共查询到20条相似文献,搜索用时 609 毫秒
1.
Hannes Kazianka 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(8):2015-2026
The present paper reports on the use of copula functions to describe the distribution of discrete spatial data, e.g. count data from environmental mapping or areal data analysis. In particular, we consider approaches to parameter point estimation and propose a fast method to perform approximate spatial prediction in copula-based spatial models with discrete marginal distributions. We assess the goodness of the resulting parameter estimates and predictors under different spatial settings and guide the analyst on which approach to apply for the data at hand. Finally, we illustrate the methodology by analyzing the well-known Lansing Woods data set. Software that implements the methods proposed in this paper is freely available in Matlab language on the author’s website. 相似文献
2.
Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments 总被引:1,自引:0,他引:1 下载免费PDF全文
Rainfall data in continuous space provide an essential input for most hydrological and water resources planning studies. Spatial distribution of rainfall is usually estimated using ground‐based point rainfall data from sparsely positioned rain‐gauge stations in a rain‐gauge network. Kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall. The objective of this study is to evaluate three geostatistical (ordinary kriging [OK], ordinary cokriging [OCK], kriging with an external drift [KED]), and two deterministic (inverse distance weighting, radial basis function) interpolation methods for enhanced spatial interpolation of monthly rainfall in the Middle Yarra River catchment and the Ovens River catchment in Victoria, Australia. Historical rainfall records from existing rain‐gauge stations of the catchments during 1980–2012 period are used for the analysis. A digital elevation model of each catchment is used as the supplementary information in addition to rainfall for the OCK and kriging with an external drift methods. The prediction performance of the adopted interpolation methods is assessed through cross‐validation. Results indicate that the geostatistical methods outperform the deterministic methods for spatial interpolation of rainfall. Results also indicate that among the geostatistical methods, the OCK method is found to be the best interpolator for estimating spatial rainfall distribution in both the catchments with the lowest prediction error between the observed and estimated monthly rainfall. Thus, this study demonstrates that the use of elevation as an auxiliary variable in addition to rainfall data in the geostatistical framework can significantly enhance the estimation of rainfall over a catchment. 相似文献
3.
V. Demyanov S. Soltani M. Kanevski S. Canu M. Maignan E. Savelieva V. Timonin V. Pisarenko 《Stochastic Environmental Research and Risk Assessment (SERRA)》2001,15(1):18-32
This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical
prediction (kriging) is proposed. The method – wavelet analysis residual kriging (WARK) – is developed in order to assess
the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have
very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals
focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present
work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network
residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear
trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing
global statistical characteristics of the distribution and spatial correlation structure. 相似文献
4.
Estimating threshold-exceeding probability maps of environmental variables with Markov chain random fields 总被引:2,自引:2,他引:0
Weidong Li Chuanrong Zhang Dipak K. Dey Shanqin Wang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(8):1113-1126
Estimating and mapping spatial uncertainty of environmental variables is crucial for environmental evaluation and decision
making. For a continuous spatial variable, estimation of spatial uncertainty may be conducted in the form of estimating the
probability of (not) exceeding a threshold value. In this paper, we introduced a Markov chain geostatistical approach for
estimating threshold-exceeding probabilities. The differences of this approach compared to the conventional indicator approach
lie with its nonlinear estimators—Markov chain random field models and its incorporation of interclass dependencies through
transiograms. We estimated threshold-exceeding probability maps of clay layer thickness through simulation (i.e., using a
number of realizations simulated by Markov chain sequential simulation) and interpolation (i.e., direct conditional probability
estimation using only the indicator values of sample data), respectively. To evaluate the approach, we also estimated those
probability maps using sequential indicator simulation and indicator kriging interpolation. Our results show that (i) the
Markov chain approach provides an effective alternative for spatial uncertainty assessment of environmental spatial variables
and the probability maps from this approach are more reasonable than those from conventional indicator geostatistics, and
(ii) the probability maps estimated through sequential simulation are more realistic than those through interpolation because
the latter display some uneven transitions caused by spatial structures of the sample data. 相似文献
5.
Shen Liu Vo Anh James McGree Erhan Kozan Rodney C. Wolff 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(6):1679-1690
Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures. 相似文献
6.
