth process from kinematic rules for nucleation, growth and stopping conditions. It mimics in a simplified way the geological fracturing processes and produces DFN characteristics -both upscaling function and spatial correlations- fully consistent with field observations. DFN structures are first compared for constant transmissivities. Flow simulations for the kinematic and equivalent Poisson DFN models show striking differences: with the kinematic DFN, connectivity and permeability are significantly smaller, down to a difference of one order of magnitude, and flow is much more channelized. Further flow analyses are performed with more realistic transmissivity distribution conditions (sealed parts, relations to fracture sizes, orientations and in-situ stress field). The relative importance of the overall DFN structure in the final flow predictions is discussed.

A new interpolation method for gridded extensive variables with application in Lagrangian transport and dispersion models

NASA Astrophysics Data System (ADS)

Hittmeir, Sabine; Philipp, Anne; Seibert, Petra

2017-04-01

In discretised form, an extensive variable usually represents an integral over a 3-dimensional (x,y,z) grid cell. In the case of vertical fluxes, gridded values represent integrals over a horizontal (x,y) grid face. In meteorological models, fluxes (precipitation, turbulent fluxes, etc.) are usually written out as temporally integrated values, thus effectively forming 3D (x,y,t) integrals. Lagrangian transport models require interpolation of all relevant variables towards the location in 4D space of each of the computational particles. Trivial interpolation algorithms usually implicitly assume the integral value to be a point value valid at the grid centre. If the integral value would be reconstructed from the interpolated point values, it would in general not be correct. If nonlinear interpolation methods are used, non-negativity cannot easily be ensured. This problem became obvious with respect to the interpolation of precipitation for the calculation of wet deposition FLEXPART (http://flexpart.eu) which uses ECMWF model output or other gridded input data. The presently implemented method consists of a special preprocessing in the input preparation software and subsequent linear interpolation in the model. The interpolated values are positive but the criterion of cell-wise conservation of the integral property is violated; it is also not very accurate as it smoothes the field. A new interpolation algorithm was developed which introduces additional supporting grid points in each time interval with linear interpolation to be applied in FLEXPART later between them. It preserves the integral precipitation in each time interval, guarantees the continuity of the time series, and maintains non-negativity. The function values of the remapping algorithm at these subgrid points constitute the degrees of freedom which can be prescribed in various ways. Combining the advantages of different approaches leads to a final algorithm respecting all the required conditions. To improve the monotonicity behaviour we additionally derived a filter to restrict over- or undershooting. At the current stage, the algorithm is meant primarily for the temporal dimension. It can also be applied with operator-splitting to include the two horizontal dimensions. An extension to 2D appears feasible, while a fully 3D version would most likely not justify the effort compared to the operator-splitting approach.

Improving spatial resolution in skin-contact thermography: comparison between a spline based and linear interpolation.

PubMed

Giansanti, Daniele

2008-07-01

A wearable device for skin-contact thermography [Giansanti D, Maccioni G. Development and testing of a wearable integrated thermometer sensor for skin contact thermography. Med Eng Phys 2006 [ahead of print

An interpolated activity during the knowledge-of-results delay interval eliminates the learning advantages of self-controlled feedback schedules.

PubMed

Carter, Michael J; Ste-Marie, Diane M

2017-03-01

The learning advantages of self-controlled knowledge-of-results (KR) schedules compared to yoked schedules have been linked to the optimization of the informational value of the KR received for the enhancement of one's error-detection capabilities. This suggests that information-processing activities that occur after motor execution, but prior to receiving KR (i.e., the KR-delay interval) may underlie self-controlled KR learning advantages. The present experiment investigated whether self-controlled KR learning benefits would be eliminated if an interpolated activity was performed during the KR-delay interval. Participants practiced a waveform matching task that required two rapid elbow extension-flexion reversals in one of four groups using a factorial combination of choice (self-controlled, yoked) and KR-delay interval (empty, interpolated). The waveform had specific spatial and temporal constraints, and an overall movement time goal. The results indicated that the self-controlled + empty group had superior retention and transfer scores compared to all other groups. Moreover, the self-controlled + interpolated and yoked + interpolated groups did not differ significantly in retention and transfer; thus, the interpolated activity eliminated the typically found learning benefits of self-controlled KR. No significant differences were found between the two yoked groups. We suggest the interpolated activity interfered with information-processing activities specific to self-controlled KR conditions that occur during the KR-delay interval and that these activities are vital for reaping the associated learning benefits. These findings add to the growing evidence that challenge the motivational account of self-controlled KR learning advantages and instead highlights informational factors associated with the KR-delay interval as an important variable for motor learning under self-controlled KR schedules.

Minimization of required model runs in the Random Mixing approach to inverse groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Hoerning, Sebastian; Bardossy, Andras; du Plessis, Jaco

2017-04-01

Most geostatistical inverse groundwater flow and transport modelling approaches utilize a numerical solver to minimize the discrepancy between observed and simulated hydraulic heads and/or hydraulic concentration values. The optimization procedure often requires many model runs, which for complex models lead to long run times. Random Mixing is a promising new geostatistical technique for inverse modelling. The method is an extension of the gradual deformation approach. It works by finding a field which preserves the covariance structure and maintains observed hydraulic conductivities. This field is perturbed by mixing it with new fields that fulfill the homogeneous conditions. This mixing is expressed as an optimization problem which aims to minimize the difference between the observed and simulated hydraulic heads and/or concentration values. To preserve the spatial structure, the mixing weights must lie on the unit hyper-sphere. We present a modification to the Random Mixing algorithm which significantly reduces the number of model runs required. The approach involves taking n equally spaced points on the unit circle as weights for mixing conditional random fields. Each of these mixtures provides a solution to the forward model at the conditioning locations. For each of the locations the solutions are then interpolated around the circle to provide solutions for additional mixing weights at very low computational cost. The interpolated solutions are used to search for a mixture which maximally reduces the objective function. This is in contrast to other approaches which evaluate the objective function for the n mixtures and then interpolate the obtained values. Keeping the mixture on the unit circle makes it easy to generate equidistant sampling points in the space; however, this means that only two fields are mixed at a time. Once the optimal mixture for two fields has been found, they are combined to form the input to the next iteration of the algorithm. This process is repeated until a threshold in the objective function is met or insufficient changes are produced in successive iterations.

Localized dark solitons and vortices in defocusing media with spatially inhomogeneous nonlinearity.

PubMed

Zeng, Jianhua; Malomed, Boris A

2017-05-01

Recent studies have demonstrated that defocusing cubic nonlinearity with local strength growing from the center to the periphery faster than r^{D}, in space of dimension D with radial coordinate r, supports a vast variety of robust bright solitons. In the framework of the same model, but with a weaker spatial-growth rate ∼r^{α} with α≤D, we test here the possibility to create stable localized continuous waves (LCWs) in one-dimensional (1D) and 2D geometries, localized dark solitons (LDSs) in one dimension, and localized dark vortices (LDVs) in two dimensions, which are all realized as loosely confined states with a divergent norm. Asymptotic tails of the solutions, which determine the divergence of the norm, are constructed in a universal analytical form by means of the Thomas-Fermi approximation (TFA). Global approximations for the LCWs, LDSs, and LDVs are constructed on the basis of interpolations between analytical approximations available far from (TFA) and close to the center. In particular, the interpolations for the 1D LDS, as well as for the 2D LDVs, are based on a deformed-tanh expression, which is suggested by the usual 1D dark-soliton solution. The analytical interpolations produce very accurate results, in comparison with numerical findings, for the 1D and 2D LCWs, 1D LDSs, and 2D LDVs with vorticity S=1. In addition to the 1D fundamental LDSs with the single notch and 2D vortices with S=1, higher-order LDSs with multiple notches are found too, as well as double LDVs, with S=2. Stability regions for the modes under consideration are identified by means of systematic simulations, the LCWs being completely stable in one and two dimensions, as they are ground states in the corresponding settings. Basic evolution scenarios are identified for those vortices that are unstable. The settings considered in this work may be implemented in nonlinear optics and in Bose-Einstein condensates.

Localized dark solitons and vortices in defocusing media with spatially inhomogeneous nonlinearity

NASA Astrophysics Data System (ADS)

Zeng, Jianhua; Malomed, Boris A.

2017-05-01

Recent studies have demonstrated that defocusing cubic nonlinearity with local strength growing from the center to the periphery faster than rD, in space of dimension D with radial coordinate r , supports a vast variety of robust bright solitons. In the framework of the same model, but with a weaker spatial-growth rate ˜rα with α ≤D , we test here the possibility to create stable localized continuous waves (LCWs) in one-dimensional (1D) and 2D geometries, localized dark solitons (LDSs) in one dimension, and localized dark vortices (LDVs) in two dimensions, which are all realized as loosely confined states with a divergent norm. Asymptotic tails of the solutions, which determine the divergence of the norm, are constructed in a universal analytical form by means of the Thomas-Fermi approximation (TFA). Global approximations for the LCWs, LDSs, and LDVs are constructed on the basis of interpolations between analytical approximations available far from (TFA) and close to the center. In particular, the interpolations for the 1D LDS, as well as for the 2D LDVs, are based on a deformed-tanh expression, which is suggested by the usual 1D dark-soliton solution. The analytical interpolations produce very accurate results, in comparison with numerical findings, for the 1D and 2D LCWs, 1D LDSs, and 2D LDVs with vorticity S =1 . In addition to the 1D fundamental LDSs with the single notch and 2D vortices with S =1 , higher-order LDSs with multiple notches are found too, as well as double LDVs, with S =2 . Stability regions for the modes under consideration are identified by means of systematic simulations, the LCWs being completely stable in one and two dimensions, as they are ground states in the corresponding settings. Basic evolution scenarios are identified for those vortices that are unstable. The settings considered in this work may be implemented in nonlinear optics and in Bose-Einstein condensates.

Generation of real-time mode high-resolution water vapor fields from GPS observations

NASA Astrophysics Data System (ADS)

Yu, Chen; Penna, Nigel T.; Li, Zhenhong

2017-02-01

Pointwise GPS measurements of tropospheric zenith total delay can be interpolated to provide high-resolution water vapor maps which may be used for correcting synthetic aperture radar images, for numeral weather prediction, and for correcting Network Real-time Kinematic GPS observations. Several previous studies have addressed the importance of the elevation dependency of water vapor, but it is often a challenge to separate elevation-dependent tropospheric delays from turbulent components. In this paper, we present an iterative tropospheric decomposition interpolation model that decouples the elevation and turbulent tropospheric delay components. For a 150 km × 150 km California study region, we estimate real-time mode zenith total delays at 41 GPS stations over 1 year by using the precise point positioning technique and demonstrate that the decoupled interpolation model generates improved high-resolution tropospheric delay maps compared with previous tropospheric turbulence- and elevation-dependent models. Cross validation of the GPS zenith total delays yields an RMS error of 4.6 mm with the decoupled interpolation model, compared with 8.4 mm with the previous model. On converting the GPS zenith wet delays to precipitable water vapor and interpolating to 1 km grid cells across the region, validations with the Moderate Resolution Imaging Spectroradiometer near-IR water vapor product show 1.7 mm RMS differences by using the decoupled model, compared with 2.0 mm for the previous interpolation model. Such results are obtained without differencing the tropospheric delays or water vapor estimates in time or space, while the errors are similar over flat and mountainous terrains, as well as for both inland and coastal areas.

Uncertainty estimates of altimetric Global Mean Sea Level timeseries

NASA Astrophysics Data System (ADS)

Scharffenberg, Martin; Hemming, Michael; Stammer, Detlef

2016-04-01

An attempt is being presented concerned with providing uncertainty measures for global mean sea level time series. For this purpose sea surface height (SSH) fields, simulated by the high resolution STORM/NCEP model for the period 1993 - 2010, were subsampled along altimeter tracks and processed similar to techniques used by five working groups to estimate GMSL. Results suggest that the spatial and temporal resolution have a substantial impact on GMSL estimates. Major impacts can especially result from the interpolation technique or the treatment of SSH outliers and easily lead to artificial temporal variability in the resulting time series.

Issues in Data Fusion for Satellite Aerosol Measurements for Applications with GIOVANNI System at NASA GES DISC

NASA Technical Reports Server (NTRS)

Gopalan, Arun; Zubko, Viktor; Leptoukh, Gregory G.

2008-01-01

We look at issues, barriers and approaches for Data Fusion of satellite aerosol data as available from the GES DISC GIOVANNI Web Service. Daily Global Maps of AOT from a single satellite sensor alone contain gaps that arise due to various sources (sun glint regions, clouds, orbital swath gaps at low latitudes, bright underlying surfaces etc.). The goal is to develop a fast, accurate and efficient method to improve the spatial coverage of the Daily AOT data to facilitate comparisons with Global Models. Data Fusion may be supplemented by Optimal Interpolation (OI) as needed.

Shape Control in Multivariate Barycentric Rational Interpolation

NASA Astrophysics Data System (ADS)

Nguyen, Hoa Thang; Cuyt, Annie; Celis, Oliver Salazar

2010-09-01

The most stable formula for a rational interpolant for use on a finite interval is the barycentric form [1, 2]. A simple choice of the barycentric weights ensures the absence of (unwanted) poles on the real line [3]. In [4] we indicate that a more refined choice of the weights in barycentric rational interpolation can guarantee comonotonicity and coconvexity of the rational interpolant in addition to a polefree region of interest. In this presentation we generalize the above to the multivariate case. We use a product-like form of univariate barycentric rational interpolants and indicate how the location of the poles and the shape of the function can be controlled. This functionality is of importance in the construction of mathematical models that need to express a certain trend, such as in probability distributions, economics, population dynamics, tumor growth models etc.

Optimizing weather radar observations using an adaptive multiquadric surface fitting algorithm

NASA Astrophysics Data System (ADS)

Martens, Brecht; Cabus, Pieter; De Jongh, Inge; Verhoest, Niko

2013-04-01

Real time forecasting of river flow is an essential tool in operational water management. Such real time modelling systems require well calibrated models which can make use of spatially distributed rainfall observations. Weather radars provide spatial data, however, since radar measurements are sensitive to a large range of error sources, often a discrepancy between radar observations and ground-based measurements, which are mostly considered as ground truth, can be observed. Through merging ground observations with the radar product, often referred to as data merging, one may force the radar observations to better correspond to the ground-based measurements, without losing the spatial information. In this paper, radar images and ground-based measurements of rainfall are merged based on interpolated gauge-adjustment factors (Moore et al., 1998; Cole and Moore, 2008) or scaling factors. Using the following equation, scaling factors (C(xα)) are calculated at each position xα where a gauge measurement (Ig(xα)) is available: Ig(xα)+-? C (xα) = Ir(xα)+ ? (1) where Ir(xα) is the radar-based observation in the pixel overlapping the rain gauge and ? is a constant making sure the scaling factor can be calculated when Ir(xα) is zero. These scaling factors are interpolated on the radar grid, resulting in a unique scaling factor for each pixel. Multiquadric surface fitting is used as an interpolation algorithm (Hardy, 1971): C*(x0) = aTv + a0 (2) where C*(x0) is the prediction at location x0, the vector a (Nx1, with N the number of ground-based measurements used) and the constant a0 parameters describing the surface and v an Nx1 vector containing the (Euclidian) distance between each point xα used in the interpolation and the point x0. The parameters describing the surface are derived by forcing the surface to be an exact interpolator and impose that the sum of the parameters in a should be zero. However, often, the surface is allowed to pass near the observations (i.e. the observed scaling factors C(xα)) on a distance aαK by introducing an offset parameter K, which results in slightly different equations to calculate a and a0. The described technique is currently being used by the Flemish Environmental Agency in an online forecasting system of river discharges within Flanders (Belgium). However, rescaling the radar data using the described algorithm is not always giving rise to an improved weather radar product. Probably one of the main reasons is the parameters K and ? which are implemented as constants. It can be expected that, among others, depending on the characteristics of the rainfall, different values for the parameters should be used. Adaptation of the parameter values is achieved by an online calibration of K and ? at each time step (every 15 minutes), using validated rain gauge measurements as ground truth. Results demonstrate that rescaling radar images using optimized values for K and ? at each time step lead to a significant improvement of the rainfall estimation, which in turn will result in higher quality discharge predictions. Moreover, it is shown that calibrated values for K and ? can be obtained in near-real time. References Cole, S. J., and Moore, R. J. (2008). Hydrological modelling using raingauge- and radar-based estimators of areal rainfall. Journal of Hydrology, 358(3-4), 159-181. Hardy, R.L., (1971) Multiquadric equations of topography and other irregular surfaces, Journal of Geophysical Research, 76(8): 1905-1915. Moore, R. J., Watson, B. C., Jones, D. A. and Black, K. B. (1989). London weather radar local calibration study. Technical report, Institute of Hydrology.

Precipitation data in a mountainous catchment in Honduras: quality assessment and spatiotemporal characteristics

NASA Astrophysics Data System (ADS)

Westerberg, I.; Walther, A.; Guerrero, J.-L.; Coello, Z.; Halldin, S.; Xu, C.-Y.; Chen, D.; Lundin, L.-C.

2010-08-01

An accurate description of temporal and spatial precipitation variability in Central America is important for local farming, water supply and flood management. Data quality problems and lack of consistent precipitation data impede hydrometeorological analysis in the 7,500 km2 Choluteca River basin in central Honduras, encompassing the capital Tegucigalpa. We used precipitation data from 60 daily and 13 monthly stations in 1913-2006 from five local authorities and NOAA's Global Historical Climatology Network. Quality control routines were developed to tackle the specific data quality problems. The quality-controlled data were characterised spatially and temporally, and compared with regional and larger-scale studies. Two gap-filling methods for daily data and three interpolation methods for monthly and mean annual precipitation were compared. The coefficient-of-correlation-weighting method provided the best results for gap-filling and the universal kriging method for spatial interpolation. In-homogeneity in the time series was the main quality problem, and 22% of the daily precipitation data were too poor to be used. Spatial autocorrelation for monthly precipitation was low during the dry season, and correlation increased markedly when data were temporally aggregated from a daily time scale to 4-5 days. The analysis manifested the high spatial and temporal variability caused by the diverse precipitation-generating mechanisms and the need for an improved monitoring network.

