A geostatistical approach for areal rainfall statistics assessment

Areal rainfall statistics are more relevant in flood hydrology and water resources management than point rainfall statistics when it comes to help designing dams or hydraulic structures. This paper presents a geostatistically based method to derive the areal statistics from point statistics. Assuming that the distribution models of point rainfall and areal belong to the same class of models and that the rainfall process is stationary, it is shown how the parameters of the areal distribution model can directly be computed from the parameters of the point distribution models in case of a non stationary process, an approximation is derived that yielded good results when applied to a mountainous region in Southern France. The method also allows the computation of the areal reduction factors in a very general form.     

A geostatistical approach for areal rainfall statistics assessment

Areal rainfall statistics are more relevant in flood hydrology and water resources management than point rainfall statistics when it comes to help designing dams or hydraulic structures. This paper presents a geostatistically based method to derive the areal statistics from point statistics. Assuming that the distribution models of point rainfall and areal belong to the same class of models and that the rainfall process is stationary, it is shown how the parameters of the areal distribution model can directly be computed from the parameters of the point distribution models in case of a non stationary process, an approximation is derived that yielded good results when applied to a mountainous region in Southern France. The method also allows the computation of the areal reduction factors in a very general form.     

黑土坡耕地土壤有机质空间变异及其与土壤侵蚀的关系——以黑龙江省鹤山农场为例

选取黑龙江省鹤山农场面积为0.91 km²的典型黑土区的坡耕地作为研究样地。按横纵100 m间隔共采集101个样点,运用地理信息系统和地统计学相结合的方法研究分析0~15 cm土层有机质空间变异及其与土壤侵蚀的关系。结果表明：位于典型黑土区样地的有机质含量集中在3%~5%范围内,均值为4.13%,高于黑龙江省的有机质平均水平。有机质含量空间变异明显,且主要受土壤侵蚀的影响：高侵蚀区对应低有机质区,中度侵蚀区对应中等有机质区,沉积区对应高有机质区。顺坡种植平均坡度2.2°时,每侵蚀1 000 t/km²土壤,有机质含量降低0.8%。土壤有机质空间变异可采用球状模型表达,自相关明显,进一步表明土壤侵蚀导致的再分布。对比分析确定200 m采样间距能够能准确表达该区表层有机质含量的空间特征,为精准施肥提供了采样依据。     

Spatial analysis of land use and shallow groundwater vulnerability in the watershed adjacent to Assateague Island National Seashore,Maryland and Virginia,USA

Spatial relations between land use and groundwater quality in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA were analyzed by the use of two spatial models. One model used a logit analysis and the other was based on geostatistics. The models were developed and compared on the basis of existing concentrations of nitrate as nitrogen in samples from 529 domestic wells. The models were applied to produce spatial probability maps that show areas in the watershed where concentrations of nitrate in groundwater are likely to exceed a predetermined management threshold value. Maps of the watershed generated by logistic regression and probability kriging analysis showing where the probability of nitrate concentrations would exceed 3 mg/L (>0.50) compared favorably. Logistic regression was less dependent on the spatial distribution of sampled wells, and identified an additional high probability area within the watershed that was missed by probability kriging. The spatial probability maps could be used to determine the natural or anthropogenic factors that best explain the occurrence and distribution of elevated concentrations of nitrate (or other constituents) in shallow groundwater. This information can be used by local land-use planners, ecologists, and managers to protect water supplies and identify land-use planning solutions and monitoring programs in vulnerable areas.     

Optimizing a piezometric network in the estimation of the groundwater budget: a case study from a crystalline-rock watershed in southern India

An estimate of the groundwater budget at the catchment scale is extremely important for the sustainable management of available water resources. Water resources are generally subjected to over-exploitation for agricultural and domestic purposes in agrarian economies like India. The double water-table fluctuation method is a reliable method for calculating the water budget in semi-arid crystalline rock areas. Extensive measurements of water levels from a dense network before and after the monsoon rainfall were made in a 53 km² watershed in southern India and various components of the water balance were then calculated. Later, water level data underwent geostatistical analyses to determine the priority and/or redundancy of each measurement point using a cross-validation method. An optimal network evolved from these analyses. The network was then used in re-calculation of the water-balance components. It was established that such an optimized network provides far fewer measurement points without considerably changing the conclusions regarding groundwater budget. This exercise is helpful in reducing the time and expenditure involved in exhaustive piezometric surveys and also in determining the water budget for large watersheds (watersheds greater than 50 km²).     