Forecasting of space–time groundwater level is important for sparsely monitored regions. Time series analysis using soft computing tools is powerful in temporal data analysis. Classical geostatistical methods provide the best estimates of spatial data. In the present work a hybrid framework for space–time groundwater level forecasting is proposed by combining a soft computing tool and a geostatistical model. Three time series forecasting models: artificial neural network, least square support vector machine and genetic programming (GP), are individually combined with the geostatistical ordinary kriging model. The experimental variogram thus obtained fits a linear combination of a nugget effect model and a power model. The efficacy of the space–time models was decided on both visual interpretation (spatial maps) and calculated error statistics. It was found that the GP–kriging space–time model gave the most satisfactory results in terms of average absolute relative error, root mean square error, normalized mean bias error and normalized root mean square error. 相似文献
7.
J. van de Kassteele A. L. M. Dekkers A. Stein G. J. M. Velders 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(3):173-183
This paper discusses two model-based geostatistical methods for spatial interpolation of the number of days that ground level ozone exceeds a threshold level. The first method assumes counts to approximately follow a Poisson distribution, while the second method assumes a log-Normal distribution. First, these methods were compared using an extensive data set covering the Netherlands, Belgium and Germany. Second, the focus was placed on only the Netherlands, where only a small data set was used. Bayesian techniques were used for parameter estimation and interpolation. Parameter estimates are comparable due to the log-link in both models. Incorporating data from adjacent countries improves parameter estimation. The Poisson model predicts more accurately (maximum kriging standard deviation of 2.16 compared to 2.69) but shows smoother surfaces than the log-Normal model. The log-Normal approach ensures a better representation of the observations and gives more realistic patterns (an RMSE of 2.26 compared to 2.44). Model-based geostatistical procedures are useful to interpolate limited data sets of counts of ozone exceedance days. Spatial risk estimates using existing prior information can be made relating health effects to environmental thresholds. 相似文献
8.
Spatial sampling design and covariance-robust minimax prediction based on convex design ideas 总被引:2,自引:2,他引:0
Gunter Spöck Jürgen Pilz 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(3):463-482
This paper presents new ideas on sampling design and minimax prediction in a geostatistical model setting. Both presented
methodologies are based on regression design ideas. For this reason the appendix of this paper gives an introduction to optimum
Bayesian experimental design theory for linear regression models with uncorrelated errors. The presented methodologies and
algorithms are then applied to the spatial setting of correlated random fields. To be specific, in Sect. 1 we will approximate an isotropic random field by means of a regression model with a large number of regression functions
with random amplitudes, similarly to Fedorov and Flanagan (J Combat Inf Syst Sci: 23, 1997). These authors make use of the
Karhunen Loeve approximation of the isotropic random field. We use the so-called polar spectral approximation instead; i.e.
we approximate the isotropic random field by means of a regression model with sine-cosine-Bessel surface harmonics with random
amplitudes and then, in accordance with Fedorov and Flanagan (J Combat Inf Syst Sci: 23, 1997), apply standard Bayesian experimental
design algorithms to the resulting Bayesian regression model. Section 2 deals with minimax prediction when the covariance function is known to vary in some set of a priori plausible covariance
functions. Using a minimax theorem due to Sion (Pac J Math 8:171–176, 1958) we are able to formulate the minimax problem as
being equivalent to an optimum experimental design problem, too. This makes the whole experimental design apparatus available
for finding minimax kriging predictors. Furthermore some hints are given, how the approach to spatial sampling design with
one a priori fixed covariance function may be extended by means of minimax kriging to a whole set of a priori plausible covariance
functions such that the resulting designs are robust. The theoretical developments are illustrated with two examples taken
from radiological monitoring and soil science. 相似文献
9.
Xavier Emery 《Stochastic Environmental Research and Risk Assessment (SERRA)》2006,20(1-2):53-65
In the geostatistical analysis of regionalized data, the practitioner may not be interested in mapping the unsampled values
of the variable that has been monitored, but in assessing the risk that these values exceed or fall short of a regulatory
threshold. This kind of concern is part of the more general problem of estimating a transfer function of the variable under
study. In this paper, we focus on the multigaussian model, for which the regionalized variable can be represented (up to a
nonlinear transformation) by a Gaussian random field. Two cases are analyzed, depending on whether the mean of this Gaussian
field is considered known or not, which lead to the simple and ordinary multigaussian kriging estimators respectively. Although
both of these estimators are theoretically unbiased, the latter may be preferred to the former for practical applications
since it is robust to a misspecification of the mean value over the domain of interest and also to local fluctuations around
this mean value. An advantage of multigaussian kriging over other nonlinear geostatistical methods such as indicator and disjunctive
kriging is that it makes use of the multivariate distribution of the available data and does not produce order relation violations.