Assessment of Potential Location of High Arsenic Contamination Using Fuzzy Overlay and Spatial Anisotropy Approach in Iron Mine Surrounding Area

PubMed Central

Wirojanagud, Wanpen; Srisatit, Thares

2014-01-01

Fuzzy overlay approach on three raster maps including land slope, soil type, and distance to stream can be used to identify the most potential locations of high arsenic contamination in soils. Verification of high arsenic contamination was made by collection samples and analysis of arsenic content and interpolation surface by spatial anisotropic method. A total of 51 soil samples were collected at the potential contaminated location clarified by fuzzy overlay approach. At each location, soil samples were taken at the depth of 0.00-1.00 m from the surface ground level. Interpolation surface of the analysed arsenic content using spatial anisotropic would verify the potential arsenic contamination location obtained from fuzzy overlay outputs. Both outputs of the spatial surface anisotropic and the fuzzy overlay mapping were significantly spatially conformed. Three contaminated areas with arsenic concentrations of 7.19 ± 2.86, 6.60 ± 3.04, and 4.90 ± 2.67 mg/kg exceeded the arsenic content of 3.9 mg/kg, the maximum concentration level (MCL) for agricultural soils as designated by Office of National Environment Board of Thailand. It is concluded that fuzzy overlay mapping could be employed for identification of potential contamination area with the verification by surface anisotropic approach including intensive sampling and analysis of the substances of interest. PMID:25110751

Spatial distribution of tropospheric ozone in national parks of California: interpretation of passive-sampler data.

PubMed

Ray, J D

2001-09-28

The National Park Service (NPS) has tested and used passive ozone samplers for several years to get baseline values for parks and to determine the spatial variability within parks. Experience has shown that the Ogawa passive samplers can provide +/-10% accuracy when used with a quality assurance program consisting of blanks, duplicates, collocated instrumentation, and a standard operating procedure that carefully guides site operators. Although the passive device does not meet EPA criteria as a certified method (mainly, that hourly values be measured), it does provide seasonal summed values of ozone. The seasonal ozone concentrations from the passive devices can be compared to other monitoring to determine baseline values, trends, and spatial variations. This point is illustrated with some kriged interpolation maps of ozone statistics. Passive ozone samplers were used to get elevational gradients and spatial distributions of ozone within a park. This was done in varying degrees at Mount Rainier, Olympic, Sequoia-Kings Canyon, Yosemite, Joshua Tree, Rocky Mountain, and Great Smoky Mountains national parks. The ozone has been found to vary by factors of 2 and 3 within a park when average ozone is compared between locations. Specific examples of the spatial distributions of ozone in three parks within California are given using interpolation maps. Positive aspects and limitations of the passive sampling approach are presented.

A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models

NASA Astrophysics Data System (ADS)

Cheng, Liantao; Zhang, Fenghui; Kang, Xiaoyu; Wang, Lang

2018-05-01

In evolutionary population synthesis (EPS) models, we need to convert stellar evolutionary parameters into spectra via interpolation in a stellar spectral library. For theoretical stellar spectral libraries, the spectrum grid is homogeneous on the effective-temperature and gravity plane for a given metallicity. It is relatively easy to derive stellar spectra. For empirical stellar spectral libraries, stellar parameters are irregularly distributed and the interpolation algorithm is relatively complicated. In those EPS models that use empirical stellar spectral libraries, different algorithms are used and the codes are often not released. Moreover, these algorithms are often complicated. In this work, based on a radial basis function (RBF) network, we present a new spectrum interpolation algorithm and its code. Compared with the other interpolation algorithms that are used in EPS models, it can be easily understood and is highly efficient in terms of computation. The code is written in MATLAB scripts and can be used on any computer system. Using it, we can obtain the interpolated spectra from a library or a combination of libraries. We apply this algorithm to several stellar spectral libraries (such as MILES, ELODIE-3.1 and STELIB-3.2) and give the integrated spectral energy distributions (ISEDs) of stellar populations (with ages from 1 Myr to 14 Gyr) by combining them with Yunnan-III isochrones. Our results show that the differences caused by the adoption of different EPS model components are less than 0.2 dex. All data about the stellar population ISEDs in this work and the RBF spectrum interpolation code can be obtained by request from the first author or downloaded from http://www1.ynao.ac.cn/˜zhangfh.

Generic Sensor Modeling Using Pulse Method

NASA Technical Reports Server (NTRS)

Helder, Dennis L.; Choi, Taeyoung

2005-01-01

Recent development of high spatial resolution satellites such as IKONOS, Quickbird and Orbview enable observation of the Earth's surface with sub-meter resolution. Compared to the 30 meter resolution of Landsat 5 TM, the amount of information in the output image was dramatically increased. In this era of high spatial resolution, the estimation of spatial quality of images is gaining attention. Historically, the Modulation Transfer Function (MTF) concept has been used to estimate an imaging system's spatial quality. Sometimes classified by target shapes, various methods were developed in laboratory environment utilizing sinusoidal inputs, periodic bar patterns and narrow slits. On-orbit sensor MTF estimation was performed on 30-meter GSD Landsat4 Thematic Mapper (TM) data from the bridge pulse target as a pulse input . Because of a high resolution sensor s small Ground Sampling Distance (GSD), reasonably sized man-made edge, pulse, and impulse targets can be deployed on a uniform grassy area with accurate control of ground targets using tarps and convex mirrors. All the previous work cited calculated MTF without testing the MTF estimator's performance. In previous report, a numerical generic sensor model had been developed to simulate and improve the performance of on-orbit MTF estimating techniques. Results from the previous sensor modeling report that have been incorporated into standard MTF estimation work include Fermi edge detection and the newly developed 4th order modified Savitzky-Golay (MSG) interpolation technique. Noise sensitivity had been studied by performing simulations on known noise sources and a sensor model. Extensive investigation was done to characterize multi-resolution ground noise. Finally, angle simulation was tested by using synthetic pulse targets with angles from 2 to 15 degrees, several brightness levels, and different noise levels from both ground targets and imaging system. As a continuing research activity using the developed sensor model, this report was dedicated to MTF estimation via pulse input method characterization using the Fermi edge detection and 4th order MSG interpolation method. The relationship between pulse width and MTF value at Nyquist was studied including error detection and correction schemes. Pulse target angle sensitivity was studied by using synthetic targets angled from 2 to 12 degrees. In this report, from the ground and system noise simulation, a minimum SNR value was suggested for a stable MTF value at Nyquist for the pulse method. Target width error detection and adjustment technique based on a smooth transition of MTF profile is presented, which is specifically applicable only to the pulse method with 3 pixel wide targets.

Spatial models to predict ash pH and Electrical Conductivity distribution after a grassland fire in Lithuania

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Cerda, Artemi; Misiūnė, Ieva

2015-04-01

Fire mineralizes the organic matter, increasing the pH level and the amount of dissolved ions (Pereira et al., 2014). The degree of mineralization depends among other factors on fire temperature, burned specie, moisture content, and contact time. The impact of wildland fires it is assessed using the fire severity, an index used in the absence of direct measures (e.g temperature), important to estimate the fire effects in the ecosystems. This impact is observed through the loss of soil organic matter, crown volume, twig diameter, ash colour, among others (Keeley et al., 2009). The effects of fire are highly variable, especially at short spatial scales (Pereira et al., in press), due the different fuel conditions (e.g. moisture, specie distribution, flammability, connectivity, arrangement, etc). This variability poses important challenges to identify the best spatial predictor and have the most accurate spatial visualization of the data. Considering this, the test of several interpolation methods it is assumed to be relevant to have the most reliable map. The aims of this work are I) study the ash pH and Electrical Conductivity (EC) after a grassland fire according to ash colour and II) test several interpolation methods in order to identify the best spatial predictor of pH and EC distribution. The study area is located near Vilnius at 54.42° N and 25.26°E and 154 ma.s.l. After the fire it was designed a plot with a 27 x 9 m space grid. Samples were taken every 3 meters for a total of 40 (Pereira et al., 2013). Ash color was classified according to Úbeda et al. (2009). Ash pH and EC laboratory analysis were carried out according to Pereira et al. (2014). Previous to data comparison and modelling, normality and homogeneity were assessed with the Shapiro-wilk and Levene test. pH data respected the normality and homogeneity, while EC only followed the Gaussian distribution and the homogeneity criteria after a logarithmic transformation. Data spatial correlation was calculated with the Global Moran's I Index. In order to identify the best interpolator, we tested several well known techniques as inverse distance to a power (IDP), with the power of 1, 2, 3, 4 and 5, local polynomial (LP) with the power of 1 (LP1), 2 (LP2) and 3 (LP3), spline with tension (SPT), completely regularized spline (CRS), multiquadratic (MTQ), inverse multiquadratic (IMTQ) thin plate spline (TPS) and ordinary kriging. The best interpolator was the one with the lowest Root mean square error (RMSE). The results shown that on average ash pH was 8.01 (±0.20) and EC (1408± 513.51µm cm3). The coefficient of correlation between both variables was 0.34, p<0.05. Black ash had a significantly higher pH (F=6.29, p<0.05) and EC (F=5.25, p<0.05) than dark grey ash. According to Moran's I index, pH data was significantly (p<0.05) dispersed, while EC had a random pattern. The best spatial predictor for pH was IDW1 (RMSE=0.210), and for EC IMTQ (RMSE=0.141). In both cases the least accurate technique was TPS. pH data did not showed a specific spatial pattern and some high values are very close to high values which shows a great local spatial variability, mainly observed in the northern part of the plot. In relation to EC, the high values were identified in the central part of the plot. In conclusion it was observed that ash pH and EC were different according to fire severity (ash color) and data distribution has a different spatial pattern, despite the significant correlation. pH and EC had different spatial impacts on soil properties in the immediate period after the fire. Acknowledgments POSTFIRE (Soil quality, erosion control and plant cover recovery under different post-fire management scenarios, CGL2013-47862-C2-1-R), funded by the Spanish Ministry of Economy and Competitiveness; Fuegored; RECARE (Preventing and Remediating Degradation of Soils in Europe Through Land Care, FP7-ENV-2013-TWO STAGE), funded by the European Commission; and for the COST action ES1306 (Connecting European connectivity research). References Keeley, J.E. (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire. 18, 116-126. Pereira, P., Úbeda, X., Martin, D., Mataix-Solera, J., Cerdà, A., Burguet, M. (2014) Wildfire effects on extractable elements in ash from a Pinus pinaster forest in Portugal. Hydrological Processes, 28, 3681-3690. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period. Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J., Jordan, A. Burguet, M. (2013) Spatial models for monitoring the spatio-temporal evolution of ashes after fire - a case study of a burnt grassland in Lithuania, Solid Earth, 4, 153-165. Úbeda, X., Pereira, P., Outeiro, L., Martin, D. (2009) Effects of fire temperature on the physical and chemical characteristics of the ash from two plots of cork oak (Quercus suber). Land Degradation and Development, 20(6), 589-608.

A Composite Source Model With Fractal Subevent Size Distribution

NASA Astrophysics Data System (ADS)

Burjanek, J.; Zahradnik, J.

A composite source model, incorporating different sized subevents, provides a pos- sible description of complex rupture processes during earthquakes. The number of subevents with characteristic dimension greater than R is proportional to R-2. The subevents do not overlap with each other, and the sum of their areas equals to the area of the target event (e.g. mainshock) . The subevents are distributed randomly over the fault. Each subevent is modeled as a finite source, using kinematic approach (radial rupture propagation, constant rupture velocity, boxcar slip-velocity function, with constant rise time on the subevent). The final slip at each subevent is related to its characteristic dimension, using constant stress-drop scaling. Variation of rise time with subevent size is a free parameter of modeling. The nucleation point of each subevent is taken as the point closest to mainshock hypocentre. The synthetic Green's functions are calculated by the discrete-wavenumber method in a 1D horizontally lay- ered crustal model in a relatively coarse grid of points covering the fault plane. The Green's functions needed for the kinematic model in a fine grid are obtained by cu- bic spline interpolation. As different frequencies may be efficiently calculated with different sampling, the interpolation simplifies and speeds-up the procedure signifi- cantly. The composite source model described above allows interpretation in terms of a kinematic model with non-uniform final slip and rupture velocity spatial distribu- tions. The 1994 Northridge earthquake (Mw = 6.7) is used as a validation event. The strong-ground motion modeling of the 1999 Athens earthquake (Mw = 5.9) is also performed.

Data Descriptor: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Treesearch

John T. Abatzoglou; Solomon Z. Dobrowski; Sean A. Parks; Katherine C. Hegewisch

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from...

Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

NASA Astrophysics Data System (ADS)

Müller, H.; Haberlandt, U.

2018-01-01

Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best overall performance regarding rainfall statistics are obtained by the method in which the disaggregation process ends at time steps with 7.5 min duration, deriving the 5 min time steps by linear interpolation. With subsequent resampling this method leads to a good representation of manhole flooding and combined sewer overflow volume in terms of hydrological simulations and outperforms the 1280 min approach.

[Spatial pattern of land surface dead combustible fuel load in Huzhong forest area in Great Xing'an Mountains].

PubMed

Liu, Zhi-Hua; Chang, Yu; Chen, Hong-Wei; Zhou, Rui; Jing, Guo-Zhi; Zhang, Hong-Xin; Zhang, Chang-Meng

2008-03-01

By using geo-statistics and based on time-lag classification standard, a comparative study was made on the land surface dead combustible fuels in Huzhong forest area in Great Xing'an Mountains. The results indicated that the first level land surface dead combustible fuel, i. e., 1 h time-lag dead fuel, presented stronger spatial auto-correlation, with an average of 762.35 g x m(-2) and contributing to 55.54% of the total load. Its determining factors were species composition and stand age. The second and third levels land surface dead combustible fuel, i. e., 10 h and 100 h time-lag dead fuels, had a sum of 610.26 g x m(-2), and presented weaker spatial auto-correlation than 1 h time-lag dead fuel. Their determining factor was the disturbance history of forest stand. The complexity and heterogeneity of the factors determining the quality and quantity of forest land surface dead combustible fuels were the main reasons for the relatively inaccurate interpolation. However, the utilization of field survey data coupled with geo-statistics could easily and accurately interpolate the spatial pattern of forest land surface dead combustible fuel loads, and indirectly provide a practical basis for forest management.

Uncertainty of future projections of species distributions in mountainous regions.

PubMed

Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Uncertainty of future projections of species distributions in mountainous regions

PubMed Central

Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. PMID:29320501

A new method for estimating carbon dioxide emissions from transportation at fine spatial scales

PubMed Central

Shu, Yuqin; Reams, Margaret

2016-01-01

Detailed estimates of carbon dioxide (CO2) emissions at fine spatial scales are useful to both modelers and decision makers who are faced with the problem of global warming and climate change. Globally, transport related emissions of carbon dioxide are growing. This letter presents a new method based on the volume-preserving principle in the areal interpolation literature to disaggregate transportation-related CO2 emission estimates from the county-level scale to a 1 km2 grid scale. The proposed volume-preserving interpolation (VPI) method, together with the distance-decay principle, were used to derive emission weights for each grid based on its proximity to highways, roads, railroads, waterways, and airports. The total CO2 emission value summed from the grids within a county is made to be equal to the original county-level estimate, thus enforcing the volume-preserving property. The method was applied to downscale the transportation-related CO2 emission values by county (i.e. parish) for the state of Louisiana into 1 km2 grids. The results reveal a more realistic spatial pattern of CO2 emission from transportation, which can be used to identify the emission ‘hot spots’. Of the four highest transportation-related CO2 emission hotspots in Louisiana, high-emission grids literally covered the entire East Baton Rouge Parish and Orleans Parish, whereas CO2 emission in Jefferson Parish (New Orleans suburb) and Caddo Parish (city of Shreveport) were more unevenly distributed. We argue that the new method is sound in principle, flexible in practice, and the resultant estimates are more accurate than previous gridding approaches. PMID:26997973

Geographically weighted regression based methods for merging satellite and gauge precipitation

NASA Astrophysics Data System (ADS)

Chao, Lijun; Zhang, Ke; Li, Zhijia; Zhu, Yuelong; Wang, Jingfeng; Yu, Zhongbo

2018-03-01

Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate hydrological forecasting. To improve the spatial resolution and quality of satellite precipitation, a three-step satellite and gauge precipitation merging method was formulated in this study: (1) bilinear interpolation is first applied to downscale coarser satellite precipitation to a finer resolution (PS); (2) the (mixed) geographically weighted regression methods coupled with a weighting function are then used to estimate biases of PS as functions of gauge observations (PO) and PS; and (3) biases of PS are finally corrected to produce a merged precipitation product. Based on the above framework, eight algorithms, a combination of two geographically weighted regression methods and four weighting functions, are developed to merge CMORPH (CPC MORPHing technique) precipitation with station observations on a daily scale in the Ziwuhe Basin of China. The geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were used for merging precipitation to avoid the artificial spatial autocorrelation resulting from traditional interpolation methods. The results show that the combination of the MGWR and BI-square function (MGWR-BI) has the best performance (R = 0.863 and RMSE = 7.273 mm/day) among the eight algorithms. The MGWR-BI algorithm was then applied to produce hourly merged precipitation product. Compared to the original CMORPH product (R = 0.208 and RMSE = 1.208 mm/hr), the quality of the merged data is significantly higher (R = 0.724 and RMSE = 0.706 mm/hr). The developed merging method not only improves the spatial resolution and quality of the satellite product but also is easy to implement, which is valuable for hydrological modeling and other applications.

Spatial sound field synthesis and upmixing based on the equivalent source method.

PubMed

Bai, Mingsian R; Hsu, Hoshen; Wen, Jheng-Ciang

2014-01-01

Given scarce number of recorded signals, spatial sound field synthesis with an extended sweet spot is a challenging problem in acoustic array signal processing. To address the problem, a synthesis and upmixing approach inspired by the equivalent source method (ESM) is proposed. The synthesis procedure is based on the pressure signals recorded by a microphone array and requires no source model. The array geometry can also be arbitrary. Four upmixing strategies are adopted to enhance the resolution of the reproduced sound field when there are more channels of loudspeakers than the microphones. Multi-channel inverse filtering with regularization is exploited to deal with the ill-posedness in the reconstruction process. The distance between the microphone and loudspeaker arrays is optimized to achieve the best synthesis quality. To validate the proposed system, numerical simulations and subjective listening experiments are performed. The results demonstrated that all upmixing methods improved the quality of reproduced target sound field over the original reproduction. In particular, the underdetermined ESM interpolation method yielded the best spatial sound field synthesis in terms of the reproduction error, timbral quality, and spatial quality.