Conditional Simulation with Patterns

An entirely new approach to stochastic simulation is proposed through the direct simulation of patterns. Unlike pixel-based (single grid cells) or object-based stochastic simulation, pattern-based simulation simulates by pasting patterns directly onto the simulation grid. A pattern is a multi-pixel configuration identifying a meaningful entity (a puzzle piece) of the underlying spatial continuity. The methodology relies on the use of a training image from which the pattern set (database) is extracted. The use of training images is not new. The concept of a training image is extensively used in simulating Markov random fields or for sequentially simulating structures using multiple-point statistics. Both these approaches rely on extracting statistics from the training image, then reproducing these statistics in multiple stochastic realizations, at the same time conditioning to any available data. The proposed approach does not rely, explicitly, on either a statistical or probabilistic methodology. Instead, a sequential simulation method is proposed that borrows heavily from the pattern recognition literature and simulates by pasting at each visited location along a random path a pattern that is compatible with the available local data and any previously simulated patterns. This paper discusses the various implementation details to accomplish this idea. Several 2D illustrative as well as realistic and complex 3D examples are presented to showcase the versatility of the proposed algorithm.     

Hydrogeological modeling of radionuclide transport in low permeability media: a comparison between Boom Clay and Ypresian Clay

Deep low-permeability clay layers are considered as suitable environments for disposal of high-level radioactive waste. In Belgium, the Boom Clay is the reference host formation and the Ypresian Clay an alternative host formation for research and safety and feasibility assessment of deep disposal of nuclear waste. In this study, two hydrogeological models are built to calculate the radionuclide fluxes that would migrate from a potential repository through these two clay formations. Transport parameter heterogeneity is incorporated in the models using geostatistical co-simulations of hydraulic conductivity, diffusion coefficient and diffusion accessible porosity. The calculated radionuclide fluxes in the two clay formations are compared. The results show that in the Ypresian Clay larger differences between the fluxes through the lower and the upper clay boundary occur, larger total output radionuclide amounts are calculated and a larger effect of parameter heterogeneity on the calculated fluxes is observed, compared to the Boom Clay.     

Optimization tools and simulation methods for designing and evaluating a mining operation

Some of the major advances in the field of mining in the last three decades have referred to the development of new design and planning techniques for optimizing open-pit mining and the inclusion of a stochastic perspective in economic models that is more revealing than a purely deterministic perspective. These advances include the use of parametric techniques in the design and planning process, the formulation of criteria for establishing an optimum cut-off grade policy when the economic goal is to optimize net present value (NPV), and the introduction of economic risk analysis. This paper examines some of the difficulties involved in applying these techniques—arising largely as a result of a lack of knowledge of the spatial location and distribution of the deposit grades—and analyses how these difficulties can be tackled with the help of geostatistical simulation techniques that take probabilistic criteria into consideration during the optimization process. These techniques enable equally likely representations of the deposit to be obtained that reproduce the main dispersion features for the starting experimental data (covariance or variogram, as well as the histogram). Consequently, the uncertainty in regard to the deposit as well as its influence on the economic assessment of the deposit in risk terms can be evaluated. This paper also describes a simple method for introducing price and cost increases into the risk analysis via the Monte Carlo method and shows how geological, technical and economic uncertainty can be integrated in risk analyses. Although it is true that the relationship between prices and costs is maintained constant in mining planning based on using parametric techniques, it is no less true that the risk analysis requires the use of models in which the main parameters with a bearing on deposit economics are considered as stochastic variables. The proposed methodology simplifies the calculations and easily integrates the different sources of uncertainty.

基于地质统计学影像纹理的海南沙漠化监测研究

海南省东部地区在沙漠化监测上呈现两大难点:一是海滩沙地与沙漠化土地在影像上呈现近乎相同的光谱特征,基于传统的遥感影像光谱分类方法无法得到实际的沙漠化面积;二是该地区属于热带沿海地区,常规的监测指标体系与实际情况相去甚远,必须寻求其他的手段来进行沙漠化程度的分级。基于不同沙地类型在地表空间结构上的差异,本文提出将地质统计学纹理方法应用到沙漠化监测中,通过变异函数纹理来加大各种不同类别沙地间的区别,提高样本选择的分离度。结果表明,运用变异函数纹理结合光谱波段的最大似然分类方法能够很好地界定海滩沙地和沙漠化土地的不同等级,依据分类结果计算得到的沙漠化土地面积与统计数据吻合较好,总精度达到92.4%,证明了地质统计学纹理在实现该地区遥感沙漠化监测方面的有效性,同时也为其他地区沙漠化监测找到一个可资借鉴的方法。     

利用 GIS地理统计模块预测海南岛植被指数季节性变化趋势

虽然采用遥感图像提取的植被指数在空间上能较好的反映作物的状况,但其不能预测植被指数在空间上的变化范围,如果能从整体上了解不同市县在不同季节的平均植被指数值,就可以对该区域整体植被状态进行量化分析,也就可以从大范围内进行植被指数的预测分析.利用地理信息系统(GIS)和地统计学相结合的地理统计分析模块(ArcGIS Geostatistical Analyst),根据MODIS遥感数据提取的每季度不同市县平均NDVI植被指数,采用Kriging插值的方法分析了海南岛归一化植被指数(NDVI)季节性变化趋势,并