The use of expansions into Hermite polynomials provides three additional results: first, an expression of the multigaussian
kriging estimators in terms of series that can be calculated without numerical integration; second, an expression of the associated
estimation variances; third, the derivation of a disjunctive-type estimator that minimizes the variance of the error when
the mean is unknown. 相似文献
10.
Geostatistical estimation (kriging) and geostatistical simulation are routinely used in ground water hydrology for optimal spatial interpolation and Monte Carlo risk assessment, respectively. Both techniques are based on a model of spatial variability (semivariogram or covariance) that generally is not known but must be inferred from the experimental data. Where the number of experimental data is small (say, several tens), as is not unusual in ground water hydrology, the model fitted to the empirical semivariogram entails considerable uncertainty. If all the practical results are based on this unique fitted model, the final results will be biased. We propose that, instead of using a unique semivariogram model, the full range of models that are inside a given confidence region should be used, and the weight that each semivariogram model has on the final result should depend on its plausibility. The first task, then, is to evaluate the uncertainty of the model, which can be efficiently done by using maximum likelihood inference. The second task is to use the range of plausible models in applications and to show the effect observed on the final results. This procedure is put forth here with kriging and simulation applications, where the uncertainty in semivariogram parameters is propagated into the final results (e.g., the prediction of ground water head). A case study using log-transmissivity data from the Vega de Granada aquifer, in southern Spain, is given to illustrate the methodology. 相似文献
11.
Spatial prediction of river channel topography by kriging 总被引:2,自引:0,他引:2
Topographic information is fundamental to geomorphic inquiry, and spatial prediction of bed elevation from irregular survey data is an important component of many reach‐scale studies. Kriging is a geostatistical technique for obtaining these predictions along with measures of their reliability, and this paper outlines a specialized framework intended for application to river channels. Our modular approach includes an algorithm for transforming the coordinates of data and prediction locations to a channel‐centered coordinate system, several different methods of representing the trend component of topographic variation and search strategies that incorporate geomorphic information to determine which survey data are used to make a prediction at a specific location. For example, a relationship between curvature and the lateral position of maximum depth can be used to include cross‐sectional asymmetry in a two‐dimensional trend surface model, and topographic breaklines can be used to restrict which data are retained in a local neighborhood around each prediction location. Using survey data from a restored gravel‐bed river, we demonstrate how transformation to the channel‐centered coordinate system facilitates interpretation of the variogram, a statistical model of reach‐scale spatial structure used in kriging, and how the choice of a trend model affects the variogram of the residuals from that trend. Similarly, we show how decomposing kriging predictions into their trend and residual components can yield useful information on channel morphology. Cross‐validation analyses involving different data configurations and kriging variants indicate that kriging is quite robust and that survey density is the primary control on the accuracy of bed elevation predictions. The root mean‐square error of these predictions is directly proportional to the spacing between surveyed cross‐sections, even in a reconfigured channel with a relatively simple morphology; sophisticated methods of spatial prediction are no substitute for field data. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
12.
Geostatistical interpolation of object counts collected from multiple strip transects: Ordinary kriging versus finite domain kriging 总被引:2,自引:0,他引:2
Hirotaka Saito Sean A. McKenna D. A. Zimmerman Timothy C. Coburn 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(1):71-85
Data collected along transects are becoming more common in environmental studies as indirect measurement devices, such as geophysical sensors, that can be attached to mobile platforms become more prevalent. Because exhaustive sampling is not always possible under constraints of time and costs, geostatistical interpolation techniques are used to estimate unknown values at unsampled locations from transect data. It is known that outlying observations can receive significantly greater ordinary kriging weights than centrally located observations when the data are contiguously aligned along a transect within a finite search window. Deutsch (1994) proposed a kriging algorithm, finite domain kriging, that uses a redundancy measure in place of the covariance function in the data-to-data kriging matrix to address the problem of overweighting the outlying observations. This paper compares the performances of two kriging techniques, ordinary kriging (OK) and finite domain kriging (FDK), on examining unexploded ordnance (UXO) densities by comparing prediction errors at unsampled locations. The impact of sampling design on object count prediction is also investigated using data collected from transects and at random locations. The Poisson process is used to model the spatial distribution of UXO for three 5000 × 5000 m fields; one of which does not have any ordnance target (homogeneous field), while the other two sites have an ordnance target in the center of the site (isotropic and anisotropic fields). In general, for a given sampling transects width, the differences between OK and FDK in terms of the mean error and the mean square error are not significant regardless of the sampled area and the choice of the field. When 20% or more of the site is sampled, the estimation of object counts is unbiased on average for all three fields regardless of the choice of the transect width and the choice of the kriging algorithm. However, for non-homogeneous fields (isotropic and anisotropic fields), the mean error fluctuates considerably when a small number of transects are sampled. The difference between the transect sampling and the random sampling in terms of prediction errors becomes almost negligible if more than 20% of the site is sampled. Overall, FDK is no better than OK in terms of the prediction performances when the transect sampling procedure is used. 相似文献
13.