Detection and correction of laser induced breakdown spectroscopy spectral background based on spline interpolation method

NASA Astrophysics Data System (ADS)

Tan, Bing; Huang, Min; Zhu, Qibing; Guo, Ya; Qin, Jianwei

2017-12-01

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique that has gained increasing attention because of many applications. The production of continuous background in LIBS is inevitable because of factors associated with laser energy, gate width, time delay, and experimental environment. The continuous background significantly influences the analysis of the spectrum. Researchers have proposed several background correction methods, such as polynomial fitting, Lorenz fitting and model-free methods. However, less of them apply these methods in the field of LIBS Technology, particularly in qualitative and quantitative analyses. This study proposes a method based on spline interpolation for detecting and estimating the continuous background spectrum according to its smooth property characteristic. Experiment on the background correction simulation indicated that, the spline interpolation method acquired the largest signal-to-background ratio (SBR) over polynomial fitting, Lorenz fitting and model-free method after background correction. These background correction methods all acquire larger SBR values than that acquired before background correction (The SBR value before background correction is 10.0992, whereas the SBR values after background correction by spline interpolation, polynomial fitting, Lorentz fitting, and model-free methods are 26.9576, 24.6828, 18.9770, and 25.6273 respectively). After adding random noise with different kinds of signal-to-noise ratio to the spectrum, spline interpolation method acquires large SBR value, whereas polynomial fitting and model-free method obtain low SBR values. All of the background correction methods exhibit improved quantitative results of Cu than those acquired before background correction (The linear correlation coefficient value before background correction is 0.9776. Moreover, the linear correlation coefficient values after background correction using spline interpolation, polynomial fitting, Lorentz fitting, and model-free methods are 0.9998, 0.9915, 0.9895, and 0.9940 respectively). The proposed spline interpolation method exhibits better linear correlation and smaller error in the results of the quantitative analysis of Cu compared with polynomial fitting, Lorentz fitting and model-free methods, The simulation and quantitative experimental results show that the spline interpolation method can effectively detect and correct the continuous background.

Modifications made to ModelMuse to add support for the Saturated-Unsaturated Transport model (SUTRA)

USGS Publications Warehouse

Winston, Richard B.

2014-01-01

This report (1) describes modifications to ModelMuse,as described in U.S. Geological Survey (USGS) Techniques and Methods (TM) 6–A29 (Winston, 2009), to add support for the Saturated-Unsaturated Transport model (SUTRA) (Voss and Provost, 2002; version of September 22, 2010) and (2) supplements USGS TM 6–A29. Modifications include changes to the main ModelMuse window where the model is designed, addition of methods for generating a finite-element mesh suitable for SUTRA, defining how some functions shouldapply when using a finite-element mesh rather than a finite-difference grid (as originally programmed in ModelMuse), and applying spatial interpolation to angles. In addition, the report describes ways of handling objects on the front view of the model and displaying data. A tabulation contains a summary of the new or modified dialog boxes.

Time series evapotranspiration maps at a regional scale: A methodology, evaluation, and their use in water resources management

NASA Astrophysics Data System (ADS)

Gowda, P. H.

2016-12-01

Evapotranspiration (ET) is an important process in ecosystems' water budget and closely linked to its productivity. Therefore, regional scale daily time series ET maps developed at high and medium resolutions have large utility in studying the carbon-energy-water nexus and managing water resources. There are efforts to develop such datasets on a regional to global scale but often faced with the limitations of spatial-temporal resolution tradeoffs in satellite remote sensing technology. In this study, we developed frameworks for generating high and medium resolution daily ET maps from Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data, respectively. For developing high resolution (30-m) daily time series ET maps with Landsat TM data, the series version of Two Source Energy Balance (TSEB) model was used to compute sensible and latent heat fluxes of soil and canopy separately. Landsat 5 (2000-2011) and Landsat 8 (2013-2014) imageries for row 28/35 and 27/36 covering central Oklahoma was used. MODIS data (2001-2014) covering Oklahoma and Texas Panhandle was used to develop medium resolution (250-m), time series daily ET maps with SEBS (Surface Energy Balance System) model. An extensive network of weather stations managed by Texas High Plains ET Network and Oklahoma Mesonet was used to generate spatially interpolated inputs of air temperature, relative humidity, wind speed, solar radiation, pressure, and reference ET. A linear interpolation sub-model was used to estimate the daily ET between the image acquisition days. Accuracy assessment of daily ET maps were done against eddy covariance data from two grassland sites at El Reno, OK. Statistical results indicated good performance by modeling frameworks developed for deriving time series ET maps. Results indicated that the proposed ET mapping framework is suitable for deriving daily time series ET maps at regional scale with Landsat and MODIS data.

ModelMuse - A Graphical User Interface for MODFLOW-2005 and PHAST

USGS Publications Warehouse

Winston, Richard B.

2009-01-01

ModelMuse is a graphical user interface (GUI) for the U.S. Geological Survey (USGS) models MODFLOW-2005 and PHAST. This software package provides a GUI for creating the flow and transport input file for PHAST and the input files for MODFLOW-2005. In ModelMuse, the spatial data for the model is independent of the grid, and the temporal data is independent of the stress periods. Being able to input these data independently allows the user to redefine the spatial and temporal discretization at will. This report describes the basic concepts required to work with ModelMuse. These basic concepts include the model grid, data sets, formulas, objects, the method used to assign values to data sets, and model features. The ModelMuse main window has a top, front, and side view of the model that can be used for editing the model, and a 3-D view of the model that can be used to display properties of the model. ModelMuse has tools to generate and edit the model grid. It also has a variety of interpolation methods and geographic functions that can be used to help define the spatial variability of the model. ModelMuse can be used to execute both MODFLOW-2005 and PHAST and can also display the results of MODFLOW-2005 models. An example of using ModelMuse with MODFLOW-2005 is included in this report. Several additional examples are described in the help system for ModelMuse, which can be accessed from the Help menu.

DEM interpolation weight calculation modulus based on maximum entropy

NASA Astrophysics Data System (ADS)

Chen, Tian-wei; Yang, Xia

2015-12-01

There is negative-weight in traditional interpolation of gridding DEM, in the article, the principle of Maximum Entropy is utilized to analyze the model system which depends on modulus of space weight. Negative-weight problem of the DEM interpolation is researched via building Maximum Entropy model, and adding nonnegative, first and second order's Moment constraints, the negative-weight problem is solved. The correctness and accuracy of the method was validated with genetic algorithm in matlab program. The method is compared with the method of Yang Chizhong interpolation and quadratic program. Comparison shows that the volume and scaling of Maximum Entropy's weight is fit to relations of space and the accuracy is superior to the latter two.

An assessment of air pollutant exposure methods in Mexico City, Mexico.

PubMed

Rivera-González, Luis O; Zhang, Zhenzhen; Sánchez, Brisa N; Zhang, Kai; Brown, Daniel G; Rojas-Bracho, Leonora; Osornio-Vargas, Alvaro; Vadillo-Ortega, Felipe; O'Neill, Marie S

2015-05-01

Geostatistical interpolation methods to estimate individual exposure to outdoor air pollutants can be used in pregnancy cohorts where personal exposure data are not collected. Our objectives were to a) develop four assessment methods (citywide average (CWA); nearest monitor (NM); inverse distance weighting (IDW); and ordinary Kriging (OK)), and b) compare daily metrics and cross-validations of interpolation models. We obtained 2008 hourly data from Mexico City's outdoor air monitoring network for PM10, PM2.5, O3, CO, NO2, and SO2 and constructed daily exposure metrics for 1,000 simulated individual locations across five populated geographic zones. Descriptive statistics from all methods were calculated for dry and wet seasons, and by zone. We also evaluated IDW and OK methods' ability to predict measured concentrations at monitors using cross validation and a coefficient of variation (COV). All methods were performed using SAS 9.3, except ordinary Kriging which was modeled using R's gstat package. Overall, mean concentrations and standard deviations were similar among the different methods for each pollutant. Correlations between methods were generally high (r=0.77 to 0.99). However, ranges of estimated concentrations determined by NM, IDW, and OK were wider than the ranges for CWA. Root mean square errors for OK were consistently equal to or lower than for the IDW method. OK standard errors varied considerably between pollutants and the computed COVs ranged from 0.46 (least error) for SO2 and PM10 to 3.91 (most error) for PM2.5. OK predicted concentrations measured at the monitors better than IDW and NM. Given the similarity in results for the exposure methods, OK is preferred because this method alone provides predicted standard errors which can be incorporated in statistical models. The daily estimated exposures calculated using these different exposure methods provide flexibility to evaluate multiple windows of exposure during pregnancy, not just trimester or pregnancy-long exposures. Many studies evaluating associations between outdoor air pollution and adverse pregnancy outcomes rely on outdoor air pollution monitoring data linked to information gathered from large birth registries, and often lack residence location information needed to estimate individual exposure. This study simulated 1,000 residential locations to evaluate four air pollution exposure assessment methods, and describes possible exposure misclassification from using spatial averaging versus geostatistical interpolation models. An implication of this work is that policies to reduce air pollution and exposure among pregnant women based on epidemiologic literature should take into account possible error in estimates of effect when spatial averages alone are evaluated.

CFA-aware features for steganalysis of color images

NASA Astrophysics Data System (ADS)

Goljan, Miroslav; Fridrich, Jessica

2015-03-01

Color interpolation is a form of upsampling, which introduces constraints on the relationship between neighboring pixels in a color image. These constraints can be utilized to substantially boost the accuracy of steganography detectors. In this paper, we introduce a rich model formed by 3D co-occurrences of color noise residuals split according to the structure of the Bayer color filter array to further improve detection. Some color interpolation algorithms, AHD and PPG, impose pixel constraints so tight that extremely accurate detection becomes possible with merely eight features eliminating the need for model richification. We carry out experiments on non-adaptive LSB matching and the content-adaptive algorithm WOW on five different color interpolation algorithms. In contrast to grayscale images, in color images that exhibit traces of color interpolation the security of WOW is significantly lower and, depending on the interpolation algorithm, may even be lower than non-adaptive LSB matching.

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

USGS Publications Warehouse

Funk, Chris; Peterson, Pete; Landsfeld, Martin; Pedreros, Diego; Verdin, James; Shukla, Shraddhanand; Husak, Gregory; Rowland, James; Harrison, Laura; Hoell, Andrew; Michaelsen, Joel

2015-01-01

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

Edge detection - Image-plane versus digital processing

NASA Technical Reports Server (NTRS)

Huck, Friedrich O.; Fales, Carl L.; Park, Stephen K.; Triplett, Judith A.

1987-01-01

To optimize edge detection with the familiar Laplacian-of-Gaussian operator, it has become common to implement this operator with a large digital convolution mask followed by some interpolation of the processed data to determine the zero crossings that locate edges. It is generally recognized that this large mask causes substantial blurring of fine detail. It is shown that the spatial detail can be improved by a factor of about four with either the Wiener-Laplacian-of-Gaussian filter or an image-plane processor. The Wiener-Laplacian-of-Gaussian filter minimizes the image-gathering degradations if the scene statistics are at least approximately known and also serves as an interpolator to determine the desired zero crossings directly. The image-plane processor forms the Laplacian-of-Gaussian response by properly combining the optical design of the image-gathering system with a minimal three-by-three lateral-inhibitory processing mask. This approach, which is suggested by Marr's model of early processing in human vision, also reduces data processing by about two orders of magnitude and data transmission by up to an order of magnitude.

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

PubMed Central

Funk, Chris; Peterson, Pete; Landsfeld, Martin; Pedreros, Diego; Verdin, James; Shukla, Shraddhanand; Husak, Gregory; Rowland, James; Harrison, Laura; Hoell, Andrew; Michaelsen, Joel

2015-01-01

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia. PMID:26646728

New gridded database of clear-sky solar radiation derived from ground-based observations over Europe

NASA Astrophysics Data System (ADS)

Bartok, Blanka; Wild, Martin; Sanchez-Lorenzo, Arturo; Hakuba, Maria Z.

2017-04-01

Since aerosols modify the entire energy balance of the climate system through different processes, assessments regarding aerosol multiannual variability are highly required by the climate modelling community. Because of the scarcity of long-term direct aerosol measurements, the retrieval of aerosol data/information from other type of observations or satellite measurements are very relevant. One approach frequently used in the literature is analyze of the clear-sky solar radiation which offer a better overview of changes in aerosol content. In the study first two empirical methods are elaborated in order to separate clear-sky situations from observed values of surface solar radiation available at the World Radiation Data Center (WRDC), St. Petersburg. The daily data has been checked for temporal homogeneity by applying the MASH method (Szentimrey, 2003). In the first approach, clear sky situations are detected based on clearness index, namely the ratio of the surface solar radiation to the extraterrestrial solar irradiation. In the second approach the observed values of surface solar radiation are compared to the climatology of clear-sky surface solar radiation calculated by the MAGIC radiation code (Muller et al. 2009). In both approaches the clear-sky radiation values highly depend on the applied thresholds. In order to eliminate this methodological error a verification of clear-sky detection is envisaged through a comparison with the values obtained by a high time resolution clear-sky detection and interpolation algorithm (Long and Ackermann, 2000) making use of the high quality data from the Baseline Surface Radiation Network (BSRN). As the consequences clear-sky data series are obtained for 118 European meteorological stations. Next a first attempt has been done in order to interpolate the point-wise clear-sky radiation data by applying the MISH (Meteorological Interpolation based on Surface Homogenized Data Basis) method for the spatial interpolation of surface meteorological elements developed at the Hungarian Meteorological Service (Szentimrey 2007). In this way new gridded database of clear-sky solar radiation is created suitable for further investigations regarding the role of aerosols in the energy budget, and also for validations of climate model outputs. References 1. Long CN, Ackerman TP. 2000. Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects, J. Geophys. Res., 105(D12), 15609-15626, doi:10.1029/2000JD900077. 2. Mueller R, Matsoukas C, Gratzki A, Behr H, Hollmann R. 2009. The CM-SAF operational scheme for the satellite based retrieval of solar surface irradiance - a LUT based eigenvector hybrid approach, Remote Sensing of Environment, 113 (5), 1012-1024, doi:10.1016/j.rse.2009. 01.012 3. Szentimrey T. 2014. Multiple Analysis of Series for Homogenization (MASHv3.03), Hungarian Meteorological Service, https://www.met.hu/en/omsz/rendezvenyek/homogenization_and_interpolation/software/ 4. Szentimrey T. Bihari Z. 2014: Meteorological Interpolation based on Surface Homogenized Data Basis (MISHv1.03) https://www.met.hu/en/omsz/rendezvenyek/homogenization_and_interpolation/software/

[Assessment on ecological security spatial differences of west areas of Liaohe River based on GIS].

PubMed

Wang, Geng; Wu, Wei

2005-09-01

Ecological security assessment and early warning research have spatiality; non-linearity; randomicity, it is needed to deal with much spatial information. Spatial analysis and data management are advantages of GIS, it can define distribution trend and spatial relations of environmental factors, and show ecological security pattern graphically. The paper discusses the method of ecological security spatial differences of west areas of Liaohe River based on GIS and ecosystem non-health. First, studying on pressure-state-response (P-S-R) assessment indicators system, investigating in person and gathering information; Second, digitizing the river, applying fuzzy AHP to put weight, quantizing and calculating by fuzzy comparing; Last, establishing grid data-base; expounding spatial differences of ecological security by GIS Interpolate and Assembly.

Long-term Observations of Intense Precipitation Small-scale Spatial Variability in a Semi-arid Catchment

NASA Astrophysics Data System (ADS)

Cropp, E. L.; Hazenberg, P.; Castro, C. L.; Demaria, E. M.

2017-12-01

In the southwestern US, the summertime North American Monsoon (NAM) provides about 60% of the region's annual precipitation. Recent research using high-resolution atmospheric model simulations and retrospective predictions has shown that since the 1950's, and more specifically in the last few decades, the mean daily precipitation in the southwestern U.S. during the NAM has followed a decreasing trend. Furthermore, days with more extreme precipitation have intensified. The current work focuses the impact of these long-term changes on the observed small-scale spatial variability of intense precipitation. Since limited long-term high-resolution observational data exist to support such climatological-induced spatial changes in precipitation frequency and intensity, the current work utilizes observations from the USDA-ARS Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona. Within this 150 km^2 catchment over 90 rain gauges have been installed since the 1950s, measuring at sub-hourly resolution. We have applied geospatial analyses and the kriging interpolation technique to identify long-term changes in the spatial and temporal correlation and anisotropy of intense precipitation. The observed results will be compared with the previously model simulated results, as well as related to large-scale variations in climate patterns, such as the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).

Temporal and spatial variations of rainfall erosivity in Southern Taiwan

NASA Astrophysics Data System (ADS)

Lee, Ming-Hsi; Lin, Huan-Hsuan; Chu, Chun-Kuang

2014-05-01

Soil erosion models are essential in developing effective soil and water resource conservation strategies. Soil erosion is generally evaluated using the Universal Soil Loss Equation (USLE) with an appropriate regional scale description. Among factors in the USLE model, the rainfall erosivity index (R) provides one of the clearest indications of the effects of climate change. Accurate estimation of rainfall erosivity requires continuous rainfall data; however, such data rarely demonstrate good spatial and temporal coverage. The data set consisted of 9240 storm events for the period 1993 to 2011, monitored by 27 rainfall stations of the Central Weather Bureau (CWB) in southern Taiwan, was used to analyze the temporal-spatial variations of rainfall erosivity. The spatial distribution map was plotted based on rainfall erosivity by the Kriging interpolation method. Results indicated that rainfall erosivity is mainly concentrated in rainy season from June to November typically contributed 90% of the yearly R factor. The temporal variations of monthly rainfall erosivity during June to November and annual rainfall erosivity have increasing trend from 1993 to 2011. There is an increasing trend from southwest to northeast in spatial distribution of rainfall erosivity in southern Taiwan. The results further indicated that there is a higher relationship between elevation and rainfall erosivity. The method developed in this study may also be useful for sediment disasters on Climate Change.

[Research on fast implementation method of image Gaussian RBF interpolation based on CUDA].

PubMed

Chen, Hao; Yu, Haizhong

2014-04-01

Image interpolation is often required during medical image processing and analysis. Although interpolation method based on Gaussian radial basis function (GRBF) has high precision, the long calculation time still limits its application in field of image interpolation. To overcome this problem, a method of two-dimensional and three-dimensional medical image GRBF interpolation based on computing unified device architecture (CUDA) is proposed in this paper. According to single instruction multiple threads (SIMT) executive model of CUDA, various optimizing measures such as coalesced access and shared memory are adopted in this study. To eliminate the edge distortion of image interpolation, natural suture algorithm is utilized in overlapping regions while adopting data space strategy of separating 2D images into blocks or dividing 3D images into sub-volumes. Keeping a high interpolation precision, the 2D and 3D medical image GRBF interpolation achieved great acceleration in each basic computing step. The experiments showed that the operative efficiency of image GRBF interpolation based on CUDA platform was obviously improved compared with CPU calculation. The present method is of a considerable reference value in the application field of image interpolation.

