Geostatistical analysis of soil moisture measurements and remotely sensed data at different spatial scales

The European remote sensing satellite (ERS-2) synthetic aperture radar (SAR) data was used for temporal monitoring of soil moisture at Sukhothai, Thailand. Higher correlations were found between the observed soil moisture and the radar backscattering coefficient. The soil moisture distribution shows great variation in space and time due to its stochastic nature. In order to obtain a better understanding of the nature and causes of spatial variation of soil moisture, the extensive soil moisture measurements observed in Thailand and also remotely sensed ERS-2 SAR data were used for geostatistical analysis. The observed soil moisture shows seasonal variations with mean varying from 3.33 %v/v (dry season) to 33.44 %v/v (wet season). The spatial geostatistical structure also shows clear seasonal variations in the geostatistical characteristics such as range and sill. The sills vary from 1.00 (%v/v)² for the driest day to 107.57 (%v/v)² for one of the wet days. The range or the correlation lengths varies between 46.5 and 149.8 m for the wettest and driest periods. The nugget effect does not show strong seasonal pattern or trend but the dry periods usually have a smaller nugget effect than the wet periods. The spherical variogram model fits the sample variograms very well in the case of soil moisture observations while the exponential model fits those of the remotely sensed data. The ranges observed from the observed soil moisture data and remotely sensed data at the same resolution are very similar. Resolution degradation affects the geostatistical structure of the data by reducing the sills, and increasing the ranges.     

Application of multivariate spatial analysis in scale-based distribution and source study of PAHs in the topsoil: an example from Tianjin,China

Polycyclic aromatic hydrocarbons (PAHs) in soil originate from various sources under different spatial scales. Coregionalization analysis is more revealing than univariate geostatistical analysis. Scale-dependent spatial features of variables reflect different sources of spatial variability. In this study, 188 topsoil samples in the Tianjin area were collected. The contents of 16 PAHs and soil background properties were determined for all samples. A multivariate geostatistical approach was used for multi-scale spatial analysis for PAH compounds. Results show that coal combustion was the major source for the spatial distribution patterns of PAHs in the topsoil of the studied area. It worked mainly at the short-range scale (5–10 km). Significant spatial variation patterns were identified. In contrast, no significant spatial distribution trends at the nugget (0–5 km) or long-range scales (10–50 km) were seen. Long-range transport and site contamination of PAHs might not be key contributors in forming the distribution pattern of PAHs in the topsoil of Tianjin area.     

岩溶区土壤有机质空间变异性分析

采用GIS和地统计学研究土壤有机质（SOM）的空间分布、影响因素和预测是指导农业生产、环境治理和土壤碳储计量的重要手段。基于广西马山县北部岩溶区表层土壤 (0~20 cm)的441个SOM数据,建立普通克里格(OK)、回归克里格（RK）,以及结合辅助变量的地理加权回归克里格(GWRK)、残差均值（MM_OK）和中值（MC_OK）均一化克里格的5种模型,并比较其预测精度,旨在探讨岩溶区SOM制图中地统计学方法的适用性。结果表明：（1）SOM的变异系数为37.30%,属于中等空间变异;（2）岩溶区SOM空间变异受土地利用方式、土壤类型和地形因子等因素共同影响,SOM高值区分布在西北部、西部和东部等石灰土分布的岩溶区和水田,低值区位于北部红水河沿岸的冲积土地带;（3）RK、GWRK、MM_OK和 MC_OK对SOM解释能力均较优,可用于岩溶区SOM预测制图。结合辅助变量因子的GWRK预测模型能有效消除空间变异因素的影响,克服岩溶区SOM含量的空间非平稳性,从而提高SOM含量模型的稳定性和精度,同时MC_OK模型能提高预测的准确度。     

Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms

The optimal selection of monitoring wells is a major task in designing an information-effective groundwater quality monitoring network which can provide sufficient and not redundant information of monitoring variables for delineating spatial distribution or variations of monitoring variables. This study develops a design approach for an optimal multivariate geostatistical groundwater quality network by proposing a network system to identify groundwater quality spatial variations by using factorial kriging with genetic algorithm. The proposed approach is applied in designing a groundwater quality monitoring network for nine variables (EC, TDS, Cl⁻, Na, Ca, Mg, SO ₄ ²⁻ , Mn and Fe) in the Pingtung Plain in Taiwan. The spatial structure results show that the variograms and cross-variograms of the nine variables can be modeled in two spatial structures: a Gaussian model with ranges 28.5 km and a spherical model with 40 km for short and long spatial scale variations, respectively. Moreover, the nine variables can be grouped into two major components for both short and long scales. The proposed optimal monitoring design model successfully obtains different optimal network systems for delineating spatial variations of the nine groundwater quality variables by using 20, 25 and 30 monitoring wells in both short scale (28.5 km) and long scale (40 km). Finally, the study confirms that the proposed model can design an optimal groundwater monitoring network that not only considers multiple groundwater quality variables but also monitors variations of monitoring variables at various spatial scales in the study area.     

Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation

Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making. One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable, joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km² (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio (alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision of K value predictions.     

我国褐煤资源及其利用前景

煤层厚度与煤质特征(包括发热量、挥发分、灰分与硫分)的变异性决定了煤炭价格的空间分布,准确表征煤层经济价值对于煤炭资源的合理开发利用有着重要作用。首先明确煤厚煤质等地质属性为区域化变量,使用块体模型的方法对研究区域地质实体进行离散化;其次利用地质统计学方法获取实测钻孔煤厚煤质的实验半变异函数,当变异性较小、实测数据丰度较大、能建立变程内实验半变异函数的数学模型时,使用普通克里金进行空间估值,反之则考虑使用距离幂次反比法进行空间估值,并使用交叉验证获取均方误差最小的幂次;然后使用动力煤计价方法计算每个块体单元上的煤炭价格,建立煤层价值块体模型;最后以准格尔煤田麻地梁煤矿5号煤层为例,考虑煤层价值模型对工作面开采接替顺序进行优化。结果表明：煤炭价格呈现出明显的空间异质性,变化范围从574元/t至1192元/t,平均834元/t,符合正态分布;工作面开采接替顺序优化后煤炭销售额净现值NPV (Net Present Value)增加4.16亿元,提升1.64%,显著提高了采矿收益。使用地质统计学方法可充分挖掘实测数据的空间相关性,建立的块体模型不仅获得煤厚、煤质与煤炭价格在宏观上的统计信息,还可以给出其精细化的空间分布,在后续的选煤、配煤及销售环节具有广泛的应用前景;使用地质统计学方法建立的块体模型精度受控于实测数据规模,可根据开采过程中的新生数据进行动态修正与预测。

     

The distribution of soil erosion as a function of slope aspect and parent material in Ciskei,Southern Africa

The paper investigates the relationship between geology, soils and slope aspects in four Ciskei catchments. The primary aim of the study was to determine whether any simple bivariate relationship exists between these independent variables and soil erosion. The secondary aim of the study was to determine whether combining the three independent variables and using a multivariate analysis could produce a more lucid picture of spatial variations in soil erosion. The results obtained from the study show that all the three independent variables (geology, soil type and slope aspects) can be used to account for spatial variations in soil erosion in the study catchments. A parent material classification (based on geology and soil type) and a parent material plus slope aspect classification provide more detailed explanation for variations in the extent of soil erosion in the study area. The paper discusses possible reasons for the observed relationships and the need for more detailed multivariate investigations.     

