Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interp...Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interpolation methods(SIMs)is usually used.However,different SIMs may obtain varied estimation values with significant differences,thus affecting the prediction of fish spatial distribution.In this study,different SIMs were used to obtain continuous environmental variables(water depth,water temperature,salinity,dissolved oxygen(DO),p H,chlorophyll a and chemical oxygen demand(COD))in the Changjiang River Estuary(CRE),including inverse distance weighted(IDW)interpolation,ordinary Kriging(OK)(semivariogram model:exponential(OKE),Gaussian(OKG)and spherical(OKS))and radial basis function(RBF)(regularized spline function(RS)and tension spline function(TS)).The accuracy and effect of SIMs were cross-validated,and two-stage generalized additive model(GAM)was used to predict the distribution of Coilia nasus from 2012 to 2014 in CRE.DO and COD were removed before model prediction due to their autocorrelation coefficient based on variance inflation factors analysis.Results showed that the estimated values of environmental variables obtained by the different SIMs differed(i.e.,mean values,range etc.).Cross-validation revealed that the most suitable SIMs of water depth and chlorophyll a was IDW,water temperature and salinity was RS,and p H was OKG.Further,different interpolation results affected the predicted spatial distribution of Coilia nasus in the CRE.The mean values of the predicted abundance were similar,but the differences between and among the maximum value were large.Studies showed that different SIMs can affect estimated values of the environmental variables in the CRE(especially salinity).These variations further suggest that the most applicable SIMs to each variable will also differ.Thus,it is necessary to take these potential impacts into consideration when studying the relationship between the spatial distribution of fishes and environmental changes in the CRE.展开更多
The patial interpolation of borehole data is an important means of stratigraphic structure to construct a three-dimensional model of coal strata,and the reasonable selection of an effective spatial interpolation metho...The patial interpolation of borehole data is an important means of stratigraphic structure to construct a three-dimensional model of coal strata,and the reasonable selection of an effective spatial interpolation method will directly affect the accuracy of three-dimensional modeling of the strata.To select an effective spatial interpolation method and improve the accuracy of 3D modeling of formations,four interpolation methods(the inverse distance weight interpolation algorithm,the local polynomial interpolation algorithm,the radial basis neural network interpolation algorithm and the kriging interpolation algorithm)were compared and analyzed.In particular,the methods of interpolation algorithm,interpolation surface,sample test error,and cross-validation error were used.The experiment of 13-1 seam coal in the Huainan mining area showed the spatial surface interpolation effect of the radial basis neural network interpolation algorithm(RBF)compared with the inverse distance weight interpolation algorithm(IDW),local polynomial interpolation algorithm(LPI)and kriging algorithm.The three interpolation methods have higher accuracy and are more suitable for surface interpolation of coal seams,which is of great significance for improving the accuracy of subsequent 3D modeling of coal seams.展开更多
The use of spatial interpolation methods of data is becoming increasingly common in geophysical analysis, for that reason, currently, several software already contain many of these methods, allowing more detailed stud...The use of spatial interpolation methods of data is becoming increasingly common in geophysical analysis, for that reason, currently, several software already contain many of these methods, allowing more detailed studies. In the present work four interpolation methods are evaluated, for the crustal thickness data of Brazil tectonic provinces, with the intention of making Moho’s map of the regions. The methods used were IDW, Natural Neighbor, Spline and Kriging. We compiled 257 data that constituted a geographic database implemented in the template Postgree PostGIS and were processed using the tools of interpolation located in the Spatyal Analyst Tools program ArcGIS?9 ESRI. Traditional methods, IDW, Natural Neighbor and Spline, generate artifacts in their results, the effects of aim, not consistent with the behavior of crust. Such anomalies are generated because of mathematical formulation methods added to data compiled gravimetry. The analysis results of geostatistical Kriging are more refined and consistent, showing no specific anormalities, i.e., the crustal thickness variation (thinning and thickening) is introduced gradually. Initial our estimates were separated in four specific blocks. With the approval of new networks (BRASIS, RSISNE and RSIS), the crustal thickness database for Brazil may be amended or supplemented so that new models may be generated more consistently, complementing studies of regional tectonics evolution and seismicity.展开更多
Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for g...Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.展开更多
Spatial distribution of and interpolation methods for soil nutrients are the basis of soil nutrient management in precision agriculture.For study of application potential and characteristics of algebra hyper-curve neu...Spatial distribution of and interpolation methods for soil nutrients are the basis of soil nutrient management in precision agriculture.For study of application potential and characteristics of algebra hyper-curve neural network(AHCNN)in delineating spatial variability and interpolation of soil properties,956 soil samples were taken from a 50 hectare field with 20 m interval for alkaline hydrolytic nitrogen measurement.The test data set consisted of 100 random samples extracted from the 956 samples,and the training data set extracted from the remaining samples using 20,40,60,80,100 and 120 m grid intervals.Using the AHCNN model,three training plans were designed,including plan AHC1,using spatial coordinates as the only network input,plan AHC2,adding information of four neighboring points as network input,and plan AHC3,adding information of six neighboring points as network input.The interpolation precision of AHCNN method was compared with that of Kriging method.When the number of training samples was big,interpolation precisions of Kriging and AHCNN were similar.When the number of training samples was small,the precisions of both methods deteriorated.Since AHCNN method has no request on data distribution and it is non-linearization of neutron input variables,it is suitable for delineation of spatial distribution of nonlinear soil properties.In addition,AHCNN has an advantage of adaptive self-adjustment of model parameters,which makes it proper for soil nutrient spatial interpolation.After comparison of mean absolute error d,root mean squared error RMSE,and mean relative error%d,and the spatial distribution maps generated from different methods,it can be concluded that using spatial coordinates as the only network input cannot simulate the characteristics of soil nutrient spatial variability well,and the simulation results can be improved greatly after adding neighboring sample points’information and the distance effect as network input.When the number of samples was small,interpolation precision can be improved after properly increasing the number of neighboring sample points.It was also showed that evaluation of interpolation precision using conventional error statistic indexes was defective,and the spatial distribution map should be used as an important evaluation factor.展开更多