Methodology for Image-Based Reconstruction of Ventricular Geometry for Patient-Specific Modeling of Cardiac Electrophysiology

PubMed Central

Prakosa, A.; Malamas, P.; Zhang, S.; Pashakhanloo, F.; Arevalo, H.; Herzka, D. A.; Lardo, A.; Halperin, H.; McVeigh, E.; Trayanova, N.; Vadakkumpadan, F.

2014-01-01

Patient-specific modeling of ventricular electrophysiology requires an interpolated reconstruction of the 3-dimensional (3D) geometry of the patient ventricles from the low-resolution (Lo-res) clinical images. The goal of this study was to implement a processing pipeline for obtaining the interpolated reconstruction, and thoroughly evaluate the efficacy of this pipeline in comparison with alternative methods. The pipeline implemented here involves contouring the epi- and endocardial boundaries in Lo-res images, interpolating the contours using the variational implicit functions method, and merging the interpolation results to obtain the ventricular reconstruction. Five alternative interpolation methods, namely linear, cubic spline, spherical harmonics, cylindrical harmonics, and shape-based interpolation were implemented for comparison. In the thorough evaluation of the processing pipeline, Hi-res magnetic resonance (MR), computed tomography (CT), and diffusion tensor (DT) MR images from numerous hearts were used. Reconstructions obtained from the Hi-res images were compared with the reconstructions computed by each of the interpolation methods from a sparse sample of the Hi-res contours, which mimicked Lo-res clinical images. Qualitative and quantitative comparison of these ventricular geometry reconstructions showed that the variational implicit functions approach performed better than others. Additionally, the outcomes of electrophysiological simulations (sinus rhythm activation maps and pseudo-ECGs) conducted using models based on the various reconstructions were compared. These electrophysiological simulations demonstrated that our implementation of the variational implicit functions-based method had the best accuracy. PMID:25148771

Informing Species Conservation at Multiple Scales Using Data Collected for Marine Mammal Stock Assessments

PubMed Central

Grech, Alana; Sheppard, James; Marsh, Helene

2011-01-01

Background Conservation planning and the design of marine protected areas (MPAs) requires spatially explicit information on the distribution of ecological features. Most species of marine mammals range over large areas and across multiple planning regions. The spatial distributions of marine mammals are difficult to predict using habitat modelling at ecological scales because of insufficient understanding of their habitat needs, however, relevant information may be available from surveys conducted to inform mandatory stock assessments. Methodology and Results We use a 20-year time series of systematic aerial surveys of dugong (Dugong dugong) abundance to create spatially-explicit models of dugong distribution and relative density at the scale of the coastal waters of northeast Australia (∼136,000 km2). We interpolated the corrected data at the scale of 2 km * 2 km planning units using geostatistics. Planning units were classified as low, medium, high and very high dugong density on the basis of the relative density of dugongs estimated from the models and a frequency analysis. Torres Strait was identified as the most significant dugong habitat in northeast Australia and the most globally significant habitat known for any member of the Order Sirenia. The models are used by local, State and Federal agencies to inform management decisions related to the Indigenous harvest of dugongs, gill-net fisheries and Australia's National Representative System of Marine Protected Areas. Conclusion/Significance In this paper we demonstrate that spatially-explicit population models add value to data collected for stock assessments, provide a robust alternative to predictive habitat distribution models, and inform species conservation at multiple scales. PMID:21464933

Mathematical modelling for distribution of heavy metals in estuary area of Red River (Vietnam)

NASA Astrophysics Data System (ADS)

Nguyen, N. T. T.; Volkova, I. V.

2018-05-01

In this paper, the authors studied the features of spatial distribution of some heavy metals (Pb, Hg, As) in the system “suspended substance - bottom sediments” in the mouth area of the Red River (Vietnam). A mathematical modelling for diffusion processes of heavy metals in a suspended form, in bottom sediments and the spatial analysis for the results of these models were proposed and implemented. The studies were carried out during main hydrological seasons of 2014 - 2016 (during the flood and inter-natal periods). The propagation of heavy metals was modeled by solving the equation of turbulent diffusion. A spatial analysis of the content of heavy metals in the suspended form and in the bottom sediments was implemented by using the interpolation model in ArcGIS 10.2.2. The distribution of Pb, Hg, As concentration of the suspended form and bottom sediment phases in the estuary area of the Red River was characterized by maximum in the mouths of the branches and general decreasing gradient towards the sea. Maximum concentrations of Pb, Hg in suspended forms were observed in the surface layer of water at the river-sea barrier. The content of Hg and As in the estuary region of the Red River was observed in the following order: SSsurf< SSbott< BS; and content of Pb – SS >BS.

Bias-adjusted satellite-based rainfall estimates for predicting floods: Narayani Basin

USGS Publications Warehouse

Shrestha, M.S.; Artan, G.A.; Bajracharya, S.R.; Gautam, D.K.; Tokar, S.A.

2011-01-01

In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32000km2) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC-RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC-RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC-RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction. ?? 2011 The Authors. Journal of Flood Risk Management ?? 2011 The Chartered Institution of Water and Environmental Management.

Bias-adjusted satellite-based rainfall estimates for predicting floods: Narayani Basin

USGS Publications Warehouse

Artan, Guleid A.; Tokar, S.A.; Gautam, D.K.; Bajracharya, S.R.; Shrestha, M.S.

2011-01-01

In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32 000 km2) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC_RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC_RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC_RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction.

Simple, Fast and Effective Correction for Irradiance Spatial Nonuniformity in Measurement of IVs of Large Area Cells at NREL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moriarty, Tom

The NREL cell measurement lab measures the IV parameters of cells of multiple sizes and configurations. A large contributing factor to errors and uncertainty in Jsc, Imax, Pmax and efficiency can be the irradiance spatial nonuniformity. Correcting for this nonuniformity through its precise and frequent measurement can be very time consuming. This paper explains a simple, fast and effective method based on bicubic interpolation for determining and correcting for spatial nonuniformity and verification of the method's efficacy.

Learning Kriging by an instructive program.

NASA Astrophysics Data System (ADS)

Cuador, José

2016-04-01

There are three types of problem classification: the deterministic, the approximated and the stochastic problems. First, in the deterministic problems the law of the phenomenon and the data are known in the entire domain and for each instant of time. In the approximated problems, the law of the phenomenon behavior is unknown but the data can be known in the entire domain and for each instant of time. In the stochastic problems much of the law and the data are unknown in the domain, so in this case the spatial behavior of the data can only be explained with probabilistic laws. This is the most important reason why the students of geo-sciences careers and others related careers need to take courses in advance estimation methods. A good example of this situation is the estimation grades in ore mineral deposit for which the Geostatistics was formalized by G. Matheron in 1962 [6]. Geostatistics is defined as the application of the theory of Random Function to the recognition and estimation of natural phenomenon [4]. Nowadays, Geostatistics is widely used in several fields of earth sciences, for example: Mining, Oil exploration, Environment, Agricultural, Forest and others [3]. It provides a wide variety of tools for spatial data analysis and allows analysing models which are subjected to degrees of uncertainty with the rigor of mathematics and formal statistical analysis [9]. Adequate models for the Kriging interpolator has been developed according to the data behavior; however there are two key steps in applying this interpolator properly: the semivariogram determination and the Kriging neighborhood selection. The main objective of this paper is to present these two elements using an instructive program.

A collocation--Galerkin finite element model of cardiac action potential propagation.

PubMed

Rogers, J M; McCulloch, A D

1994-08-01

A new computational method was developed for modeling the effects of the geometric complexity, nonuniform muscle fiber orientation, and material inhomogeneity of the ventricular wall on cardiac impulse propagation. The method was used to solve a modification to the FitzHugh-Nagumo system of equations. The geometry, local muscle fiber orientation, and material parameters of the domain were defined using linear Lagrange or cubic Hermite finite element interpolation. Spatial variations of time-dependent excitation and recovery variables were approximated using cubic Hermite finite element interpolation, and the governing finite element equations were assembled using the collocation method. To overcome the deficiencies of conventional collocation methods on irregular domains, Galerkin equations for the no-flux boundary conditions were used instead of collocation equations for the boundary degrees-of-freedom. The resulting system was evolved using an adaptive Runge-Kutta method. Converged two-dimensional simulations of normal propagation showed that this method requires less CPU time than a traditional finite difference discretization. The model also reproduced several other physiologic phenomena known to be important in arrhythmogenesis including: Wenckebach periodicity, slowed propagation and unidirectional block due to wavefront curvature, reentry around a fixed obstacle, and spiral wave reentry. In a new result, we observed wavespeed variations and block due to nonuniform muscle fiber orientation. The findings suggest that the finite element method is suitable for studying normal and pathological cardiac activation and has significant advantages over existing techniques.

Hybrid Multiscale Finite Volume Method for Advection-Diffusion Equations Subject to Heterogeneous Reactive Boundary Conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barajas-Solano, David A.; Tartakovsky, A. M.

2016-10-13

We present a hybrid scheme for the coupling of macro and microscale continuum models for reactive contaminant transport in fractured and porous media. The transport model considered is the advection-dispersion equation, subject to linear heterogeneous reactive boundary conditions. The Multiscale Finite Volume method (MsFV) is employed to define an approximation to the microscale concentration field defined in terms of macroscopic or \\emph{global} degrees of freedom, together with local interpolator and corrector functions capturing microscopic spatial variability. The macroscopic mass balance relations for the MsFV global degrees of freedom are coupled with the macroscopic model, resulting in a global problem for the simultaneous time-stepping of all macroscopic degrees of freedom throughout the domain. In order to perform the hybrid coupling, the micro and macroscale models are applied over overlapping subdomains of the simulation domain, with the overlap denoted as the handshake subdomainmore » $$\\Omega^{hs}$$, over which continuity of concentration and transport fluxes between models is enforced. Continuity of concentration is enforced by posing a restriction relation between models over $$\\Omega^{hs}$$. Continuity of fluxes is enforced by prolongating the macroscopic model fluxes across the boundary of $$\\Omega^{hs}$$ to microscopic resolution. The microscopic interpolator and corrector functions are solutions to local microscopic advection-diffusion problems decoupled from the global degrees of freedom and from each other by virtue of the MsFV decoupling ansatz. The error introduced by the decoupling ansatz is reduced iteratively by the preconditioned GMRES algorithm, with the hybrid MsFV operator serving as the preconditioner.« less

Finite-frequency structural sensitivities of short-period compressional body waves

NASA Astrophysics Data System (ADS)

Fuji, Nobuaki; Chevrot, Sébastien; Zhao, Li; Geller, Robert J.; Kawai, Kenji

2012-07-01

We present an extension of the method recently introduced by Zhao & Chevrot for calculating Fréchet kernels from a precomputed database of strain Green's tensors by normal mode summation. The extension involves two aspects: (1) we compute the strain Green's tensors using the Direct Solution Method, which allows us to go up to frequencies as high as 1 Hz; and (2) we develop a spatial interpolation scheme so that the Green's tensors can be computed with a relatively coarse grid, thus improving the efficiency in the computation of the sensitivity kernels. The only requirement is that the Green's tensors be computed with a fine enough spatial sampling rate to avoid spatial aliasing. The Green's tensors can then be interpolated to any location inside the Earth, avoiding the need to store and retrieve strain Green's tensors for a fine sampling grid. The interpolation scheme not only significantly reduces the CPU time required to calculate the Green's tensor database and the disk space to store it, but also enhances the efficiency in computing the kernels by reducing the number of I/O operations needed to retrieve the Green's tensors. Our new implementation allows us to calculate sensitivity kernels for high-frequency teleseismic body waves with very modest computational resources such as a laptop. We illustrate the potential of our approach for seismic tomography by computing traveltime and amplitude sensitivity kernels for high frequency P, PKP and Pdiff phases. A comparison of our PKP kernels with those computed by asymptotic ray theory clearly shows the limits of the latter. With ray theory, it is not possible to model waves diffracted by internal discontinuities such as the core-mantle boundary, and it is also difficult to compute amplitudes for paths close to the B-caustic of the PKP phase. We also compute waveform partial derivatives for different parts of the seismic wavefield, a key ingredient for high resolution imaging by waveform inversion. Our computations of partial derivatives in the time window where PcP precursors are commonly observed show that the distribution of sensitivity is complex and counter-intuitive, with a large contribution from the mid-mantle region. This clearly emphasizes the need to use accurate and complete partial derivatives in waveform inversion.

Improving the accuracy of livestock distribution estimates through spatial interpolation.

PubMed

Bryssinckx, Ward; Ducheyne, Els; Muhwezi, Bernard; Godfrey, Sunday; Mintiens, Koen; Leirs, Herwig; Hendrickx, Guy

2012-11-01

Animal distribution maps serve many purposes such as estimating transmission risk of zoonotic pathogens to both animals and humans. The reliability and usability of such maps is highly dependent on the quality of the input data. However, decisions on how to perform livestock surveys are often based on previous work without considering possible consequences. A better understanding of the impact of using different sample designs and processing steps on the accuracy of livestock distribution estimates was acquired through iterative experiments using detailed survey. The importance of sample size, sample design and aggregation is demonstrated and spatial interpolation is presented as a potential way to improve cattle number estimates. As expected, results show that an increasing sample size increased the precision of cattle number estimates but these improvements were mainly seen when the initial sample size was relatively low (e.g. a median relative error decrease of 0.04% per sampled parish for sample sizes below 500 parishes). For higher sample sizes, the added value of further increasing the number of samples declined rapidly (e.g. a median relative error decrease of 0.01% per sampled parish for sample sizes above 500 parishes. When a two-stage stratified sample design was applied to yield more evenly distributed samples, accuracy levels were higher for low sample densities and stabilised at lower sample sizes compared to one-stage stratified sampling. Aggregating the resulting cattle number estimates yielded significantly more accurate results because of averaging under- and over-estimates (e.g. when aggregating cattle number estimates from subcounty to district level, P <0.009 based on a sample of 2,077 parishes using one-stage stratified samples). During aggregation, area-weighted mean values were assigned to higher administrative unit levels. However, when this step is preceded by a spatial interpolation to fill in missing values in non-sampled areas, accuracy is improved remarkably. This counts especially for low sample sizes and spatially even distributed samples (e.g. P <0.001 for a sample of 170 parishes using one-stage stratified sampling and aggregation on district level). Whether the same observations apply on a lower spatial scale should be further investigated.

Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP

NASA Astrophysics Data System (ADS)

Hofierka, Jaroslav; Lacko, Michal; Zubal, Stanislav

2017-10-01

In this paper, we describe the parallelization of three complex and computationally intensive modules of GRASS GIS using the OpenMP application programming interface for multi-core computers. These include the v.surf.rst module for spatial interpolation, the r.sun module for solar radiation modeling and the r.sim.water module for water flow simulation. We briefly describe the functionality of the modules and parallelization approaches used in the modules. Our approach includes the analysis of the module's functionality, identification of source code segments suitable for parallelization and proper application of OpenMP parallelization code to create efficient threads processing the subtasks. We document the efficiency of the solutions using the airborne laser scanning data representing land surface in the test area and derived high-resolution digital terrain model grids. We discuss the performance speed-up and parallelization efficiency depending on the number of processor threads. The study showed a substantial increase in computation speeds on a standard multi-core computer while maintaining the accuracy of results in comparison to the output from original modules. The presented parallelization approach showed the simplicity and efficiency of the parallelization of open-source GRASS GIS modules using OpenMP, leading to an increased performance of this geospatial software on standard multi-core computers.

Mapping extreme rainfall in the Northwest Portugal region: statistical analysis and spatial modelling

NASA Astrophysics Data System (ADS)

Santos, Monica; Fragoso, Marcelo

2010-05-01

Extreme precipitation events are one of the causes of natural hazards, such as floods and landslides, making its investigation so important, and this research aims to contribute to the study of the extreme rainfall patterns in a Portuguese mountainous area. The study area is centred on the Arcos de Valdevez county, located in the northwest region of Portugal, the rainiest of the country, with more than 3000 mm of annual rainfall at the Peneda-Gerês mountain system. This work focus on two main subjects related with the precipitation variability on the study area. First, a statistical analysis of several precipitation parameters is carried out, using daily data from 17 rain-gauges with a complete record for the 1960-1995 period. This approach aims to evaluate the main spatial contrasts regarding different aspects of the rainfall regime, described by ten parameters and indices of precipitation extremes (e.g. mean annual precipitation, the annual frequency of precipitation days, wet spells durations, maximum daily precipitation, maximum of precipitation in 30 days, number of days with rainfall exceeding 100 mm and estimated maximum daily rainfall for a return period of 100 years). The results show that the highest precipitation amounts (from annual to daily scales) and the higher frequency of very abundant rainfall events occur in the Serra da Peneda and Gerês mountains, opposing to the valleys of the Lima, Minho and Vez rivers, with lower precipitation amounts and less frequent heavy storms. The second purpose of this work is to find a method of mapping extreme rainfall in this mountainous region, investigating the complex influence of the relief (e.g. elevation, topography) on the precipitation patterns, as well others geographical variables (e.g. distance from coast, latitude), applying tested geo-statistical techniques (Goovaerts, 2000; Diodato, 2005). Models of linear regression were applied to evaluate the influence of different geographical variables (altitude, latitude, distance from sea and distance to the highest orographic barrier) on the rainfall behaviours described by the studied variables. The techniques of spatial interpolation evaluated include univariate and multivariate methods: cokriging, kriging, IDW (inverse distance weighted) and multiple linear regression. Validation procedures were used, assessing the estimated errors in the analysis of descriptive statistics of the models. Multiple linear regression models produced satisfactory results in relation to 70% of the rainfall parameters, suggested by lower average percentage of error. However, the results also demonstrates that there is no an unique and ideal model, depending on the rainfall parameter in consideration. Probably, the unsatisfactory results obtained in relation to some rainfall parameters was motivated by constraints as the spatial complexity of the precipitation patterns, as well as to the deficient spatial coverage of the territory by the rain-gauges network. References Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. Internacional Journal of Climatology, 25(3), 351-363. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113 - 129.

Targeting trachoma control through risk mapping: the example of Southern Sudan.

PubMed

Clements, Archie C A; Kur, Lucia W; Gatpan, Gideon; Ngondi, Jeremiah M; Emerson, Paul M; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H

2010-08-17

Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1-9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention.