Optimal design of rain gauge network in the Middle Yarra River catchment,Australia 总被引:1,自引:0,他引:1 下载免费PDF全文
Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A well‐designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging‐based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd. 相似文献
14.
Jürgen Pilz Gunter Spöck 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(5):621-632
The spatial prediction methodology that has become known under the heading of kriging is largely based on the assumptions
that the underlying random field is Gaussian and the covariance function is exactly known. In practical applications, however,
these assumptions will not hold. Beyond Gaussianity of the random field, lognormal kriging, disjunctive kriging, (generalized
linear) model-based kriging and trans-Gaussian kriging have been proposed in the literature. The latter approach makes use
of the Box–Cox-transform of the data. Still, all the alternatives mentioned do not take into account the uncertainty with
respect to the distribution (or transformation) and the estimated covariance function of the data. The Bayesian trans-Gaussian
kriging methodology proposed in the present paper is in the spirit of the “Bayesian bootstrap” idea advocated by Rubin (Ann
Stat 9:130–134, 1981) and avoids the unusual specification of noninformative priors often made in the literature and is entirely based on the
sample distribution of the estimators of the covariance function and of the Box–Cox parameter. After some notes on Bayesian
spatial prediction, noninformative priors and developing our new methodology finally we will present an example illustrating
our pragmatic approach to Bayesian prediction by means of a simulated data set. 相似文献
15.
An application of Spartan spatial random fields in environmental mapping: focus on automatic mapping capabilities 总被引:4,自引:2,他引:2
Samuel N. Elogne Dionissios T. Hristopulos Emmanouil Varouchakis 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(5):633-646
This paper investigates the potential of Spartan spatial random fields (SSRFs) in real-time mapping applications. The data
set that we study focuses on the distribution of daily gamma dose rates over part of Germany. Our goal is to determine a Spartan
spatial model from the data, and then use it to generate “predictive” maps of the radioactivity. In the SSRF framework, the
spatial dependence is determined from sample functions that focus on short-range correlations. A recently formulated SSRF
predictor is used to derive isolevel contour maps of the dose rates. The SSRF predictor is explicit. Moreover, the adjustments that it requires by the user are reduced compared to classical geostatistical methods. These features
present clear advantages for an automatic mapping system. The performance of the SSRF predictor is evaluated by means of various
cross-validation measures. The values of the performance measures are similar to those obtained by classical geostatistical
methods. Application of the SSRF method to data that simulate a radioactivity release scenario is also discussed. Hot spots
are detected and removed using a heuristic method. The extreme values that appear in the path of the simulated plume are not
captured by the currently used Spartan spatial model. Modeling of the processes leading to extreme values can enhance the
predictive capabilities of the spatial model, by incorporating physical information. 相似文献
16.
Abstract This paper compares the performance of three geostatistical algorithms, which integrate elevation as an auxiliary variable: kriging with external drift (KED); kriging combined with regression, called regression kriging (RK) or kriging after detrending; and co-kriging (CK). These three methods differ by the way by in which the secondary information is introduced into the prediction procedure. They are applied to improve the prediction of the monthly average rainfall observations measured at 106 climatic stations in Tunisia over an area of 164 150 km2 using the elevation as the auxiliary variable. The experimental sample semivariograms, residual semivariograms and cross-variograms are constructed and fitted to estimate the rainfall levels and the estimation variance at the nodes of a square grid of 20 km?×?20 km resolution and to develop corresponding contour maps. Contour diagrams for KED and RK were similar and exhibited a pattern corresponding more closely to local topographic features when (a) the network is sparse and (b) the rainfall–elevation correlation is poor, while CK showed a smooth zonal pattern. Smaller prediction variances are obtained for the RK algorithm. The cross-validation showed that the RMSE obtained for CK gave better results than for KED or RK. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Feki, H., Slimani, M., and Cudennec, C., 2012. Incorporating elevation in rainfall interpolation in Tunisia using geostatistical methods. Hydrological Sciences Journal, 57 (7), 1294–1314. 相似文献
17.