Spatial interpolation is an important method in the process of DEM construction. However, DEMs constructed by interpolation methods may induce serious distortion of surface morphology in areas lack of terrain data. In...Spatial interpolation is an important method in the process of DEM construction. However, DEMs constructed by interpolation methods may induce serious distortion of surface morphology in areas lack of terrain data. In order to solve this problem, this paper proposes a strategy combining high-accuracy surface modeling(HASM) and classical interpolation methods to construct DEM. Firstly, a triangulated irregular network(TIN) is built based on the original terrain data, and the area of the triangles in the TIN is used to determine whether to add supplementary altimetric points(SA-Points). Then, classical interpolation methods, such as Inverse Distance Weighted(IDW) method, Kriging, and Spline, are applied to assign elevation values to the SA-Points. Finally, the SA-Points are merged with the original terrain data, and HASM is used to construct DEM. In this research, two test areas which are located in Nanjing suburb in Jiangsu Province and Guiyang suburb in Guizhou Province are selected to verify the feasibility of the new strategy. The study results show that:(1) The combination of HASM and classical interpolation methods can significantly improve the elevation accuracy of DEMs compared with DEM constructed by a single method.(2) The process of adding SA-Points proposed in this study can be repeated in many times. For the test areas in this paper, compared with the results with only one execution, the results with more executions are in much more accordance with the actual terrain.(3) Among all the methods discussed in this paper, the one combined HASM and Kriging produce the best result. Compared with the HASM alone, absolute mean error(MAE) and root mean square error(RMSE) of the best result were reduced from 1.29 m and 1.83 m to 0.68 m and 0.45 m(the first test area), and from 0.32 m and 0.38 m to 0.21 m and 0.28 m( The second test area).展开更多
The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provid...The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models.However,traditional interpolation methods(nearest neighbor interpolation,bilinear interpolation,and bicubic interpolation)lack physical constraints and can generate significant errors at land-sea boundaries and around islands.In this paper,a machine learning method is used to design an interpolation algorithm based on Gaussian process regression.The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information(sea surface wind stress,sea surface heat flux,and ocean current velocity).This greatly improves the spatial resolution of ocean features and the interpolation accuracy.The eff ectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature(SST).The root mean square error(RMSE)of the interpolation algorithm was 38.9%,43.7%,and 62.4%lower than that of bilinear interpolation,bicubic interpolation,and nearest neighbor interpolation,respectively.The interpolation accuracy was also significantly better in off shore area and around islands.The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.展开更多
The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is c...The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is crucial for managing and conserving genetic resources in forest tree species. By combining tools from population genetics, landscape ecology and spatial statistics, landscape genetics thus represents a powerful method for evaluating the geographic patterns of genetic resources at the population level. In this study, we explore the possibility of combining genetic diversity data, spatial statistic tools and GIS technologies to map the genetic divergence and diversity of 31 Castanea sativa populations collected in Spain, Italy, Greece, and Turkey. The IDW technique was used to interpolate the diversity values and divergence indices as expected hetereozygosity (He), allelic richness (Rs), private allelic richness (PRs), and membership values (Q) of each population to different clusters. Genetic diversity maps and a synthetic map of the spatial genetic structure of European chestnut populations were produced. Spatial coincidences between landscape elements and statistically significant genetic discontinuities between populations were investigated. Evidence is provided of the significance of cartographic outputs produced in the study and on their usefulness in managing genetic resources.展开更多
This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning(ML) methods. Instead of using XY coordinate fields directl...This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning(ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields(GDFs) is designed to enable the ML models to infer the spatial relationship between the sampled locations and unknown locations. The whole framework using GDF with ML methods is named GDF-ML. This framework is purely data-driven which avoids the tedious work in the scale of fluctuations(SOFs)estimating and data detrending in the conventional spatial interpolation methods. Six local mapping ML methods(extra trees(ETs), gradient boosting(GB), extreme gradient boosting(XGBoost), random forest(RF), general regression neural network(GRNN) and k-nearest neighbors(KNN)) are compared in the GDF-ML framework. The results show that the GDFs are better than the conventional XY coordinate fields based ML methods in both accuracy and spatial continuity. GDF-ML is flexible which can be applied to high-dimensional, multi-variable and incomplete datasets. Among these six methods, GDF with ET method(GDF-ET) clearly shows the best accuracy and best spatial continuity. The proposed GDF-ET method can provide a fast and accurate interpretation of the soil property profile. Sensitivity analysis shows that this method is applicable to very small training dataset size. The associated statistical uncertainty can also be quantified so that the reliability of the subsurface modeling results can be estimated objectively and explicitly. The uncertainty results clearly show that the prediction becomes more accurate when more sampled data are available.展开更多
This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bil...This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.展开更多
Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictio...Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictions of numerical models and the actual sea state has been observed,predictions can only be released after correction by forecasters.This paper proposes a spati-otemporal interactive processing bias correction method to correct numerical prediction fields applied to the production and release of operational ocean wave forecasting products.The proposed method combines the advantages of numerical models and Forecast Discussion;specifically,it integrates subjective and objective information to achieve interactive spatiotemporal correc-tions for numerical prediction.The method corrects the single-time numerical prediction field in space by spatial interpolation and sub-zone numerical analyses using numerical model grid data in combination with real-time observations and the artificial judg-ment of forecasters to achieve numerical prediction accuracy.The difference between the original numerical prediction field and the spatial correction field is interpolated to an adjacent time series by successive correction analysis,thereby achieving highly efficient correction for multi-time forecasting fields.In this paper,the significant wave height forecasts from the European Centre for Medium-Range Weather Forecasts are used as background field for forecasting correction and analysis.Results indicate that the proposed method has good application potential for the bias correction of numerical predictions under different sea states.The method takes into account spatial correlations for the numerical prediction field and the time series development of the numerical model to correct numerical predictions efficiently.展开更多
Numerical climate models render data in a gridded format which is often problematic for integrated analysis with other kinds of data in jurisdictional formats. In this paper a joint analysis of municipal Gross Domesti...Numerical climate models render data in a gridded format which is often problematic for integrated analysis with other kinds of data in jurisdictional formats. In this paper a joint analysis of municipal Gross Domestic Product per capita (GDPc) and predicted temperature increase was undertaken in order to estimate different levels of human and economic exposure. This is based on a method of converting model outputs into a country municipal grid which enabled depicting climate predictions from the Eta-Hadgem2-ES Regional Climate Model (RCM) into the municipal level in Brazil. The conversion to country municipality grid was made using a combination of interpolation and buffering techniques in ArcGIS for two emission scenarios (RCP 4.5 and 8.5) and three timeframes (2011-2040, 2041-2070, 2071-2100) for mean temperature increase and number of heatwave days (WSDI). The results were used to support the Third National Communication (TCN) of Brazil to the United Nations Framework Convention on Climate Change (UNFCCC) and show a coherent matching of the gridded output from the original RCM. The joint climate and GDPc analysis show that in the beginning of the century the more severe warming is centred over regions where GDPc is generally higher (Centre-West and Southeast). At the end of the century, critical levels of warming spread north and northeastwards where municipalities have the lowest GDPc levels. In the high emission scenario (RCP 8.5), the strongest warming and the spreading over poorer regions are anticipated to the mid-century. These results are the key to further explore solutions for climate change adaptation based on current resources and prepare in different sectors, for long-term risk management and climate adaptation planning strategies.展开更多
Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this stu...Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.展开更多
Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computatio...Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computational resources(e.g.cokriging).This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey.This new method,named MIDW(Ws),is a modified IDW through the integration of IDW with wind profile model,power law(PL),representing the influence of land cover and topography on Ws.Terrain features and elevation data of PL were obtained using normalized difference vegetation index(NDVI)and digital elevation model(DEM),respectively.Results showed superior and comparable performance of MIDW(Ws)to standard IDW and ordinary kriging(OK)across all months of year.Compared to ordinary cokriging(OCK)using DEM as covariate,MIDW(Ws)generated better results in the arid–semiarid seasons(around summer).Local complex atmospheric conditions during rainy seasons(around winter)may have affected the performance of incorporating PL with MIDW(Ws).Generally,the proposed MIDW(Ws)is simpler and easier to implement compared to OCK.For landscape-scale projects,its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.展开更多
Rapid estimation of post-earthquake building damage and loss is very important in urgent response efforts.The current approach leaves much room for improvement in estimating ground motion and correctly incorporating t...Rapid estimation of post-earthquake building damage and loss is very important in urgent response efforts.The current approach leaves much room for improvement in estimating ground motion and correctly incorporating the uncertainty and spatial correlation of the loss.This study proposed a new approach for rapidly estimating post-earthquake building loss with reasonable accuracy.The proposed method interpolates ground motion based on the observed ground motion using the Ground Motion Prediction Equation(GMPE)as the weight.It samples the building seismic loss quantile considering the spatial loss correlation that is expressed by Gaussian copula,and kriging is applied to reduce the dimension of direct sampling for estimation speed.The proposed approach was validated using three historical earthquake events in Japan with actual loss reports,and was then applied to predict the building loss amount for the March 2022 Fukushima Mw7.3 earthquake.The proposed method has high potential in future emergency efforts such as search,rescue,and evacuation planning.展开更多
The finite-difference time-domain(FDTD)method is used effectively to solve electromagnetic(EM)scattering and radiation problems using a 3D sub-gridding algorithm.For accuracy and stability of the FDTD method,the compu...The finite-difference time-domain(FDTD)method is used effectively to solve electromagnetic(EM)scattering and radiation problems using a 3D sub-gridding algorithm.For accuracy and stability of the FDTD method,the computational domain of EM problems with locally fine structures or electrically small objects is discretized with finer grids.This sub-gridding algorithm for different regions of the computational domain was implemented to increase the accuracy and reduce the computational time and memory requirements compared to those of the traditional FDTD method.In the sub-gridding algorithm,the FDTD computational domain is divided into separate regions:coarse grid and fine grid regions.Since the cell sizes and time steps are different in the coarse and fine grid regions,interpolations in both time and space are used to evaluate the electric and magnetic fields on the boundaries between different regions.The accuracy of the developed 3D sub-gridding algorithm has been verified for radiation and scattering problems,including multiple fine grid regions.Excellent performance is obtained even for higher and different contrast ratios in fine grid regions.展开更多