Spatial Model of Sky Brightness Magnitude in Langkawi Island, Malaysia

NASA Astrophysics Data System (ADS)

Redzuan Tahar, Mohammad; Kamarudin, Farahana; Umar, Roslan; Khairul Amri Kamarudin, Mohd; Sabri, Nor Hazmin; Ahmad, Karzaman; Rahim, Sobri Abdul; Sharul Aikal Baharim, Mohd

2017-03-01

Sky brightness is an essential topic in the field of astronomy, especially for optical astronomical observations that need very clear and dark sky conditions. This study presents the spatial model of sky brightness magnitude in Langkawi Island, Malaysia. Two types of Sky Quality Meter (SQM) manufactured by Unihedron are used to measure the sky brightness on a moonless night (or when the Moon is below the horizon), when the sky is cloudless and the locations are at least 100 m from the nearest light source. The selected locations are marked by their GPS coordinates. The sky brightness data obtained in this study were interpolated and analyzed using a Geographic Information System (GIS), thus producing a spatial model of sky brightness that clearly shows the dark and bright sky areas in Langkawi Island. Surprisingly, our results show the existence of a few dark sites nearby areas of high human activity. The sky brightness of 21.45 mag arcsec{}-2 in the Johnson-Cousins V-band, as the average of sky brightness equivalent to 2.8 × {10}-4{cd} {{{m}}}-2 over the entire island, is an indication that the island is, overall, still relatively dark. However, the amount of development taking place might reduce the number in the near future as the island is famous as a holiday destination.

Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan

PubMed Central

Clements, Archie C. A.; Kur, Lucia W.; Gatpan, Gideon; Ngondi, Jeremiah M.; Emerson, Paul M.; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H.

2010-01-01

Background Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. Methods/Principal Findings A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1–9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. Conclusion In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention. PMID:20808910

Using "snapshot" measurements of CH4 fluxes from peatlands to estimate annual budgets: interpolation vs. modelling.

NASA Astrophysics Data System (ADS)

Green, Sophie M.; Baird, Andy J.

2016-04-01

There is growing interest in estimating annual budgets of peatland-atmosphere carbon dioxide (CO2) and methane (CH4) exchanges. Such budgeting is required for calculating peatland carbon balance and the radiative forcing impact of peatlands on climate. There have been multiple approaches used to estimate CO2 budgets; however, there is a limited literature regarding the modelling of annual CH4 budgets. Using data collected from flux chamber tests in an area of blanket peatland in North Wales, we compared annual estimates of peatland-atmosphere CH4 emissions using an interpolation approach and an additive and multiplicative modelling approach. Flux-chamber measurements represent a snapshot of the conditions on a particular site. In contrast to CO2, most studies that have estimated the time-integrated flux of CH4 have not used models. Typically, linear interpolation is used to estimate CH4 fluxes during the time periods between flux-chamber measurements. It is unclear how much error is involved with such a simple integration method. CH4 fluxes generally show a rise followed by a fall through the growing season that may be captured reasonably well by interpolation, provided there are sufficiently frequent measurements. However, day-to-day and week-to-week variability is also often evident in CH4 flux data, and will not necessarily be properly represented by interpolation. Our fits of the CH4 flux models yielded r2 > 0.5 in 38 of the 48 models constructed, with 55% of these having a weighted rw2 > 0.4. Comparison of annualised CH4 fluxes estimated by interpolation and modelling reveals no correlation between the two data sets; indeed, in some cases even the sign of the flux differs. The difference between the methods seems also to be related to the size of the flux - for modest annual fluxes there is a fairly even scatter of points around the 1:1 line, whereas when the modelled fluxes are high, the corresponding interpolated fluxes tend to be low. We consider the implications of these results for the calculation of the radiative forcing effect of peatlands.

Well-posedness and decay for the dissipative system modeling electro-hydrodynamics in negative Besov spaces

NASA Astrophysics Data System (ADS)

Zhao, Jihong; Liu, Qiao

2017-07-01

In Guo and Wang (2012) [10], Y. Guo and Y. Wang developed a general new energy method for proving the optimal time decay rates of the solutions to dissipative equations. In this paper, we generalize this method in the framework of homogeneous Besov spaces. Moreover, we apply this method to a model arising from electro-hydrodynamics, which is a strongly coupled system of the Navier-Stokes equations and the Poisson-Nernst-Planck equations through charge transport and external forcing terms. We show that some weighted negative Besov norms of solutions are preserved along time evolution, and obtain the optimal time decay rates of the higher-order spatial derivatives of solutions by the Fourier splitting approach and the interpolation techniques.

A robust interpolation procedure for producing tidal current ellipse inputs for regional and coastal ocean numerical models

NASA Astrophysics Data System (ADS)

Byun, Do-Seong; Hart, Deirdre E.

2017-04-01

Regional and/or coastal ocean models can use tidal current harmonic forcing, together with tidal harmonic forcing along open boundaries in order to successfully simulate tides and tidal currents. These inputs can be freely generated using online open-access data, but the data produced are not always at the resolution required for regional or coastal models. Subsequent interpolation procedures can produce tidal current forcing data errors for parts of the world's coastal ocean where tidal ellipse inclinations and phases move across the invisible mathematical "boundaries" between 359° and 0° degrees (or 179° and 0°). In nature, such "boundaries" are in fact smooth transitions, but if these mathematical "boundaries" are not treated correctly during interpolation, they can produce inaccurate input data and hamper the accurate simulation of tidal currents in regional and coastal ocean models. These avoidable errors arise due to procedural shortcomings involving vector embodiment problems (i.e., how a vector is represented mathematically, for example as velocities or as coordinates). Automated solutions for producing correct tidal ellipse parameter input data are possible if a series of steps are followed correctly, including the use of Cartesian coordinates during interpolation. This note comprises the first published description of scenarios where tidal ellipse parameter interpolation errors can arise, and of a procedure to successfully avoid these errors when generating tidal inputs for regional and/or coastal ocean numerical models. We explain how a straightforward sequence of data production, format conversion, interpolation, and format reconversion steps may be used to check for the potential occurrence and avoidance of tidal ellipse interpolation and phase errors. This sequence is demonstrated via a case study of the M2 tidal constituent in the seas around Korea but is designed to be universally applicable. We also recommend employing tidal ellipse parameter calculation methods that avoid the use of Foreman's (1978) "northern semi-major axis convention" since, as revealed in our analysis, this commonly used conversion can result in inclination interpolation errors even when Cartesian coordinate-based "vector embodiment" solutions are employed.

Geostatistical interpolation of individual average monthly temperature supported by MODIS MOD11C3 product

NASA Astrophysics Data System (ADS)

Perčec Tadić, M.

2010-09-01

The increased availability of satellite products of high spatial and temporal resolution together with developing user support, encourages the climatologists to use this data in research and practice. Since climatologists are mainly interested in monthly or even annual averages or aggregates, this high temporal resolution and hence, large amount of data, can be challenging for the less experienced users. Even if the attempt is made to aggregate e. g. the 15' (temporal) MODIS LST (land surface temperature) to daily temperature average, the development of the algorithm is not straight forward and should be done by the experts. Recent development of many temporary aggregated products on daily, several days or even monthly scale substantially decrease the amount of satellite data that needs to be processed and rise the possibility for development of various climatological applications. Here the attempt is presented in incorporating the MODIS satellite MOD11C3 product (Wan, 2009), that is monthly CMG (climate modelling 0.05 degree latitude/longitude grids) LST, as predictor in geostatistical interpolation of climatological data in Croatia. While in previous applications, e. g. in Climate Atlas of Croatia (Zaninović et al. 2008), the static predictors as digital elevation model, distance to the sea, latitude and longitude were used for the interpolation of monthly, seasonal and annual 30-years averages (reference climatology), here the monthly MOD11C3 is used to support the interpolation of the individual monthly average in the regression kriging framework. We believe that this can be a valuable show case of incorporating the remote sensed data for climatological application, especially in the areas that are under-sampled by conventional observations. Zaninović K, Gajić-Čapka M, Perčec Tadić M et al (2008) Klimatski atlas Hrvatske / Climate atlas of Croatia 1961-1990, 1971-2000. Meteorological and Hydrological Service of Croatia, Zagreb, pp 200. Wan Z, 2009: Collection-5 MODIS Land Surface Temperature Products Users' Guide, ICESS, University of California, Santa Barbara, pp 30.

INTERPOLATING VANCOUVER'S DAILY AMBIENT PM 10 FIELD

EPA Science Inventory

In this article we develop a spatial predictive distribution for the ambient space- time response field of daily ambient PM10 in Vancouver, Canada. Observed responses have a consistent temporal pattern from one monitoring site to the next. We exploit this feature of the field b...

Experiences with digital processing of images at INPE

NASA Technical Reports Server (NTRS)

Mascarenhas, N. D. A. (Principal Investigator)

1984-01-01

Four different research experiments with digital image processing at INPE will be described: (1) edge detection by hypothesis testing; (2) image interpolation by finite impulse response filters; (3) spatial feature extraction methods in multispectral classification; and (4) translational image registration by sequential tests of hypotheses.

Spatial interpolation of daily reference evapotranspiration in the Texas High Plains

USDA-ARS?s Scientific Manuscript database

The Texas High Plains Evapotranspiration (ET) Network collects meteorological data from 18 grass reference weather stations at hourly intervals and estimates hourly and daily reference ET using the American Society of Civil Engineers (ASCE) Standardized Reference ET equation. Producers in the Texas ...

Spatial interpolation of daily evapotranspiration data in the Texas High Plains

USDA-ARS?s Scientific Manuscript database

The Texas High Plains Evapotranspiration (ET) Network collects meteorological data from grass-referenced weather stations at hourly intervals and estimates hourly and daily reference ET using the American Society of Civil Engineers (ASCE) Standardized Reference ET equation. Producers in the Texas Hi...

Automated Approach to Very High-Order Aeroacoustic Computations. Revision

NASA Technical Reports Server (NTRS)

Dyson, Rodger W.; Goodrich, John W.

2001-01-01

Computational aeroacoustics requires efficient, high-resolution simulation tools. For smooth problems, this is best accomplished with very high-order in space and time methods on small stencils. However, the complexity of highly accurate numerical methods can inhibit their practical application, especially in irregular geometries. This complexity is reduced by using a special form of Hermite divided-difference spatial interpolation on Cartesian grids, and a Cauchy-Kowalewski recursion procedure for time advancement. In addition, a stencil constraint tree reduces the complexity of interpolating grid points that am located near wall boundaries. These procedures are used to develop automatically and to implement very high-order methods (> 15) for solving the linearized Euler equations that can achieve less than one grid point per wavelength resolution away from boundaries by including spatial derivatives of the primitive variables at each grid point. The accuracy of stable surface treatments is currently limited to 11th order for grid aligned boundaries and to 2nd order for irregular boundaries.

An Automated Approach to Very High Order Aeroacoustic Computations in Complex Geometries

NASA Technical Reports Server (NTRS)

Dyson, Rodger W.; Goodrich, John W.

2000-01-01

Computational aeroacoustics requires efficient, high-resolution simulation tools. And for smooth problems, this is best accomplished with very high order in space and time methods on small stencils. But the complexity of highly accurate numerical methods can inhibit their practical application, especially in irregular geometries. This complexity is reduced by using a special form of Hermite divided-difference spatial interpolation on Cartesian grids, and a Cauchy-Kowalewslci recursion procedure for time advancement. In addition, a stencil constraint tree reduces the complexity of interpolating grid points that are located near wall boundaries. These procedures are used to automatically develop and implement very high order methods (>15) for solving the linearized Euler equations that can achieve less than one grid point per wavelength resolution away from boundaries by including spatial derivatives of the primitive variables at each grid point. The accuracy of stable surface treatments is currently limited to 11th order for grid aligned boundaries and to 2nd order for irregular boundaries.

Space-time light field rendering.

PubMed

Wang, Huamin; Sun, Mingxuan; Yang, Ruigang

2007-01-01

In this paper, we propose a novel framework called space-time light field rendering, which allows continuous exploration of a dynamic scene in both space and time. Compared to existing light field capture/rendering systems, it offers the capability of using unsynchronized video inputs and the added freedom of controlling the visualization in the temporal domain, such as smooth slow motion and temporal integration. In order to synthesize novel views from any viewpoint at any time instant, we develop a two-stage rendering algorithm. We first interpolate in the temporal domain to generate globally synchronized images using a robust spatial-temporal image registration algorithm followed by edge-preserving image morphing. We then interpolate these software-synchronized images in the spatial domain to synthesize the final view. In addition, we introduce a very accurate and robust algorithm to estimate subframe temporal offsets among input video sequences. Experimental results from unsynchronized videos with or without time stamps show that our approach is capable of maintaining photorealistic quality from a variety of real scenes.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Employing a portable X-Ray fluorescence (P-XRF) analyser and GIS to identify and map heavy metal pollution in soils of a traditional bonfire site

NASA Astrophysics Data System (ADS)

Dao, Ligang; Zhang, Chaosheng; Morrison, Liam

2010-05-01

Soils in the vicinity of bonfires are recipients of metal contaminants from burning of metal-containing materials. In order to better understand the impacts of bonfires on soils, a total of 218 surface soil samples were collected from a traditional bonfire site in Galway City, Ireland. Concentrations of Cu, Pb and Zn were determined using a portable X-ray Fluorescence (P-XRF) analyser. Strong variations were observed for these metals, and several samples contained elevated Zn concentrations which exceeded the intervention threshold of the Dutch criteria (720 mg kg-1). Spatial clusters and spatial outliers were detected using the local Moran's I index and were mapped using GIS. Two clear high value spatial clusters could be observed on the upper left side and centre part of the study area for Cu, Pb and Zn. Results of variogram analyses showed high nugget-sill-ratios for Cu, Pb and Zn, indicating strong spatial variation over short distances which could be resulted from anthropogenic activities. The spatial interpolation method of ordinary kriging was applied to produce the spatial interpolation maps for Cu, Pb and Zn, and the areas with elevated concentrations were in line with historical locations of the bonfires. The hazard maps showed small parts of the study area with Zn concentrations exceeding the Dutch intervention values. In order to prevent further contamination from bonfires, it is advised that tyres and other metal-containing wastes should not be burnt. The results in this study provide useful information for management of bonfires.

Statistical comparison of various interpolation algorithms for reconstructing regional grid ionospheric maps over China

NASA Astrophysics Data System (ADS)

Li, Min; Yuan, Yunbin; Wang, Ningbo; Li, Zishen; Liu, Xifeng; Zhang, Xiao

2018-07-01

This paper presents a quantitative comparison of several widely used interpolation algorithms, i.e., Ordinary Kriging (OrK), Universal Kriging (UnK), planar fit and Inverse Distance Weighting (IDW), based on a grid-based single-shell ionosphere model over China. The experimental data were collected from the Crustal Movement Observation Network of China (CMONOC) and the International GNSS Service (IGS), covering the days of year 60-90 in 2015. The quality of these interpolation algorithms was assessed by cross-validation in terms of both the ionospheric correction performance and Single-Frequency (SF) Precise Point Positioning (PPP) accuracy on an epoch-by-epoch basis. The results indicate that the interpolation models perform better at mid-latitudes than low latitudes. For the China region, the performance of OrK and UnK is relatively better than the planar fit and IDW model for estimating ionospheric delay and positioning. In addition, the computational efficiencies of the IDW and planar fit models are better than those of OrK and UnK.

Higher-order triangular spectral element method with optimized cubature points for seismic wavefield modeling

NASA Astrophysics Data System (ADS)

Liu, Youshan; Teng, Jiwen; Xu, Tao; Badal, José

2017-05-01

The mass-lumped method avoids the cost of inverting the mass matrix and simultaneously maintains spatial accuracy by adopting additional interior integration points, known as cubature points. To date, such points are only known analytically in tensor domains, such as quadrilateral or hexahedral elements. Thus, the diagonal-mass-matrix spectral element method (SEM) in non-tensor domains always relies on numerically computed interpolation points or quadrature points. However, only the cubature points for degrees 1 to 6 are known, which is the reason that we have developed a p-norm-based optimization algorithm to obtain higher-order cubature points. In this way, we obtain and tabulate new cubature points with all positive integration weights for degrees 7 to 9. The dispersion analysis illustrates that the dispersion relation determined from the new optimized cubature points is comparable to that of the mass and stiffness matrices obtained by exact integration. Simultaneously, the Lebesgue constant for the new optimized cubature points indicates its surprisingly good interpolation properties. As a result, such points provide both good interpolation properties and integration accuracy. The Courant-Friedrichs-Lewy (CFL) numbers are tabulated for the conventional Fekete-based triangular spectral element (TSEM), the TSEM with exact integration, and the optimized cubature-based TSEM (OTSEM). A complementary study demonstrates the spectral convergence of the OTSEM. A numerical example conducted on a half-space model demonstrates that the OTSEM improves the accuracy by approximately one order of magnitude compared to the conventional Fekete-based TSEM. In particular, the accuracy of the 7th-order OTSEM is even higher than that of the 14th-order Fekete-based TSEM. Furthermore, the OTSEM produces a result that can compete in accuracy with the quadrilateral SEM (QSEM). The high accuracy of the OTSEM is also tested with a non-flat topography model. In terms of computational efficiency, the OTSEM is more efficient than the Fekete-based TSEM, although it is slightly costlier than the QSEM when a comparable numerical accuracy is required.

Assimilation of altimeter data into a quasigeostrophic ocean model using optimal interpolation and eofs

NASA Astrophysics Data System (ADS)

Rienecker, M. M.; Adamec, D.

1995-01-01

An ensemble of fraternal-twin experiments is used to assess the utility of optimal interpolation and model-based vertical empirical orthogonal functions (eofs) of streamfunction variability to assimilate satellite altimeter data into ocean models. Simulated altimeter data are assimilated into a basin-wide 3-layer quasi-geostrophic model with a horizontal grid spacing of 15 km. The effects of bottom topography are included and the model is forced by a wind stress curl distribution which is constant in time. The simulated data are extracted, along altimeter tracks with spatial and temporal characteristics of Geosat, from a reference model ocean with a slightly different climatology from that generated by the model used for assimilation. The use of vertical eofs determined from the model-generated streamfunction variability is shown to be effective in aiding the model's dynamical extrapolation of the surface information throughout the rest of the water column. After a single repeat cycle (17 days), the analysis errors are reduced markedly from the initial level, by 52% in the surface layer, 41% in the second layer and 11% in the bottom layer. The largest differences between the assimilation analysis and the reference ocean are found in the nonlinear regime of the mid-latitude jet in all layers. After 100 days of assimilation, the error in the upper two layers has been reduced by over 50% and that in the bottom layer by 38%. The essence of the method is that the eofs capture the statistics of the dynamical balances in the model and ensure that this balance is not inappropriately disturbed during the assimilation process. This statistical balance includes any potential vorticity homogeneity which may be associated with the eddy stirring by mid-latitude surface jets.