This study uses elliptical copulas and transition probabilities for uncertainty modeling of categorical spatial data. It begins by discussing the expressions of the cumulative distribution function and probability density function of two major elliptical copulas: Gaussian copula and t copula. The basic form of spatial copula discriminant function is then derived based on Bayes’ theorem, which consists of three parts: the prior probability, the conditional marginal densities, and the conditional copula density. Finally, three kinds of parameter estimation methods are discussed, including maximum likelihood estimation, inference functions for margins and canonical maximum likelihood (CML). To avoid making assumptions on the form of marginal distributions, the CML approach is adopted in the real-world case study. Results show that the occurrence probability maps generated by these two elliptical copulas are similar to each other. However, the prediction map interpolated by Gaussian copula has a relatively higher classification accuracy than t copula. 相似文献
18.
Abstract The present research study investigates the application of nonlinear normalizing data transformations in conjunction with ordinary kriging (OK) for the accurate prediction of groundwater level spatial variability in a sparsely-gauged basin. We investigate three established normalizing methods, Gaussian anamorphosis, trans-Gaussian kriging and the Box-Cox method to improve the estimation accuracy. The first two are applied for the first time to groundwater level data. All three methods improve the mean absolute prediction error compared to the application of OK to the non-transformed data. In addition, a modified Box-Cox transformation is proposed and applied to normalize the hydraulic heads. The modified Box-Cox transformation in conjunction with OK is found to be the optimal spatial model based on leave-one-out cross-validation. The recently established Spartan semivariogram family provides the optimal model fit to the transformed data. Finally, we present maps of the groundwater level and the kriging variance based on the optimal spatial model. Editor D. Koutsoyiannis; Associate editor A. Montanari Citation Varouchakis, E.A., Hristopoulos, D.T., and Karatzas, G.P., 2012. Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application. Hydrological Sciences Journal, 57 (7), 1404–1419. 相似文献
19.
Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation 总被引:1,自引:0,他引:1
Ming Li Quanxi Shao Luigi Renzullo 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(1):117-130
Great emphasis is being placed on the use of rainfall intensity data at short time intervals to accurately model the dynamics
of modern cropping systems, runoff, erosion and pollutant transport. However, rainfall data are often readily available at
more aggregated level of time scale and measurements of rainfall intensity at higher resolution are available only at limited
stations. A distribution approach is a good compromise between fine-scale (e.g. sub-daily) models and coarse-scale (e.g. daily)
rainfall data, because the use of rainfall intensity distribution could substantially improve hydrological models. In the
distribution approach, the cumulative distribution function of rainfall intensity is employed to represent the effect of the
within-day temporal variability of rainfall and a disaggregation model (i.e. a model disaggregates time series into sets of
higher solution) is used to estimate distribution parameters from the daily average effective precipitation. Scaling problems
in hydrologic applications often occur at both space and time dimensions and temporal scaling effects on hydrologic responses
may exhibit great spatial variability. Transferring disaggregation model parameter values from one station to an arbitrary
position is prone to error, thus a satisfactory alternative is to employ spatial interpolation between stations. This study
investigates the spatial interpolation of the probability-based disaggregation model. Rainfall intensity observations are
represented as a two-parameter lognormal distribution and methods are developed to estimate distribution parameters from either
high-resolution rainfall data or coarse-scale precipitation information such as effective intensity rates. Model parameters
are spatially interpolated by kriging to obtain the rainfall intensity distribution when only daily totals are available.
The method was applied to 56 pluviometer stations in Western Australia. Two goodness-of-fit statistics were used to evaluate
the skill—daily and quantile coefficient of efficiency between simulations and observations. Simulations based on cross-validation
show that kriging performed better than other two spatial interpolation approaches (B-splines and thin-plate splines). 相似文献
20.
The ordinary kriging method, a geostatistical interpolation technique, was applied for developing contour maps of design storm depth in northern Taiwan using intensity–duration–frequency (IDF) data. Results of variogram modelling on design storm depths indicate that the design storms can be categorized into two distinct storm types: (i) storms of short duration and high spatial variation and (ii) storms of long duration and less spatial variation. For storms of the first category, the influence range of rainfall depth decreases when the recurrence interval increases, owing to the increasing degree of their spatial independence. However, for storms of the second category, the influence range of rainfall depth does not change significantly and has an average of approximately 72 km. For very extreme events, such as events of short duration and long recurrence interval, we do not recommend usage of the established design storm contours, because most of the interstation distances exceed the influence ranges. Our study concludes that the influence range of the design storm depth is dependent on the design duration and recurrence interval and is a key factor in developing design storm contours. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献