The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size,spatial structure,and auxiliary variables(terrain indices and electromagnetic induction surv...The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size,spatial structure,and auxiliary variables(terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes(agricultural vs.forested).When spatial structure could not be well captured by point-based observations(e.g.,when the ratio of sample spacing over correlation range was > 0.5),or when a strong relationship existed between target soil properties and auxiliary variables(e.g.,their R2 was > 0.6),regression kriging(RK) was more accurate for interpolating soil properties in both landscapes studied.Otherwise,ordinary kriging(OK) was better.Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation,because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables.Because of a smaller ratio of elevation change over total study area(E/A = 1.2) and multiple parent materials in the agricultural land,OK was generally more accurate in that landscape.In contrast,a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment.The results from this study can be useful for selecting kriging for various soil properties and landscapes.展开更多
We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface ...We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.展开更多
Present work is exploring the influence of land cover on channel morphology in 34 headwater catchments of the lateritic belt of West Bengal.Non-parametric tests(Mann-Whitney U and Kruskal-Wallis)and multivariate analy...Present work is exploring the influence of land cover on channel morphology in 34 headwater catchments of the lateritic belt of West Bengal.Non-parametric tests(Mann-Whitney U and Kruskal-Wallis)and multivariate analysis(Principal Component Analysis and Canonical Discriminant Function models)have successfully differentiated the performance of land cover on channel morphology adjustment among the three groups of headwater streams(forested,transitional,and agricultural)on the Kunur River Basin(KRB).Spatial Interpolation Techniques reveal that intense land-use change,particularly forest conversion to agricultural land,is significantly increasing channel widths(269%)and cross-section area(78%),whereas agricultural channels become shallower(40%)than would be predicted from forested streams.Catchments with the dominance of forest and agricultural land are classified as‘C′and‘B′types of streams respectively,as per Rosgen's Stream Classification Model.Finally,the work claimed that transitional stream group is the definitive area to exaggerate the river restoration plan to stabilize the anthropogenic deformation on channel morphology.展开更多
Background:Many countries are experiencing significant water scarcity and related problems due to limited availability,uneven distribution of water resources and high demand.Therefore,increasing water use efficiency a...Background:Many countries are experiencing significant water scarcity and related problems due to limited availability,uneven distribution of water resources and high demand.Therefore,increasing water use efficiency and better management of existing resources have become substantially important.The agricultural sector is responsible for around 80%of global freshwater withdrawal.Wheat is one of the most important crops having large volumes of virtual water(VW)which is defined as the hidden water embedded in the products.Methods:Water footprint(WF)is an indicator showing the total volume of freshwater consumption of a product or process.Blue water concept is defined as the amount of exploited surface and groundwater resources.Green water represents the total volume of rainwater allocated by the product.WF methodology brings a new approach to inter-regional water use and management by quantifying the amount of direct and indirect water use and tracing the hidden links between production,consumption and trade.The main objective of this study is to analyze Turkey’s national blue and green WF of wheat production,consumption and virtual water trade between 2008 and 2019.Detailed province-based quantification of wheat’s water exploitation is provided using spatial interpolation method.Results:Total consumptive WF of wheat production and consumption of Turkey is calculated as 39.3 and 48.1 Gm^(3)/year,respectively.The average blue and green VW contents of wheat production through Turkey are assessed to be 1161 and 748 m^(3)/ton,respectively.The water footprint parameters of each province are calculated and discussed using climatic and agricultural data.VW transfer of Turkey’s international wheat trade is also analyzed.Total national water saving is calculated as 7.8 Gm^(3)/year which is mostly imported from Russia.Global VW deficit due to international wheat trade is calculated to be 1.76 Gm^(3)/year.Conclusion:Despite its high contribution to global wheat production,increasing population and strong wheatbased diet,quantitative,comparative and up-to-date analyses of the blue and green WF and the VW transfer of wheat production in Turkey are not available.This study contributes to the national and international water management and planning studies to increase the water allocation efficiency of agricultural products.展开更多
基金The Shanghai Municipal Science and Technology Commission Local Capacity Construction Project under contract No.18050502000the Monitoring and Evaluation of National Sea Ranch Demonstration Area Project in Changjiang River Estuary under contract No.171015the National Natural Science Foundation of China under contract No.41906074。
文摘Spatial-temporal distribution of marine fishes is strongly influenced by environmental factors.To obtain a more continuous distribution of these variables usually measured by stationary sampling designs,spatial interpolation methods(SIMs)is usually used.However,different SIMs may obtain varied estimation values with significant differences,thus affecting the prediction of fish spatial distribution.In this study,different SIMs were used to obtain continuous environmental variables(water depth,water temperature,salinity,dissolved oxygen(DO),p H,chlorophyll a and chemical oxygen demand(COD))in the Changjiang River Estuary(CRE),including inverse distance weighted(IDW)interpolation,ordinary Kriging(OK)(semivariogram model:exponential(OKE),Gaussian(OKG)and spherical(OKS))and radial basis function(RBF)(regularized spline function(RS)and tension spline function(TS)).The accuracy and effect of SIMs were cross-validated,and two-stage generalized additive model(GAM)was used to predict the distribution of Coilia nasus from 2012 to 2014 in CRE.DO and COD were removed before model prediction due to their autocorrelation coefficient based on variance inflation factors analysis.Results showed that the estimated values of environmental variables obtained by the different SIMs differed(i.e.,mean values,range etc.).Cross-validation revealed that the most suitable SIMs of water depth and chlorophyll a was IDW,water temperature and salinity was RS,and p H was OKG.Further,different interpolation results affected the predicted spatial distribution of Coilia nasus in the CRE.The mean values of the predicted abundance were similar,but the differences between and among the maximum value were large.Studies showed that different SIMs can affect estimated values of the environmental variables in the CRE(especially salinity).These variations further suggest that the most applicable SIMs to each variable will also differ.Thus,it is necessary to take these potential impacts into consideration when studying the relationship between the spatial distribution of fishes and environmental changes in the CRE.