Integration of Geophysical Data into Structural Geological Modelling through Bayesian Networks

NASA Astrophysics Data System (ADS)

de la Varga, Miguel; Wellmann, Florian; Murdie, Ruth

2016-04-01

Structural geological models are widely used to represent the spatial distribution of relevant geological features. Several techniques exist to construct these models on the basis of different assumptions and different types of geological observations (e.g. Jessell et al., 2014). However, two problems are prevalent when constructing models: (i) observations and assumptions, and therefore also the constructed model, are subject to uncertainties, and (ii) additional information, such as geophysical data, is often available, but cannot be considered directly in the geological modelling step. In our work, we propose the integration of all available data into a Bayesian network including the generation of the implicit geological method by means of interpolation functions (Mallet, 1992; Lajaunie et al., 1997; Mallet, 2004; Carr et al., 2001; Hillier et al., 2014). As a result, we are able to increase the certainty of the resultant models as well as potentially learn features of our regional geology through data mining and information theory techniques. MCMC methods are used in order to optimize computational time and assure the validity of the results. Here, we apply the aforementioned concepts in a 3-D model of the Sandstone Greenstone Belt in the Archean Yilgarn Craton in Western Australia. The example given, defines the uncertainty in the thickness of greenstone as limited by Bouguer anomaly and the internal structure of the greenstone as limited by the magnetic signature of a banded iron formation. The incorporation of the additional data and specially the gravity provides an important reduction of the possible outcomes and therefore the overall uncertainty. References Carr, C. J., K. R. Beatson, B. J. Cherrie, J. T. Mitchell, R. W. Fright, C. B. McCallum, and R. T. Evans, 2001, Reconstruction and representation of 3D objects with radial basis functions: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, 67-76. Jessell, M., Aillères, L., de Kemp, E., Lindsay, M., Wellmann, F., Hillier, M., ... & Martin, R. (2014). Next Generation Three-Dimensional Geologic Modeling and Inversion. Lajaunie, C., G. Courrioux, and L. Manuel, 1997, Foliation fields and 3D cartography in geology: Principles of a method based on potential interpolation: Mathematical Geology, 29, 571-584. Mallet, J.-L., 1992, Discrete smooth interpolation in geometric modelling: Computer-Aided Design, 24, 178-191 Mallet, L. J., 2004, Space-time mathematical framework for sedimentary geology: Mathematical Geology, 36, 1-32.

The Spatial Structure of Planform Migration - Curvature Relation of Meandering Rivers

NASA Astrophysics Data System (ADS)

Guneralp, I.; Rhoads, B. L.

2005-12-01

Planform dynamics of meandering rivers have been of fundamental interest to fluvial geomorphologists and engineers because of the intriguing complexity of these dynamics, the role of planform change in floodplain development and landscape evolution, and the economic and social consequences of bank erosion and channel migration. Improved understanding of the complex spatial structure of planform change and capacity to predict these changes are important for effective stream management, engineering and restoration. The planform characteristics of a meandering river channel are integral to its planform dynamics. Active meandering rivers continually change their positions and shapes as a consequence of hydraulic forces exerted on the channel banks and bed, but as the banks and bed change through sediment transport, so do the hydraulic forces. Thus far, this complex feedback between form and process is incompletely understood, despite the fact that the characteristics and the dynamics of meandering rivers have been studied extensively. Current theoretical models aimed at predicting planform dynamics relate rates of meander migration to local and upstream planform curvature where weighting of the influence of curvature on migration rate decays exponentially over distance. This theoretical relation, however, has not been rigorously evaluated empirically. Furthermore, although models based on exponential-weighting of curvature effects yield fairly realistic predictions of meander migration, such models are incapable of reproducing complex forms of bend development, such as double heading or compound looping. This study presents the development of a new methodology based on parametric cubic spline interpolation for the characterization of channel planform and the planform curvature of meandering rivers. The use of continuous mathematical functions overcomes the reliance on bend-averaged values or piece-wise discrete approximations of planform curvature - a major limitation of previous studies. Continuous curvature series can be related to measured rates of lateral migration to explore empirically the relationship between spatially extended curvature and local bend migration. The methodology is applied to a study reach along a highly sinuous section of the Embarras River in Illinois, USA, which contains double-headed asymmetrical loops. To identify patterns of channel planform and rates of lateral migration for a study reach along Embarrass River in central Illinois, geographical information systems analysis of historical aerial photography over a period from 1936 to 1998 was conducted. Results indicate that parametric cubic spline interpolation provides excellent characterization of the complex planforms and planform curvatures of meandering rivers. The findings also indicate that the spatial structure of migration rate-curvature relation may be more complex than a simple exponential distance-decay function. The study represents a first step toward unraveling the spatial structure of planform evolution of meandering rivers and for developing models of planform dynamics that accurately relate spatially extended patterns of channel curvature to local rates of lateral migration. Such knowledge is vital for improving the capacity to accurately predict planform change of meandering rivers.

Ground Magnetic Data for West-Central Colorado

DOE Data Explorer

Richard Zehner

2012-03-08

Modeled ground magnetic data was extracted from the Pan American Center for Earth and Environmental Studies database at http://irpsrvgis08.utep.edu/viewers/Flex/GravityMagnetic/GravityMagnetic_CyberShare/ on 2/29/2012. The downloaded text file was then imported into an Excel spreadsheet. This spreadsheet data was converted into an ESRI point shapefile in UTM Zone 13 NAD27 projection, showing location and magnetic field strength in nano-Teslas. This point shapefile was then interpolated to an ESRI grid using an inverse-distance weighting method, using ESRI Spatial Analyst. The grid was used to create a contour map of magnetic field strength.

Image interpolation via regularized local linear regression.

PubMed

Liu, Xianming; Zhao, Debin; Xiong, Ruiqin; Ma, Siwei; Gao, Wen; Sun, Huifang

2011-12-01

The linear regression model is a very attractive tool to design effective image interpolation schemes. Some regression-based image interpolation algorithms have been proposed in the literature, in which the objective functions are optimized by ordinary least squares (OLS). However, it is shown that interpolation with OLS may have some undesirable properties from a robustness point of view: even small amounts of outliers can dramatically affect the estimates. To address these issues, in this paper we propose a novel image interpolation algorithm based on regularized local linear regression (RLLR). Starting with the linear regression model where we replace the OLS error norm with the moving least squares (MLS) error norm leads to a robust estimator of local image structure. To keep the solution stable and avoid overfitting, we incorporate the l(2)-norm as the estimator complexity penalty. Moreover, motivated by recent progress on manifold-based semi-supervised learning, we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points. Specifically, our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art interpolation algorithms, especially in image edge structure preservation. © 2011 IEEE

Research and implementation on 3D modeling of geological body

NASA Astrophysics Data System (ADS)

Niu, Lijuan; Li, Ligong; Zhu, Renyi; Huang, Man

2017-10-01

This study based on GIS thinking explores the combination of the mixed spatial data model and GIS model to build three-dimensional(3d) model of geological bodies in the Arc Engine platform, describes the interface and method used in the construction of 3d geological body in Arc Engine component platform in detail, and puts forward an indirect method which constructs a set of geological grid layers through Rigging interpolation by the borehole data and then converts it into the geological layers of TIN, which improves the defect in building the geological layers of TIN directly and makes it better to complete the simulation of the real geological layer. This study makes a useful attempt to build 3d model of the geological body based on the GIS, and provides a certain reference value for simulating geological bodies in 3d and constructing the digital system of underground space.

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

USGS Publications Warehouse

Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J. Andrew; Daly, Christopher; Gibson, W.P.; Fisher, H.H.; Thornton, P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; Neilson, R.; Lenihan, J.; Drapek, R.; Ojima, D.S.; Parton, W.J.; Melillo, J.M.; Kicklighter, D.W.; Tian, H.; McGuire, A.D.; Sykes, M.T.; Smith, B.; Cowling, S.; Hickler, T.; Prentice, I.C.; Running, S.; Hibbard, K.A.; Post, W.M.; King, A.W.; Smith, T.; Rizzo, B.; Woodward, F.I.

2004-01-01

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5?? latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. ?? Inter-Research 2004.

Characterization of a 2-mm thick, 16x16 Cadmium-Zinc-Telluride Pixel Array

NASA Technical Reports Server (NTRS)

Gaskin, Jessica; Richardson, Georgia; Mitchell, Shannon; Ramsey, Brian; Seller, Paul; Sharma, Dharma

2003-01-01

The detector under study is a 2-mm-thick, 16x16 Cadmium-Zinc-Telluride pixel array with a pixel pitch of 300 microns and inter-pixel gap of 50 microns. This detector is a precursor to that which will be used at the focal plane of the High Energy Replicated Optics (HERO) telescope currently being developed at Marshall Space Flight Center. With a telescope focal length of 6 meters, the detector needs to have a spatial resolution of around 200 microns in order to take full advantage of the HERO angular resolution. We discuss to what degree charge sharing will degrade energy resolution but will improve our spatial resolution through position interpolation. In addition, we discuss electric field modeling for this specific detector geometry and the role this mapping will play in terms of charge sharing and charge loss in the detector.

Use of shape-preserving interpolation methods in surface modeling

NASA Technical Reports Server (NTRS)

Ftitsch, F. N.

1984-01-01

In many large-scale scientific computations, it is necessary to use surface models based on information provided at only a finite number of points (rather than determined everywhere via an analytic formula). As an example, an equation of state (EOS) table may provide values of pressure as a function of temperature and density for a particular material. These values, while known quite accurately, are typically known only on a rectangular (but generally quite nonuniform) mesh in (T,d)-space. Thus interpolation methods are necessary to completely determine the EOS surface. The most primitive EOS interpolation scheme is bilinear interpolation. This has the advantages of depending only on local information, so that changes in data remote from a mesh element have no effect on the surface over the element, and of preserving shape information, such as monotonicity. Most scientific calculations, however, require greater smoothness. Standard higher-order interpolation schemes, such as Coons patches or bicubic splines, while providing the requisite smoothness, tend to produce surfaces that are not physically reasonable. This means that the interpolant may have bumps or wiggles that are not supported by the data. The mathematical quantification of ideas such as physically reasonable and visually pleasing is examined.

A Development of Nonstationary Regional Frequency Analysis Model with Large-scale Climate Information: Its Application to Korean Watershed

NASA Astrophysics Data System (ADS)

Kim, Jin-Young; Kwon, Hyun-Han; Kim, Hung-Soo

2015-04-01

The existing regional frequency analysis has disadvantages in that it is difficult to consider geographical characteristics in estimating areal rainfall. In this regard, this study aims to develop a hierarchical Bayesian model based nonstationary regional frequency analysis in that spatial patterns of the design rainfall with geographical information (e.g. latitude, longitude and altitude) are explicitly incorporated. This study assumes that the parameters of Gumbel (or GEV distribution) are a function of geographical characteristics within a general linear regression framework. Posterior distribution of the regression parameters are estimated by Bayesian Markov Chain Monte Carlo (MCMC) method, and the identified functional relationship is used to spatially interpolate the parameters of the distributions by using digital elevation models (DEM) as inputs. The proposed model is applied to derive design rainfalls over the entire Han-river watershed. It was found that the proposed Bayesian regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis. In addition, the model showed an advantage in terms of quantifying uncertainty of the design rainfall and estimating the area rainfall considering geographical information. Finally, comprehensive discussion on design rainfall in the context of nonstationary will be presented. KEYWORDS: Regional frequency analysis, Nonstationary, Spatial information, Bayesian Acknowledgement This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Translating hydrologically-relevant variables from the ice sheet model SICOPOLIS to the Greenland Analog Project hydrologic modeling domain

NASA Astrophysics Data System (ADS)

Vallot, Dorothée; Applegate, Patrick; Pettersson, Rickard

2013-04-01

Projecting future climate and ice sheet development requires sophisticated models and extensive field observations. Given the present state of our knowledge, it is very difficult to say what will happen with certainty. Despite the ongoing increase in atmospheric greenhouse gas concentrations, the possibility that a new ice sheet might form over Scandinavia in the far distant future cannot be excluded. The growth of a new Scandinavian Ice Sheet would have important consequences for buried nuclear waste repositories. The Greenland Analogue Project, initiated by the Swedish Nuclear Fuel and Waste Management Company (SKB), is working to assess the effects of a possible future ice sheet on groundwater flow by studying a constrained domain in Western Greenland by field measurements (including deep bedrock drilling in front of the ice sheet) combined with numerical modeling. To address the needs of the GAP project, we interpolated results from an ensemble of ice sheet model runs to the smaller and more finely resolved modeling domain used in the GAP project's hydrologic modeling. Three runs have been chosen with three fairly different positive degree-day factors among those that reproduced the modern ice margin at the borehole position. The interpolated results describe changes in hydrologically-relevant variables over two time periods, 115 ka to 80 ka, and 20 ka to 1 ka. In the first of these time periods, the ice margin advances over the model domain; in the second time period, the ice margin retreats over the model domain. The spatially-and temporally dependent variables that we treated include the ice thickness, basal melting rate, surface mass balance, basal temperature, basal thermal regime (frozen or thawed), surface temperature, and basal water pressure. The melt flux is also calculated.

Evaluation of GFDL-AM4 simulations of nitrogen oxides with OMI satellite observations

NASA Astrophysics Data System (ADS)

Penn, E.; Horowitz, L. W.; Naik, V.

2017-12-01

We examine the seasonal cycle and interannual variability of NO2 from 2005-2015 of NO2 over key global regions using simulations with a nudged version of the GFDL-AM4 chemistry-climate model and satellite-based observations from OMI (Ozone Monitoring Instrument), which observes near-global NO2 column abundances at 1pm local time daily. We gridded TEMIS (Tropospheric Emissions Monitoring Internet Service) OMI data to the model spatial grid using WHIPS 2.0 (Wisconsin Horizontal Interpolation Program for Satellites version 2.0) and applied the OMI averaging kernel to weight the model's NO2 concentrations vertically. Model-simulated tropospheric NO2 columns reproduce well the OMI spatial patterns (averaging r2=0.81) and seasonal cycles, but underestimate observations in most regions by 16-62%. A notable exception is the overestimate by 5-35% in East Asia. In regions dominated by biomass burning, these emissions tend to control the seasonal cycle of NO2. However, where anthropogenic emissions dominate, the photochemical conversion of NO2 to PAN and nitric acid controls the seasonal cycle, as indicated by NO2/NOy ratios. Future work is required to explain AM4 biases relative to OMI.

Spatial Scale Variability of NH3 and Impacts to interpolated Concentration Grids

EPA Science Inventory

Over the past decade, reduced nitrogen (NH3, NH4) has become an important component of atmospheric nitrogen deposition due to increases in agricultural activities and reductions in oxidized sulfur and nitrogen emissions from the power sector and mobile sources. Reduced nitrogen i...

An Immersed Boundary method with divergence-free velocity interpolation and force spreading

NASA Astrophysics Data System (ADS)

Bao, Yuanxun; Donev, Aleksandar; Griffith, Boyce E.; McQueen, David M.; Peskin, Charles S.

2017-10-01

The Immersed Boundary (IB) method is a mathematical framework for constructing robust numerical methods to study fluid-structure interaction in problems involving an elastic structure immersed in a viscous fluid. The IB formulation uses an Eulerian representation of the fluid and a Lagrangian representation of the structure. The Lagrangian and Eulerian frames are coupled by integral transforms with delta function kernels. The discretized IB equations use approximations to these transforms with regularized delta function kernels to interpolate the fluid velocity to the structure, and to spread structural forces to the fluid. It is well-known that the conventional IB method can suffer from poor volume conservation since the interpolated Lagrangian velocity field is not generally divergence-free, and so this can cause spurious volume changes. In practice, the lack of volume conservation is especially pronounced for cases where there are large pressure differences across thin structural boundaries. The aim of this paper is to greatly reduce the volume error of the IB method by introducing velocity-interpolation and force-spreading schemes with the properties that the interpolated velocity field in which the structure moves is at least C1 and satisfies a continuous divergence-free condition, and that the force-spreading operator is the adjoint of the velocity-interpolation operator. We confirm through numerical experiments in two and three spatial dimensions that this new IB method is able to achieve substantial improvement in volume conservation compared to other existing IB methods, at the expense of a modest increase in the computational cost. Further, the new method provides smoother Lagrangian forces (tractions) than traditional IB methods. The method presented here is restricted to periodic computational domains. Its generalization to non-periodic domains is important future work.

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; Mount, David M.

2007-01-01

Interpolating scattered data points is a problem of wide ranging interest. A number of approaches for interpolation have been proposed both from theoretical domains such as computational geometry and in applications' fields such as geostatistics. Our motivation arises from geological and mining applications. In many instances data can be costly to compute and are available only at nonuniformly scattered positions. Because of the high cost of collecting measurements, high accuracy is required in the interpolants. One of the most popular interpolation methods in this field is called ordinary kriging. It is popular because it is a best linear unbiased estimator. The price for its statistical optimality is that the estimator is computationally very expensive. This is because the value of each interpolant is given by the solution of a large dense linear system. In practice, kriging problems have been solved approximately by restricting the domain to a small local neighborhood of points that lie near the query point. Determining the proper size for this neighborhood is a solved by ad hoc methods, and it has been shown that this approach leads to undesirable discontinuities in the interpolant. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. This process achieves its efficiency by replacing the large dense kriging system with a much sparser linear system. This technique has been applied to a restriction of our problem, called simple kriging, which is not unbiased for general data sets. In this paper we generalize these results by showing how to apply covariance tapering to the more general problem of ordinary kriging. Through experimentation we demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods for solving large sparse systems. We demonstrate our approach on large data sizes arising both from synthetic sources and from real applications.

A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2017-07-01

Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling.

PubMed

Chang, Howard H; Hu, Xuefei; Liu, Yang

2014-07-01

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.

Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application.

PubMed

Li, Lixin; Zhou, Xiaolu; Kalo, Marc; Piltner, Reinhard

2016-07-25

Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)'s AirNow program.

Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application

PubMed Central

Li, Lixin; Zhou, Xiaolu; Kalo, Marc; Piltner, Reinhard

2016-01-01

Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)’s AirNow program. PMID:27463722

Wavelet-based adaptation methodology combined with finite difference WENO to solve ideal magnetohydrodynamics

NASA Astrophysics Data System (ADS)

Do, Seongju; Li, Haojun; Kang, Myungjoo

2017-06-01

In this paper, we present an accurate and efficient wavelet-based adaptive weighted essentially non-oscillatory (WENO) scheme for hydrodynamics and ideal magnetohydrodynamics (MHD) equations arising from the hyperbolic conservation systems. The proposed method works with the finite difference weighted essentially non-oscillatory (FD-WENO) method in space and the third order total variation diminishing (TVD) Runge-Kutta (RK) method in time. The philosophy of this work is to use the lifted interpolating wavelets as not only detector for singularities but also interpolator. Especially, flexible interpolations can be performed by an inverse wavelet transformation. When the divergence cleaning method introducing auxiliary scalar field ψ is applied to the base numerical schemes for imposing divergence-free condition to the magnetic field in a MHD equation, the approximations to derivatives of ψ require the neighboring points. Moreover, the fifth order WENO interpolation requires large stencil to reconstruct high order polynomial. In such cases, an efficient interpolation method is necessary. The adaptive spatial differentiation method is considered as well as the adaptation of grid resolutions. In order to avoid the heavy computation of FD-WENO, in the smooth regions fixed stencil approximation without computing the non-linear WENO weights is used, and the characteristic decomposition method is replaced by a component-wise approach. Numerical results demonstrate that with the adaptive method we are able to resolve the solutions that agree well with the solution of the corresponding fine grid.