文摘The patial interpolation of borehole data is an important means of stratigraphic structure to construct a three-dimensional model of coal strata,and the reasonable selection of an effective spatial interpolation method will directly affect the accuracy of three-dimensional modeling of the strata.To select an effective spatial interpolation method and improve the accuracy of 3D modeling of formations,four interpolation methods(the inverse distance weight interpolation algorithm,the local polynomial interpolation algorithm,the radial basis neural network interpolation algorithm and the kriging interpolation algorithm)were compared and analyzed.In particular,the methods of interpolation algorithm,interpolation surface,sample test error,and cross-validation error were used.The experiment of 13-1 seam coal in the Huainan mining area showed the spatial surface interpolation effect of the radial basis neural network interpolation algorithm(RBF)compared with the inverse distance weight interpolation algorithm(IDW),local polynomial interpolation algorithm(LPI)and kriging algorithm.The three interpolation methods have higher accuracy and are more suitable for surface interpolation of coal seams,which is of great significance for improving the accuracy of subsequent 3D modeling of coal seams.
基金supported CNPq/Instituto do Milenio grant 420222/05-7CNPq/INCT 573713/2008-1.
文摘The use of spatial interpolation methods of data is becoming increasingly common in geophysical analysis, for that reason, currently, several software already contain many of these methods, allowing more detailed studies. In the present work four interpolation methods are evaluated, for the crustal thickness data of Brazil tectonic provinces, with the intention of making Moho’s map of the regions. The methods used were IDW, Natural Neighbor, Spline and Kriging. We compiled 257 data that constituted a geographic database implemented in the template Postgree PostGIS and were processed using the tools of interpolation located in the Spatyal Analyst Tools program ArcGIS?9 ESRI. Traditional methods, IDW, Natural Neighbor and Spline, generate artifacts in their results, the effects of aim, not consistent with the behavior of crust. Such anomalies are generated because of mathematical formulation methods added to data compiled gravimetry. The analysis results of geostatistical Kriging are more refined and consistent, showing no specific anormalities, i.e., the crustal thickness variation (thinning and thickening) is introduced gradually. Initial our estimates were separated in four specific blocks. With the approval of new networks (BRASIS, RSISNE and RSIS), the crustal thickness database for Brazil may be amended or supplemented so that new models may be generated more consistently, complementing studies of regional tectonics evolution and seismicity.
基金supported by grants from the Research Grants Council of Hong Kong Special Administrative Region,China(Project No.City U 11213119 and T22-603/15N)The financial support is gratefully acknowledgedfinancial support from the Hong Kong Ph.D.Fellowship Scheme funded by the Research Grants Council of Hong Kong,China。
文摘Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.
基金This research was financially supported by the National Natural Science Foundation of China(30600375)the National High Technology Research and Development Program of China(2006AA10A306,2006AA10Z271)。
文摘Spatial distribution of and interpolation methods for soil nutrients are the basis of soil nutrient management in precision agriculture.For study of application potential and characteristics of algebra hyper-curve neural network(AHCNN)in delineating spatial variability and interpolation of soil properties,956 soil samples were taken from a 50 hectare field with 20 m interval for alkaline hydrolytic nitrogen measurement.The test data set consisted of 100 random samples extracted from the 956 samples,and the training data set extracted from the remaining samples using 20,40,60,80,100 and 120 m grid intervals.Using the AHCNN model,three training plans were designed,including plan AHC1,using spatial coordinates as the only network input,plan AHC2,adding information of four neighboring points as network input,and plan AHC3,adding information of six neighboring points as network input.The interpolation precision of AHCNN method was compared with that of Kriging method.When the number of training samples was big,interpolation precisions of Kriging and AHCNN were similar.When the number of training samples was small,the precisions of both methods deteriorated.Since AHCNN method has no request on data distribution and it is non-linearization of neutron input variables,it is suitable for delineation of spatial distribution of nonlinear soil properties.In addition,AHCNN has an advantage of adaptive self-adjustment of model parameters,which makes it proper for soil nutrient spatial interpolation.After comparison of mean absolute error d,root mean squared error RMSE,and mean relative error%d,and the spatial distribution maps generated from different methods,it can be concluded that using spatial coordinates as the only network input cannot simulate the characteristics of soil nutrient spatial variability well,and the simulation results can be improved greatly after adding neighboring sample points’information and the distance effect as network input.When the number of samples was small,interpolation precision can be improved after properly increasing the number of neighboring sample points.It was also showed that evaluation of interpolation precision using conventional error statistic indexes was defective,and the spatial distribution map should be used as an important evaluation factor.
基金supported by Key Project of Natural Science Research of Anhui Provincial Department of Education (No.KJ2020A0722,No.KJ2020A0721,No.KJ2020A0705)Grant from National Sensor Network Engineering Technology Research Center (No.NSNC202103)+6 种基金National Natural Science Foundation of China (No.41930102)Grant from State Key Laboratory of Resources and Environmental Information System in 2018Key Project of Research and Development in Chuzhou Science and Technology Program (No.2020ZG016)Open Fund of Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying,Mapping and Remote Sensing,Hunan University of Science and Technology (No.E22136 )Innovation program for Returned Overseas Chinese Scholars of Anhui Province (No.2021LCX014)Anhui Province Universities Outstanding Talented Person Support Project (No.gxyq2019093)Anhui Provincial College Natural Science Research General Project (No.KJ2020B01,No.KJ2020B02)。
文摘Spatial interpolation is an important method in the process of DEM construction. However, DEMs constructed by interpolation methods may induce serious distortion of surface morphology in areas lack of terrain data. In order to solve this problem, this paper proposes a strategy combining high-accuracy surface modeling(HASM) and classical interpolation methods to construct DEM. Firstly, a triangulated irregular network(TIN) is built based on the original terrain data, and the area of the triangles in the TIN is used to determine whether to add supplementary altimetric points(SA-Points). Then, classical interpolation methods, such as Inverse Distance Weighted(IDW) method, Kriging, and Spline, are applied to assign elevation values to the SA-Points. Finally, the SA-Points are merged with the original terrain data, and HASM is used to construct DEM. In this research, two test areas which are located in Nanjing suburb in Jiangsu Province and Guiyang suburb in Guizhou Province are selected to verify the feasibility of the new strategy. The study results show that:(1) The combination of HASM and classical interpolation methods can significantly improve the elevation accuracy of DEMs compared with DEM constructed by a single method.(2) The process of adding SA-Points proposed in this study can be repeated in many times. For the test areas in this paper, compared with the results with only one execution, the results with more executions are in much more accordance with the actual terrain.(3) Among all the methods discussed in this paper, the one combined HASM and Kriging produce the best result. Compared with the HASM alone, absolute mean error(MAE) and root mean square error(RMSE) of the best result were reduced from 1.29 m and 1.83 m to 0.68 m and 0.45 m(the first test area), and from 0.32 m and 0.38 m to 0.21 m and 0.28 m( The second test area).
基金Supported by the National Natural Science Foundation of China(Nos.41675097,41375113)。
文摘The resolution of ocean reanalysis datasets is generally low because of the limited resolution of their associated numerical models.Low-resolution ocean reanalysis datasets are therefore usually interpolated to provide an initial or boundary field for higher-resolution regional ocean models.However,traditional interpolation methods(nearest neighbor interpolation,bilinear interpolation,and bicubic interpolation)lack physical constraints and can generate significant errors at land-sea boundaries and around islands.In this paper,a machine learning method is used to design an interpolation algorithm based on Gaussian process regression.The method uses a multiscale kernel function to process two-dimensional space meteorological ocean processes and introduces multiscale physical feature information(sea surface wind stress,sea surface heat flux,and ocean current velocity).This greatly improves the spatial resolution of ocean features and the interpolation accuracy.The eff ectiveness of the algorithm was validated through interpolation experiments relating to sea surface temperature(SST).The root mean square error(RMSE)of the interpolation algorithm was 38.9%,43.7%,and 62.4%lower than that of bilinear interpolation,bicubic interpolation,and nearest neighbor interpolation,respectively.The interpolation accuracy was also significantly better in off shore area and around islands.The algorithm has an acceptable runtime cost and good temporal and spatial generalizability.
文摘The current distribution of forest tree species is a result of natural or human mediated historical and contemporary processes. Knowledge of the spatial distribution of the diversity and divergence of populations is crucial for managing and conserving genetic resources in forest tree species. By combining tools from population genetics, landscape ecology and spatial statistics, landscape genetics thus represents a powerful method for evaluating the geographic patterns of genetic resources at the population level. In this study, we explore the possibility of combining genetic diversity data, spatial statistic tools and GIS technologies to map the genetic divergence and diversity of 31 Castanea sativa populations collected in Spain, Italy, Greece, and Turkey. The IDW technique was used to interpolate the diversity values and divergence indices as expected hetereozygosity (He), allelic richness (Rs), private allelic richness (PRs), and membership values (Q) of each population to different clusters. Genetic diversity maps and a synthetic map of the spatial genetic structure of European chestnut populations were produced. Spatial coincidences between landscape elements and statistically significant genetic discontinuities between populations were investigated. Evidence is provided of the significance of cartographic outputs produced in the study and on their usefulness in managing genetic resources.