Effect of climate data on simulated carbon and nitrogen balances for Europe

NASA Astrophysics Data System (ADS)

Blanke, Jan Hendrik; Lindeskog, Mats; Lindström, Johan; Lehsten, Veiko

2016-05-01

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%-93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%-10.7% of the total variability followed by GCMs (0.3%-7.6%), RCPs (0%-6.3%), and downscaling methods (0.1%-4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%-45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.

Rtop - an R package for interpolation along the stream network

NASA Astrophysics Data System (ADS)

Skøien, J. O.; Laaha, G.; Koffler, D.; Blöschl, G.; Pebesma, E.; Parajka, J.; Viglione, A.

2012-04-01

Geostatistical methods have a long tradition within analysis of data that can be conceptualized as simple point data, such as soil properties, or for regular blocks, such as mining data. However, these methods have been used to a limited extent for estimation along stream networks. A few exceptions are given by (Gottschalk 1993, Sauquet et al. 2000, Gottschalk et al. 2006, Skøien et al. 2006), and an overview by Laaha and Blöschl (2011). Interpolation of runoff characteristics are more complicated than the traditional random variables estimated by geostatistical methods, as the measurements have a more complicated support, and many catchments are nested. Skøien et al. (2006) presented the model Top-kriging which takes these effects into account for interpolation of stream flow characteristics (exemplified by the 100 year flood). The method has here been implemented as a package in the open source statistical environment R (R Development Core Team 2011). Taking advantage of the existing methods in R for working with spatial objects, and the extensive possibilities for visualizing the result, this makes it considerably easier to apply the method on new data sets, in comparison to earlier implementation of the method. In addition to user feedback, the package has also been tested by colleagues whose only responsibility has been to search for bugs, inconsistencies and shortcomings of the documentation. The last part is often the part that gets the least attention in small open source projects, and we have solved this by acknowledging their effects as co-authors. The model will soon be uploaded to CRAN, but is in the meantime also available from R-forge and can be installed by: > install.packages("rtop", repos="http://R-Forge.R-project.org") Gottschalk, L., 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., Krasovskaia, I., Leblois, E. & Sauquet, E., 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. Laaha, G. & Blöschl, G. 2011. Geostatistics on river networks - a reviewed. EGU General Assembly, Vienna, Austria. R Development Core Team, 2011. R: A language and environment for statistical computing. Vienna, Austria, ISBN 3-900051-07-0. Sauquet, E., Gottschalk, L. & Leblois, E., 2000. Mapping average annual runoff: A hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J.O., Merz, R. & Blöschl, G., 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

Analysis of Mining Terrain Deformation Characteristics with Deformation Information System

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Milczarek, Wojciech; Grzempowski, Piotr

2014-05-01

Mapping and prediction of mining related deformations of the earth surface is an important measure for minimising threat to surface infrastructure, human population, the environment and safety of the mining operation itself arising from underground extraction of useful minerals. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and increasing with the development of geographical information technologies. These include for example: terrestrial geodetic measurements, global positioning systems, remote sensing, spatial interpolation, finite element method modelling, GIS based modelling, geological modelling, empirical modelling using the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The aim of this paper is to introduce the concept of an integrated Deformation Information System (DIS) developed in geographic information systems environment for analysis and modelling of various spatial data related to mining activity and demonstrate its applications for mapping and visualising, as well as identifying possible mining terrain deformation areas with various spatial modelling methods. The DIS concept is based on connected modules that include: the spatial database - the core of the system, the spatial data collection module formed by: terrestrial, satellite and remote sensing measurements of the ground changes, the spatial data mining module for data discovery and extraction, the geological modelling module, the spatial data modeling module with data processing algorithms for spatio-temporal analysis and mapping of mining deformations and their characteristics (e.g. deformation parameters: tilt, curvature and horizontal strain), the multivariate spatial data classification module and the visualization module allowing two-dimensional interactive and static mapping and three-dimensional visualizations of mining ground characteristics. The Systems's functionality has been presented on the case study of a coal mining region in SW Poland where it has been applied to study characteristics and map mining induced ground deformations in a city in the last two decades of underground coal extraction and in the first decade after the end of mining. The mining subsidence area and its deformation parameters (tilt and curvature) have been calculated and the latter classified and mapped according to the Polish regulations. In addition possible areas of ground deformation have been indicated based on multivariate spatial data analysis of geological and mining operation characteristics with the geographically weighted regression method.

Constructing polyatomic potential energy surfaces by interpolating diabatic Hamiltonian matrices with demonstration on green fluorescent protein chromophore

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, Jae Woo; Rhee, Young Min, E-mail: ymrhee@postech.ac.kr; Department of Chemistry, Pohang University of Science and Technology

2014-04-28

Simulating molecular dynamics directly on quantum chemically obtained potential energy surfaces is generally time consuming. The cost becomes overwhelming especially when excited state dynamics is aimed with multiple electronic states. The interpolated potential has been suggested as a remedy for the cost issue in various simulation settings ranging from fast gas phase reactions of small molecules to relatively slow condensed phase dynamics with complex surrounding. Here, we present a scheme for interpolating multiple electronic surfaces of a relatively large molecule, with an intention of applying it to studying nonadiabatic behaviors. The scheme starts with adiabatic potential information and its diabaticmore » transformation, both of which can be readily obtained, in principle, with quantum chemical calculations. The adiabatic energies and their derivatives on each interpolation center are combined with the derivative coupling vectors to generate the corresponding diabatic Hamiltonian and its derivatives, and they are subsequently adopted in producing a globally defined diabatic Hamiltonian function. As a demonstration, we employ the scheme to build an interpolated Hamiltonian of a relatively large chromophore, para-hydroxybenzylidene imidazolinone, in reference to its all-atom analytical surface model. We show that the interpolation is indeed reliable enough to reproduce important features of the reference surface model, such as its adiabatic energies and derivative couplings. In addition, nonadiabatic surface hopping simulations with interpolation yield population transfer dynamics that is well in accord with the result generated with the reference analytic surface. With these, we conclude by suggesting that the interpolation of diabatic Hamiltonians will be applicable for studying nonadiabatic behaviors of sizeable molecules.« less

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Temporal and Spatial prediction of groundwater levels using Artificial Neural Networks, Fuzzy logic and Kriging interpolation.

NASA Astrophysics Data System (ADS)

Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

2014-05-01

The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of the ANN. Therefore, the neighborhood of each prediction point is the best available. Then, the appropriate variogram is determined, by fitting the experimental variogram to a theoretical variogram model. Three models are examined, the linear, the exponential and the power-law. Finally, the hydraulic head change is predicted for every grid cell and for every time step used. All the algorithms used were developed in Visual Basic .NET, while the visualization of the results was performed in MATLAB using the .NET COM Interoperability. The results are evaluated using leave one out cross-validation and various performance indicators. The best results were achieved by using ANNs with two hidden layers, consisting of 20 and 15 nodes respectively and by using power-law variogram with the fuzzy logic system.

Interpolating seismic data via the POCS method based on shearlet transform

NASA Astrophysics Data System (ADS)

Jicheng, Liu; Yongxin, Chou; Jianjiang, Zhu

2018-06-01

A method based on shearlet transform and the projection onto convex sets with L0-norm constraint is proposed to interpolate irregularly sampled 2D and 3D seismic data. The 2D directional filter of shearlet transform is constructed by modulating a low-pass diamond filter pair to minimize the effect of additional edges introduced by the missing traces. In order to abate the spatial aliasing and control the maximal gap between missing traces for a 3D data cube, a 2D separable jittered sampling strategy is discussed. Finally, numerical experiments on 2D and 3D synthetic and real data with different under-sampling rates prove the validity of the proposed method.

An empirical model of diagnostic x-ray attenuation under narrow-beam geometry.

PubMed

Mathieu, Kelsey B; Kappadath, S Cheenu; White, R Allen; Atkinson, E Neely; Cody, Dianna D

2011-08-01

The purpose of this study was to develop and validate a mathematical model to describe narrow-beam attenuation of kilovoltage x-ray beams for the intended applications of half-value layer (HVL) and quarter-value layer (QVL) estimations, patient organ shielding, and computer modeling. An empirical model, which uses the Lambert W function and represents a generalized Lambert-Beer law, was developed. To validate this model, transmission of diagnostic energy x-ray beams was measured over a wide range of attenuator thicknesses [0.49-33.03 mm Al on a computed tomography (CT) scanner, 0.09-1.93 mm Al on two mammography systems, and 0.1-0.45 mm Cu and 0.49-14.87 mm Al using general radiography]. Exposure measurements were acquired under narrow-beam geometry using standard methods, including the appropriate ionization chamber, for each radiographic system. Nonlinear regression was used to find the best-fit curve of the proposed Lambert W model to each measured transmission versus attenuator thickness data set. In addition to validating the Lambert W model, we also assessed the performance of two-point Lambert W interpolation compared to traditional methods for estimating the HVL and QVL [i.e., semi-logarithmic (exponential) and linear interpolation]. The Lambert W model was validated for modeling attenuation versus attenuator thickness with respect to the data collected in this study (R2 > 0.99). Furthermore, Lambert W interpolation was more accurate and less sensitive to the choice of interpolation points used to estimate the HVL and/or QVL than the traditional methods of semilogarithmic and linear interpolation. The proposed Lambert W model accurately describes attenuation of both monoenergetic radiation and (kilovoltage) polyenergetic beams (under narrow-beam geometry).

An empirical model of diagnostic x-ray attenuation under narrow-beam geometry

PubMed Central

Mathieu, Kelsey B.; Kappadath, S. Cheenu; White, R. Allen; Atkinson, E. Neely; Cody, Dianna D.

2011-01-01

Purpose: The purpose of this study was to develop and validate a mathematical model to describe narrow-beam attenuation of kilovoltage x-ray beams for the intended applications of half-value layer (HVL) and quarter-value layer (QVL) estimations, patient organ shielding, and computer modeling. Methods: An empirical model, which uses the Lambert W function and represents a generalized Lambert-Beer law, was developed. To validate this model, transmission of diagnostic energy x-ray beams was measured over a wide range of attenuator thicknesses [0.49–33.03 mm Al on a computed tomography (CT) scanner, 0.09–1.93 mm Al on two mammography systems, and 0.1–0.45 mm Cu and 0.49–14.87 mm Al using general radiography]. Exposure measurements were acquired under narrow-beam geometry using standard methods, including the appropriate ionization chamber, for each radiographic system. Nonlinear regression was used to find the best-fit curve of the proposed Lambert W model to each measured transmission versus attenuator thickness data set. In addition to validating the Lambert W model, we also assessed the performance of two-point Lambert W interpolation compared to traditional methods for estimating the HVL and QVL [i.e., semilogarithmic (exponential) and linear interpolation]. Results: The Lambert W model was validated for modeling attenuation versus attenuator thickness with respect to the data collected in this study (R2 > 0.99). Furthermore, Lambert W interpolation was more accurate and less sensitive to the choice of interpolation points used to estimate the HVL and∕or QVL than the traditional methods of semilogarithmic and linear interpolation. Conclusions: The proposed Lambert W model accurately describes attenuation of both monoenergetic radiation and (kilovoltage) polyenergetic beams (under narrow-beam geometry). PMID:21928626

An empirical model of diagnostic x-ray attenuation under narrow-beam geometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mathieu, Kelsey B.; Kappadath, S. Cheenu; White, R. Allen

2011-08-15

Purpose: The purpose of this study was to develop and validate a mathematical model to describe narrow-beam attenuation of kilovoltage x-ray beams for the intended applications of half-value layer (HVL) and quarter-value layer (QVL) estimations, patient organ shielding, and computer modeling. Methods: An empirical model, which uses the Lambert W function and represents a generalized Lambert-Beer law, was developed. To validate this model, transmission of diagnostic energy x-ray beams was measured over a wide range of attenuator thicknesses [0.49-33.03 mm Al on a computed tomography (CT) scanner, 0.09-1.93 mm Al on two mammography systems, and 0.1-0.45 mm Cu and 0.49-14.87more » mm Al using general radiography]. Exposure measurements were acquired under narrow-beam geometry using standard methods, including the appropriate ionization chamber, for each radiographic system. Nonlinear regression was used to find the best-fit curve of the proposed Lambert W model to each measured transmission versus attenuator thickness data set. In addition to validating the Lambert W model, we also assessed the performance of two-point Lambert W interpolation compared to traditional methods for estimating the HVL and QVL [i.e., semilogarithmic (exponential) and linear interpolation]. Results: The Lambert W model was validated for modeling attenuation versus attenuator thickness with respect to the data collected in this study (R{sup 2} > 0.99). Furthermore, Lambert W interpolation was more accurate and less sensitive to the choice of interpolation points used to estimate the HVL and/or QVL than the traditional methods of semilogarithmic and linear interpolation. Conclusions: The proposed Lambert W model accurately describes attenuation of both monoenergetic radiation and (kilovoltage) polyenergetic beams (under narrow-beam geometry).« less

High-resolution daily gridded datasets of air temperature and wind speed for Europe

NASA Astrophysics Data System (ADS)

Brinckmann, S.; Krähenmann, S.; Bissolli, P.

2015-08-01

New high-resolution datasets for near surface daily air temperature (minimum, maximum and mean) and daily mean wind speed for Europe (the CORDEX domain) are provided for the period 2001-2010 for the purpose of regional model validation in the framework of DecReg, a sub-project of the German MiKlip project, which aims to develop decadal climate predictions. The main input data sources are hourly SYNOP observations, partly supplemented by station data from the ECA&D dataset (http://www.ecad.eu). These data are quality tested to eliminate erroneous data and various kinds of inhomogeneities. Grids in a resolution of 0.044° (5 km) are derived by spatial interpolation of these station data into the CORDEX area. For temperature interpolation a modified version of a regression kriging method developed by Krähenmann et al. (2011) is used. At first, predictor fields of altitude, continentality and zonal mean temperature are chosen for a regression applied to monthly station data. The residuals of the monthly regression and the deviations of the daily data from the monthly averages are interpolated using simple kriging in a second and third step. For wind speed a new method based on the concept used for temperature was developed, involving predictor fields of exposure, roughness length, coastal distance and ERA Interim reanalysis wind speed at 850 hPa. Interpolation uncertainty is estimated by means of the kriging variance and regression uncertainties. Furthermore, to assess the quality of the final daily grid data, cross validation is performed. Explained variance ranges from 70 to 90 % for monthly temperature and from 50 to 60 % for monthly wind speed. The resulting RMSE for the final daily grid data amounts to 1-2 °C and 1-1.5 m s-1 (depending on season and parameter) for daily temperature parameters and daily mean wind speed, respectively. The datasets presented in this article are published at http://dx.doi.org/10.5676/DWD_CDC/DECREG0110v1.

Shape and Albedo from Shading (SAfS) for Pixel-Level dem Generation from Monocular Images Constrained by Low-Resolution dem

NASA Astrophysics Data System (ADS)

Wu, Bo; Chung Liu, Wai; Grumpe, Arne; Wöhler, Christian

2016-06-01

Lunar topographic information, e.g., lunar DEM (Digital Elevation Model), is very important for lunar exploration missions and scientific research. Lunar DEMs are typically generated from photogrammetric image processing or laser altimetry, of which photogrammetric methods require multiple stereo images of an area. DEMs generated from these methods are usually achieved by various interpolation techniques, leading to interpolation artifacts in the resulting DEM. On the other hand, photometric shape reconstruction, e.g., SfS (Shape from Shading), extensively studied in the field of Computer Vision has been introduced to pixel-level resolution DEM refinement. SfS methods have the ability to reconstruct pixel-wise terrain details that explain a given image of the terrain. If the terrain and its corresponding pixel-wise albedo were to be estimated simultaneously, this is a SAfS (Shape and Albedo from Shading) problem and it will be under-determined without additional information. Previous works show strong statistical regularities in albedo of natural objects, and this is even more logically valid in the case of lunar surface due to its lower surface albedo complexity than the Earth. In this paper we suggest a method that refines a lower-resolution DEM to pixel-level resolution given a monocular image of the coverage with known light source, at the same time we also estimate the corresponding pixel-wise albedo map. We regulate the behaviour of albedo and shape such that the optimized terrain and albedo are the likely solutions that explain the corresponding image. The parameters in the approach are optimized through a kernel-based relaxation framework to gain computational advantages. In this research we experimentally employ the Lunar-Lambertian model for reflectance modelling; the framework of the algorithm is expected to be independent of a specific reflectance model. Experiments are carried out using the monocular images from Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) (0.5 m spatial resolution), constrained by the SELENE and LRO Elevation Model (SLDEM 2015) of 60 m spatial resolution. The results indicate that local details are largely recovered by the algorithm while low frequency topographic consistency is affected by the low-resolution DEM.

Scaling Properties and Spatial Interpolation of Soil Moisture

DTIC Science & Technology

2004-08-24

the sensitivities is useful not only for characterizing soil moisture but also for forecasting the vulnerability of a region’s water cycle to climate...regional water balance was presented that can be used to assess the impact of climatic fluctuations and changes on the water cycle of a region. In

A JACKNIFE APPROACH TO EXAMINE UNCERTAINTY AND TEMPORAL CHANGES IN THE SPATIL CORRELATION OF A VOC PLUME

EPA Science Inventory

ABSTRACT: The application of geostatistics to spatial interpolation of time-invariant properties in ground-water studies (such as transmissivity or aquifer thickness) is well documented. The use of geostatistics on time-variant conditions such as ground-water quality is also be...

Finite-difference modeling with variable grid-size and adaptive time-step in porous media

NASA Astrophysics Data System (ADS)

Liu, Xinxin; Yin, Xingyao; Wu, Guochen

2014-04-01

Forward modeling of elastic wave propagation in porous media has great importance for understanding and interpreting the influences of rock properties on characteristics of seismic wavefield. However, the finite-difference forward-modeling method is usually implemented with global spatial grid-size and time-step; it consumes large amounts of computational cost when small-scaled oil/gas-bearing structures or large velocity-contrast exist underground. To overcome this handicap, combined with variable grid-size and time-step, this paper developed a staggered-grid finite-difference scheme for elastic wave modeling in porous media. Variable finite-difference coefficients and wavefield interpolation were used to realize the transition of wave propagation between regions of different grid-size. The accuracy and efficiency of the algorithm were shown by numerical examples. The proposed method is advanced with low computational cost in elastic wave simulation for heterogeneous oil/gas reservoirs.