基金funded by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (Project DP190101592)the National Natural Science Foundation of China (Grant Nos. 41972280 and 52179103)。
文摘This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning(ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields(GDFs) is designed to enable the ML models to infer the spatial relationship between the sampled locations and unknown locations. The whole framework using GDF with ML methods is named GDF-ML. This framework is purely data-driven which avoids the tedious work in the scale of fluctuations(SOFs)estimating and data detrending in the conventional spatial interpolation methods. Six local mapping ML methods(extra trees(ETs), gradient boosting(GB), extreme gradient boosting(XGBoost), random forest(RF), general regression neural network(GRNN) and k-nearest neighbors(KNN)) are compared in the GDF-ML framework. The results show that the GDFs are better than the conventional XY coordinate fields based ML methods in both accuracy and spatial continuity. GDF-ML is flexible which can be applied to high-dimensional, multi-variable and incomplete datasets. Among these six methods, GDF with ET method(GDF-ET) clearly shows the best accuracy and best spatial continuity. The proposed GDF-ET method can provide a fast and accurate interpretation of the soil property profile. Sensitivity analysis shows that this method is applicable to very small training dataset size. The associated statistical uncertainty can also be quantified so that the reliability of the subsurface modeling results can be estimated objectively and explicitly. The uncertainty results clearly show that the prediction becomes more accurate when more sampled data are available.
文摘This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.
基金supported by the National Key Research and Development Program of China(No.2018YFC1407002)the National Natural Science Foundation of China(Nos.62071279,41930535)the SDUST Research Fund(No.2019TDJH103).
文摘Numerical models and correct predictions are important for marine forecasting,but the forecasting results are often unable to satisfy the requirements of operational wave forecasting.Because bias between the predictions of numerical models and the actual sea state has been observed,predictions can only be released after correction by forecasters.This paper proposes a spati-otemporal interactive processing bias correction method to correct numerical prediction fields applied to the production and release of operational ocean wave forecasting products.The proposed method combines the advantages of numerical models and Forecast Discussion;specifically,it integrates subjective and objective information to achieve interactive spatiotemporal correc-tions for numerical prediction.The method corrects the single-time numerical prediction field in space by spatial interpolation and sub-zone numerical analyses using numerical model grid data in combination with real-time observations and the artificial judg-ment of forecasters to achieve numerical prediction accuracy.The difference between the original numerical prediction field and the spatial correction field is interpolated to an adjacent time series by successive correction analysis,thereby achieving highly efficient correction for multi-time forecasting fields.In this paper,the significant wave height forecasts from the European Centre for Medium-Range Weather Forecasts are used as background field for forecasting correction and analysis.Results indicate that the proposed method has good application potential for the bias correction of numerical predictions under different sea states.The method takes into account spatial correlations for the numerical prediction field and the time series development of the numerical model to correct numerical predictions efficiently.
基金the United Nations Programme for Development(UNDP),Rede Clima(FINEP 01.13.0353.00)the Brazilian Ministry for Science and Technology for their financial support
文摘Numerical climate models render data in a gridded format which is often problematic for integrated analysis with other kinds of data in jurisdictional formats. In this paper a joint analysis of municipal Gross Domestic Product per capita (GDPc) and predicted temperature increase was undertaken in order to estimate different levels of human and economic exposure. This is based on a method of converting model outputs into a country municipal grid which enabled depicting climate predictions from the Eta-Hadgem2-ES Regional Climate Model (RCM) into the municipal level in Brazil. The conversion to country municipality grid was made using a combination of interpolation and buffering techniques in ArcGIS for two emission scenarios (RCP 4.5 and 8.5) and three timeframes (2011-2040, 2041-2070, 2071-2100) for mean temperature increase and number of heatwave days (WSDI). The results were used to support the Third National Communication (TCN) of Brazil to the United Nations Framework Convention on Climate Change (UNFCCC) and show a coherent matching of the gridded output from the original RCM. The joint climate and GDPc analysis show that in the beginning of the century the more severe warming is centred over regions where GDPc is generally higher (Centre-West and Southeast). At the end of the century, critical levels of warming spread north and northeastwards where municipalities have the lowest GDPc levels. In the high emission scenario (RCP 8.5), the strongest warming and the spreading over poorer regions are anticipated to the mid-century. These results are the key to further explore solutions for climate change adaptation based on current resources and prepare in different sectors, for long-term risk management and climate adaptation planning strategies.
基金Science and Technology Program of Guangzhou,China(2014A050503060).
文摘Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.
文摘Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computational resources(e.g.cokriging).This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey.This new method,named MIDW(Ws),is a modified IDW through the integration of IDW with wind profile model,power law(PL),representing the influence of land cover and topography on Ws.Terrain features and elevation data of PL were obtained using normalized difference vegetation index(NDVI)and digital elevation model(DEM),respectively.Results showed superior and comparable performance of MIDW(Ws)to standard IDW and ordinary kriging(OK)across all months of year.Compared to ordinary cokriging(OCK)using DEM as covariate,MIDW(Ws)generated better results in the arid–semiarid seasons(around summer).Local complex atmospheric conditions during rainy seasons(around winter)may have affected the performance of incorporating PL with MIDW(Ws).Generally,the proposed MIDW(Ws)is simpler and easier to implement compared to OCK.For landscape-scale projects,its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.
基金supported by the Scientific Research Fund of the Institute of Engineering Mechanics,China Earthquake Administration(Grant No.2021B09)the National Natural Science Foundation of China(Grant No.51978634)。
文摘Rapid estimation of post-earthquake building damage and loss is very important in urgent response efforts.The current approach leaves much room for improvement in estimating ground motion and correctly incorporating the uncertainty and spatial correlation of the loss.This study proposed a new approach for rapidly estimating post-earthquake building loss with reasonable accuracy.The proposed method interpolates ground motion based on the observed ground motion using the Ground Motion Prediction Equation(GMPE)as the weight.It samples the building seismic loss quantile considering the spatial loss correlation that is expressed by Gaussian copula,and kriging is applied to reduce the dimension of direct sampling for estimation speed.The proposed approach was validated using three historical earthquake events in Japan with actual loss reports,and was then applied to predict the building loss amount for the March 2022 Fukushima Mw7.3 earthquake.The proposed method has high potential in future emergency efforts such as search,rescue,and evacuation planning.
文摘The finite-difference time-domain(FDTD)method is used effectively to solve electromagnetic(EM)scattering and radiation problems using a 3D sub-gridding algorithm.For accuracy and stability of the FDTD method,the computational domain of EM problems with locally fine structures or electrically small objects is discretized with finer grids.This sub-gridding algorithm for different regions of the computational domain was implemented to increase the accuracy and reduce the computational time and memory requirements compared to those of the traditional FDTD method.In the sub-gridding algorithm,the FDTD computational domain is divided into separate regions:coarse grid and fine grid regions.Since the cell sizes and time steps are different in the coarse and fine grid regions,interpolations in both time and space are used to evaluate the electric and magnetic fields on the boundaries between different regions.The accuracy of the developed 3D sub-gridding algorithm has been verified for radiation and scattering problems,including multiple fine grid regions.Excellent performance is obtained even for higher and different contrast ratios in fine grid regions.
基金Supported by the United States Department of Agriculture National Research Initiative Grant(No.2002-35102-12547)
文摘The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size,spatial structure,and auxiliary variables(terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes(agricultural vs.forested).When spatial structure could not be well captured by point-based observations(e.g.,when the ratio of sample spacing over correlation range was > 0.5),or when a strong relationship existed between target soil properties and auxiliary variables(e.g.,their R2 was > 0.6),regression kriging(RK) was more accurate for interpolating soil properties in both landscapes studied.Otherwise,ordinary kriging(OK) was better.Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation,because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables.Because of a smaller ratio of elevation change over total study area(E/A = 1.2) and multiple parent materials in the agricultural land,OK was generally more accurate in that landscape.In contrast,a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment.The results from this study can be useful for selecting kriging for various soil properties and landscapes.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41930647, 41590844, 41421001 & 41971358)the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (Grant No. XDA20030203)+1 种基金the Innovation Project of LREIS (Grant No. O88RA600YA)the Biodiversity Investigation, Observation and Assessment Program (2019–2023) of the Ministry of Ecology and Environment of China。
文摘We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.
基金University Grand Commission,New Delhi,India,for the financial support as Junior Research Fellowship[Award Letter No.:F.15-6(DEC.,2012)/2013(NET),UGC Ref.No.3224/(NET-DEC.2012)]to carry out the research work presented in this paper.
文摘Present work is exploring the influence of land cover on channel morphology in 34 headwater catchments of the lateritic belt of West Bengal.Non-parametric tests(Mann-Whitney U and Kruskal-Wallis)and multivariate analysis(Principal Component Analysis and Canonical Discriminant Function models)have successfully differentiated the performance of land cover on channel morphology adjustment among the three groups of headwater streams(forested,transitional,and agricultural)on the Kunur River Basin(KRB).Spatial Interpolation Techniques reveal that intense land-use change,particularly forest conversion to agricultural land,is significantly increasing channel widths(269%)and cross-section area(78%),whereas agricultural channels become shallower(40%)than would be predicted from forested streams.Catchments with the dominance of forest and agricultural land are classified as‘C′and‘B′types of streams respectively,as per Rosgen's Stream Classification Model.Finally,the work claimed that transitional stream group is the definitive area to exaggerate the river restoration plan to stabilize the anthropogenic deformation on channel morphology.
文摘Background:Many countries are experiencing significant water scarcity and related problems due to limited availability,uneven distribution of water resources and high demand.Therefore,increasing water use efficiency and better management of existing resources have become substantially important.The agricultural sector is responsible for around 80%of global freshwater withdrawal.Wheat is one of the most important crops having large volumes of virtual water(VW)which is defined as the hidden water embedded in the products.Methods:Water footprint(WF)is an indicator showing the total volume of freshwater consumption of a product or process.Blue water concept is defined as the amount of exploited surface and groundwater resources.Green water represents the total volume of rainwater allocated by the product.WF methodology brings a new approach to inter-regional water use and management by quantifying the amount of direct and indirect water use and tracing the hidden links between production,consumption and trade.The main objective of this study is to analyze Turkey’s national blue and green WF of wheat production,consumption and virtual water trade between 2008 and 2019.Detailed province-based quantification of wheat’s water exploitation is provided using spatial interpolation method.Results:Total consumptive WF of wheat production and consumption of Turkey is calculated as 39.3 and 48.1 Gm^(3)/year,respectively.The average blue and green VW contents of wheat production through Turkey are assessed to be 1161 and 748 m^(3)/ton,respectively.The water footprint parameters of each province are calculated and discussed using climatic and agricultural data.VW transfer of Turkey’s international wheat trade is also analyzed.Total national water saving is calculated as 7.8 Gm^(3)/year which is mostly imported from Russia.Global VW deficit due to international wheat trade is calculated to be 1.76 Gm^(3)/year.Conclusion:Despite its high contribution to global wheat production,increasing population and strong wheatbased diet,quantitative,comparative and up-to-date analyses of the blue and green WF and the VW transfer of wheat production in Turkey are not available.This study contributes to the national and international water management and planning studies to increase the water allocation efficiency of agricultural products.