Optics ellipticity performance of an unobscured off-axis space telescope.

PubMed

Zeng, Fei; Zhang, Xin; Zhang, Jianping; Shi, Guangwei; Wu, Hongbo

2014-10-20

With the development of astronomy, more and more attention is paid to the survey of dark matter. Dark matter cannot be seen directly but can be detected by weak gravitational lensing measurement. Ellipticity is an important parameter used to define the shape of a galaxy. Galaxy ellipticity changes with weak gravitational lensing and nonideal optics. With our design of an unobscured off-axis telescope, we implement the simulation and calculation of optics ellipticity. With an accurate model of optics PSF, the characteristic of ellipticity is modeled and analyzed. It is shown that with good optical design, the full field ellipticity can be quite small. The spatial ellipticity change can be modeled by cubic interpolation with very high accuracy. We also modeled the ellipticity variance with time and analyzed the tolerance. It is shown that the unobscured off-axis telescope has good ellipticity performance and fulfills the requirement of dark matter survey.

Temporal and spatial characterization of zenith total delay (ZTD) in North Europe

NASA Astrophysics Data System (ADS)

Stoew, B.; Elgered, G.

2003-04-01

The estimates of ZTD are often treated as realizations of random walk stochastic processes. We derive the corresponding process parameters for 34 different locations in North Europe using two measurement techniques - Global Positioning System (GPS) and Water Vapor Radiometer (WVR). GPS-estimated ZTD is an excellent candidate for data assimilation in numerical weather prediction (NWP) models in terms of both spatial and temporal resolution. We characterize the long term behavior of the ZTD as a function of site latitude and height. The spatial characteristics of the ZTD are studied as a function of site separation and season. We investigate the influence of the time-interpolated atmospheric pressure data used for the estimation of zenith wet delay (ZWD) from ZTD. Characterization of extreme atmospheric events can aid the development of an early warning system. We consider two types of extreme meteorological phenomena with regard to their spatial scales. The first type concerns larger regions (including several GPS sites); the extreme weather is characterized by intense precipitation which may result in a flood. The second type is related to local variations in the ZWD/ZTD and can be used for detection/monitoring of passing atmospheric fronts.

Urban-scale mapping of PM2.5 distribution via data fusion between high-density sensor network and MODIS Aerosol Optical Depth

NASA Astrophysics Data System (ADS)

Ba, Yu Tao; xian Liu, Bao; Sun, Feng; Wang, Li hua; Tang, Yu jia; Zhang, Da wei

2017-04-01

High-resolution mapping of PM2.5 is the prerequisite for precise analytics and subsequent anti-pollution interventions. Considering the large variances of particulate distribution, urban-scale mapping is challenging either with ground-based fixed stations, with satellites or via models. In this study, a dynamic fusion method between high-density sensor network and MODIS Aerosol Optical Depth (AOD) was introduced. The sensor network was deployed in Beijing ( > 1000 fixed monitors across 16000 km2 area) to provide raw observations with high temporal resolution (sampling interval < 1 hour), high spatial resolution in flat areas ( < 1 km), and low spatial resolution in mountainous areas ( > 5 km). The MODIS AOD was calibrated to provide distribution map with low temporal resolution (daily) and moderate spatial resolution ( = 3 km). By encoding the data quality and defects (e.g. could, reflectance, abnormal), a hybrid interpolation procedure with cross-validation generated PM2.5 distribution with both high temporal and spatial resolution. Several no-pollutant and high-pollution periods were tested to validate the proposed fusion method for capturing the instantaneous patterns of PM2.5 emission.

An analysis of spatial representativeness of air temperature monitoring stations

NASA Astrophysics Data System (ADS)

Liu, Suhua; Su, Hongbo; Tian, Jing; Wang, Weizhen

2018-05-01

Surface air temperature is an essential variable for monitoring the atmosphere, and it is generally acquired at meteorological stations that can provide information about only a small area within an r m radius ( r-neighborhood) of the station, which is called the representable radius. In studies on a local scale, ground-based observations of surface air temperatures obtained from scattered stations are usually interpolated using a variety of methods without ascertaining their effectiveness. Thus, it is necessary to evaluate the spatial representativeness of ground-based observations of surface air temperature before conducting studies on a local scale. The present study used remote sensing data to estimate the spatial distribution of surface air temperature using the advection-energy balance for air temperature (ADEBAT) model. Two target stations in the study area were selected to conduct an analysis of spatial representativeness. The results showed that one station (AWS 7) had a representable radius of about 400 m with a possible error of less than 1 K, while the other station (AWS 16) had the radius of about 250 m. The representable radius was large when the heterogeneity of land cover around the station was small.

Phytoplankton community in lake Ebony, Pantai Indah Kapuk, North Jakarta

NASA Astrophysics Data System (ADS)

Pratiwi, NTM; Ayu, IP; Hariyadi, S.; Mulyawati, D.; Iswantari, A.

2018-05-01

Lake Ebony is an ornamental lake in coastal area of North Jakarta, located at 6°6’18”S- 6°6’35”S and 106°44’39’Έ-106°44’56’Έ. Phytoplankton community in Lake Ebony lives in high organic materials received from domestic waste. A spatio-temporal observation at five sites was carried out to understand the spatial distribution of phytoplankton at each group of time of observation and the succession of phytoplankton. Spatial analysis was carried out to map the distribution pattern of plankton,using ArcGIS 10.1 with IDW (Inverse Distance Weighted) interpolation method. Spatial clustering was determined by Canberra Index. The succession of phytoplankton was shown by graph of Frontier succession models, SDI (rate of succession), and SIMI. There were two clustered groups of site. Based on graph of Frontier succession, phytoplankton in Lake Ebony was at Stage 2 and 3 with the rate of succession ranged from 0.008 to 0.003, and value of SIMI ranged from 0.68 to 0.97. There was different spatial distribution pattern of phytoplankton in three groups of observation time, with low rate of succession.

Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area.

PubMed

Zhao, Xinyi; Cheng, Hongguang; He, Siyuan; Cui, Xiangfen; Pu, Xiao; Lu, Lu

2018-07-01

Few studies have linked social factors to air pollution exposure in China. Unlike the race or minority concepts in western countries, the Hukou system (residential registration system) is a fundamental reason for the existence of social deprivation in China. To assess the differences in ozone (O 3 ) exposure among social groups, especially groups divided by Hukou status, we assigned estimates of O 3 exposure to the latest census data of the Beijing urban area using a kriging interpolation model. We developed simultaneous autoregressive (SAR) models that account for spatial autocorrelation to identify the associations between O 3 exposure and social factors. Principal component regression was used to control the multicollinearity bias as well as explore the spatial structure of the social data. The census tracts (CTs) with higher proportions of persons living alone and migrants with non-local Hukou were characterized by greater exposure to ambient O 3 . The areas with greater proportions of seniors had lower O 3 exposure. The spatial distribution patterns were similar among variables including migrants, agricultural population and household separation (population status with separation between Hukou and actual residences), which fit the demographic characteristics of the majority of migrants. Migrants bore a double burden of social deprivation and O 3 pollution exposure due to city development planning and the Hukou system. Copyright © 2018 Elsevier Inc. All rights reserved.

The stochastic runoff-runon process: Extending its analysis to a finite hillslope

NASA Astrophysics Data System (ADS)

Jones, O. D.; Lane, P. N. J.; Sheridan, G. J.

2016-10-01

The stochastic runoff-runon process models the volume of infiltration excess runoff from a hillslope via the overland flow path. Spatial variability is represented in the model by the spatial distribution of rainfall and infiltration, and their ;correlation scale;, that is, the scale at which the spatial correlation of rainfall and infiltration become negligible. Notably, the process can produce runoff even when the mean rainfall rate is less than the mean infiltration rate, and it displays a gradual increase in net runoff as the rainfall rate increases. In this paper we present a number of contributions to the analysis of the stochastic runoff-runon process. Firstly we illustrate the suitability of the process by fitting it to experimental data. Next we extend previous asymptotic analyses to include the cases where the mean rainfall rate equals or exceeds the mean infiltration rate, and then use Monte Carlo simulation to explore the range of parameters for which the asymptotic limit gives a good approximation on finite hillslopes. Finally we use this to obtain an equation for the mean net runoff, consistent with our asymptotic results but providing an excellent approximation for finite hillslopes. Our function uses a single parameter to capture spatial variability, and varying this parameter gives us a family of curves which interpolate between known upper and lower bounds for the mean net runoff.

Modelling soil sodium and potassium adsorption ratio (SPAR) in the immediate period after a grassland fire in Lithuania.

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Cerda, Artemi; Misiūnė, Ieva

2015-04-01

The soil sodium and potassium adsorption ratio (SPAR) is an index that measures the amount of sodium and potassium adsorbed onto clay and organic matter surfaces, in relation to calcium and magnesium. Assess the potential of soil dispersion or flocculation, a process which has implication in soil hydraulic properties and erosion (Sarah, 2004). Depending on severity and the type of ash produced, fire can changes in the immediate period the soil nutrient status (Bodi et al. 2014). Ash releases onto soil surface a large amount of cations, due the high pH. Previous works showed that SPAR from ash slurries is higher than solutions produced from litter (Pereira et al., 2014a). Normally the spatial distribution of topsoil nutrients in the immediate period after the fire is very heterogeneous, due to the different impacts of fire. Thus it is important to identify the most accurate interpolation method in order to identify with better precision the impacts of fire on soil properties. The objective of this work is to test several interpolation methods. The study area is located in near Vilnius (Lithuania) at 54° 42' N, 25° 08 E, 158 masl. Four days after the fire it was designed a plot in a burned area with near Vilnius (Lithuania) at 54° 42' N, 25° 08 E, 158 masl. Twenty five samples were collected from the topsoil. The SPAR index was calculated according to the formula: (Na++K+)/(Ca2++Mg2+)1/2 (Sarah, 2004). Data followed the normal distribution, thus no transformation was required previous to data modelling. Several well know interpolation models were tested, as Inverse Distance to a Weight (IDW) with the power of 1, 2, 3 and 4, Radial Basis Functions (RBF), Inverse Multiquadratic (IMT), Multilog (MTG), Multiquadratic (MTQ), Natural Cubic Spline (NCS) and Thin Plate Spline (TPS) and Local Polynomial (LP) with the power of 1 and 2 and Ordinary Kriging. The best interpolator was the one which had the lowest Root Mean Square Error (RMSE) (Pereira et al., 2014b). The results showed that on average, SPAR index was 0.85, with a minimum of 0.18, a maximum of 1.55, a standard deviation of 0.38 and a coefficient of variation of 44.70%. No previous works were carried out on fire-affected soils, however comparing it to ash slurries obtained from previous works (Pereira et al., 2014a), the values were higher. Among all the interpolation methods tested, the most accurate was IDW 1 (RMSE=0.393), and the less precise NCS (RMSE=0.542). This shows that data distribution is highly variable in space, since IDW methods are better interpolators for data irregularly distributed. The high spatial variability distribution of SPAR is very likely to affect soil hydraulic properties and plant recuperation in the immediate period after the fire. More research is needed to identify the SPAR spatio-temporal impacts of fire on soil. Acknowledgments POSTFIRE (Soil quality, erosion control and plant cover recovery under different post-fire management scenarios, CGL2013-47862-C2-1-R), funded by the Spanish Ministry of Economy and Competitiveness; Fuegored; RECARE (Preventing and Remediating Degradation of Soils in Europe Through Land Care, FP7-ENV-2013-TWO STAGE), funded by the European Commission; and for the COST action ES1306 (Connecting European connectivity research). References Bodi, M., Martin, D.A., Santin, C., Balfour, V., Doerr, S.H., Pereira, P., Cerda, A., Mataix-Solera, J. (2014) Wildland fire ash: production, composition and eco-hyro-geomorphic effects. Earth-Science Reviews, 130, 103-127. Pereira, P., Úbeda, X., Martin, D., Mataix-Solera, J., Cerdà, A., Burguet, M. (2014a) Wildfire effects on extractable elements in ash from Pinus Pinaster forest in Portugal. Hydrological Processes, 28, 3681-3690. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. (2014) Modelling the impacts of wildfire on ash thickness in the immediate period after the fire. Land Degradation and Development. DOI: 10.1002/ldr.2195 Sarah, P. (2004) Soil sodium and potassium adsorption ratio along a Mediterranean-arid transect. Journal of Arid Environments, 59, 731-741.

Distance to health services affects local-level vaccine efficacy for pneumococcal conjugate vaccine (PCV) among rural Filipino children.

PubMed

Root, Elisabeth Dowling; Lucero, Marilla; Nohynek, Hanna; Anthamatten, Peter; Thomas, Deborah S K; Tallo, Veronica; Tanskanen, Antti; Quiambao, Beatriz P; Puumalainen, Taneli; Lupisan, Socorro P; Ruutu, Petri; Ladesma, Erma; Williams, Gail M; Riley, Ian; Simões, Eric A F

2014-03-04

Pneumococcal conjugate vaccines (PCVs) have demonstrated efficacy against childhood pneumococcal disease in several regions globally. We demonstrate how spatial epidemiological analysis of a PCV trial can assist in developing vaccination strategies that target specific geographic subpopulations at greater risk for pneumococcal pneumonia. We conducted a secondary analysis of a randomized, placebo-controlled, double-blind vaccine trial that examined the efficacy of an 11-valent PCV among children less than 2 y of age in Bohol, Philippines. Trial data were linked to the residential location of each participant using a geographic information system. We use spatial interpolation methods to create smoothed surface maps of vaccination rates and local-level vaccine efficacy across the study area. We then measure the relationship between distance to the main study hospital and local-level vaccine efficacy, controlling for ecological factors, using spatial autoregressive models with spatial autoregressive disturbances. We find a significant amount of spatial variation in vaccination rates across the study area. For the primary study endpoint vaccine efficacy increased with distance from the main study hospital from -14% for children living less than 1.5 km from Bohol Regional Hospital (BRH) to 55% for children living greater than 8.5 km from BRH. Spatial regression models indicated that after adjustment for ecological factors, distance to the main study hospital was positively related to vaccine efficacy, increasing at a rate of 4.5% per kilometer distance. Because areas with poor access to care have significantly higher VE, targeted vaccination of children in these areas might allow for a more effective implementation of global programs.

Continuous assimilation of simulated Geosat altimetric sea level into an eddy-resolving numerical ocean model. I - Sea level differences. II - Referenced sea level differences

NASA Technical Reports Server (NTRS)

White, Warren B.; Tai, Chang-Kou; Holland, William R.

1990-01-01

The optimal interpolation method of Lorenc (1981) was used to conduct continuous assimilation of altimetric sea level differences from the simulated Geosat exact repeat mission (ERM) into a three-layer quasi-geostrophic eddy-resolving numerical ocean box model that simulates the statistics of mesoscale eddy activity in the western North Pacific. Assimilation was conducted continuously as the Geosat tracks appeared in simulated real time/space, with each track repeating every 17 days, but occurring at different times and locations within the 17-day period, as would have occurred in a realistic nowcast situation. This interpolation method was also used to conduct the assimilation of referenced altimetric sea level differences into the same model, performing the referencing of altimetric sea sevel differences by using the simulated sea level. The results of this dynamical interpolation procedure are compared with those of a statistical (i.e., optimum) interpolation procedure.

Spherical-earth Gravity and Magnetic Anomaly Modeling by Gauss-legendre Quadrature Integration

NASA Technical Reports Server (NTRS)

Vonfrese, R. R. B.; Hinze, W. J.; Braile, L. W.; Luca, A. J. (Principal Investigator)

1981-01-01

The anomalous potential of gravity and magnetic fields and their spatial derivatives on a spherical Earth for an arbitrary body represented by an equivalent point source distribution of gravity poles or magnetic dipoles were calculated. The distribution of equivalent point sources was determined directly from the coordinate limits of the source volume. Variable integration limits for an arbitrarily shaped body are derived from interpolation of points which approximate the body's surface envelope. The versatility of the method is enhanced by the ability to treat physical property variations within the source volume and to consider variable magnetic fields over the source and observation surface. A number of examples verify and illustrate the capabilities of the technique, including preliminary modeling of potential field signatures for Mississippi embayment crustal structure at satellite elevations.

Optimization of the resolution of remotely sensed digital elevation model to facilitate the simulation and spatial propagation of flood events in flat areas

NASA Astrophysics Data System (ADS)

Karapetsas, Nikolaos; Skoulikaris, Charalampos; Katsogiannos, Fotis; Zalidis, George; Alexandridis, Thomas

2013-04-01

The use of satellite remote sensing products, such as Digital Elevation Models (DEMs), under specific computational interfaces of Geographic Information Systems (GIS) has fostered and facilitated the acquisition of data on specific hydrologic features, such as slope, flow direction and flow accumulation, which are crucial inputs to hydrology or hydraulic models at the river basin scale. However, even though DEMs of different resolution varying from a few km up to 20m are freely available for the European continent, these remotely sensed elevation data are rather coarse in cases where large flat areas are dominant inside a watershed, resulting in an unsatisfactory representation of the terrain characteristics. This scientific work aims at implementing a combing interpolation technique for the amelioration of the analysis of a DEM in order to be used as the input ground model to a hydraulic model for the assessment of potential flood events propagation in plains. More specifically, the second version of the ASTER Global Digital Elevation Model (GDEM2), which has an overall accuracy of around 20 meters, was interpolated with a vast number of aerial control points available from the Hellenic Mapping and Cadastral Organization (HMCO). The uncertainty that was inherent in both the available datasets (ASTER & HMCO) and the appearance of uncorrelated errors and artifacts was minimized by incorporating geostatistical filtering. The resolution of the produced DEM was approximately 10 meters and its validation was conducted with the use of an external dataset of 220 geodetic survey points. The derived DEM was then used as an input to the hydraulic model InfoWorks RS, whose operation is based on the relief characteristics contained in the ground model, for defining, in an automated way, the cross section parameters and simulating the flood spatial distribution. The plain of Serres, which is located in the downstream part of the Struma/Strymon transboundary river basin shared by Bulgaria and Greece, was selected as the case study area, because of its importance to the regional and national economy of Greece and because of the numerous flood events recorded in the past. The results of the simulation processing demonstrated the importance of high resolution relief models for estimating the potential flood hazard zones in order to mitigate the catastrophe caused, both in economic and environmental terms, by this type of extreme event.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430