Application of geostatistical methods in gold geochemical anomalies identification (Montemor-O-Novo, Portugal)

The study described herein concerns the application of geostatistical methods to data soil from Montemor-O-Novo area (Southern Portugal). In the area, the gold mineralised zones (Banhos, Caeiras, Falés, Gamela, Malaca and Monfurado) are characterised by different geological settings and mineralogical assemblages. A total of 1211 soil samples were collected in Montemor-O-Novo area and analysed for Cu, Pb, Zn, As, Ba and Au by atomic absorption spectrometry.To account for spatial structure, simple and cross variograms were computed for the main directions of the grid sampling. From the experimental variograms a linear model of coregionalization composed of three structures, a nugget effect and two anisotropic spherical structures, was fitted to each of the six variables. The coregionalization matrices deduced from the theoretical model show the relationships between the variables at different scales. These matrices were compared with those obtained by principal component analysis (PCA).This methodology was the basis for estimating the corresponding spatial components (Y0, Y1 and Y2) using factorial kriging analysis (FKA). Maps of raw data, Y0, Y1 and Y2 were made for each variable.The use of multivariate analysis permit the study of the spatial structure intrinsic to geochemical data and the identification and refinement of significant anomalies related to Au-bearing mineral deposits.     

Geostatistical Simulation of Regionalized Pore-Size Distributions Using Min/Max Autocorrelation Factors

In many fields of the Earth Sciences, one is interested in the distribution of particle or void sizes within samples. Like many other geological attributes, size distributions exhibit spatial variability, and it is convenient to view them within a geostatistical framework, as regionalized functions or curves. Since they rarely conform to simple parametric models, size distributions are best characterized using their raw spectrum as determined experimentally in the form of a series of abundance measures corresponding to a series of discrete size classes. However, the number of classes may be large and the class abundances may be highly cross-correlated. In order to model the spatial variations of discretized size distributions using current geostatistical simulation methods, it is necessary to reduce the number of variables considered and to render them uncorrelated among one another. This is achieved using a principal components-based approach known as Min/Max Autocorrelation Factors (MAF). For a two-structure linear model of coregionalization, the approach has the attractive feature of producing orthogonal factors ranked in order of increasing spatial correlation. Factors consisting largely of noise and exhibiting pure nugget–effect correlation structures are isolated in the lower rankings, and these need not be simulated. The factors to be simulated are those capturing most of the spatial correlation in the data, and they are isolated in the highest rankings. Following a review of MAF theory, the approach is applied to the modeling of pore-size distributions in partially welded tuff. Results of the case study confirm the usefulness of the MAF approach for the simulation of large numbers of coregionalized variables.     

Joint Consistent Mapping of High-Dimensional Geochemical Surveys

Geochemical surveys often contain several tens of components, obtained from different horizons and with different analytical techniques. These are used either to obtain elemental concentration maps or to explore links between the variables. The first task involves interpolation, the second task principal component analysis (PCA) or a related technique. Interpolation of all geochemical variables (in wt% or ppm) should guarantee consistent results: At any location, all variables must be positive and sum up to 100 %. This is not ensured by any conventional geostatistical technique. Moreover, the maps should ideally preserve any link present in the data. PCA also presents some problems, derived from the spatial dependence between the observations, and the compositional nature of the data. Log-ratio geostatistical techniques offer a consistent solution to all these problems. Variation-variograms are introduced to capture the spatial dependence structure: These are direct variograms of all possible log ratios of two components. They can be modeled with a function analogous to the linear model of coregionalization (LMC), where for each spatial structure there is an associated variation matrix describing the links between the components. Eigenvalue decompositions of these matrices provide a PCA of that particular spatial scale. The whole data set can then be interpolated by cokriging. Factorial cokriging can also be used to map a certain spatial structure, eventually projected onto those principal components (PCs) of that structure with relevant contribution to the spatial variability. If only one PC is used for a certain structure, the maps obtained represent the spatial variability of a geochemical link between the variables. These procedures and their advantages are illustrated with the horizon C Kola data set, with 25 components and 605 samples covering most of the Kola peninsula (Finland, Norway, Russia).     

Mapping and assessment-based modeling of soil fertility differences in the central and eastern parts of the Black Sea region using GIS and geostatistical approaches

This study was carried out on arable lands of the central and eastern Black Sea regions including eight provinces (Artvin, Giresun, Gümü?hane, Ordu, Rize, Samsun, Sinop, and Trabzon). The present study aims to generate a soil fertility map for agricultural lands in the central and eastern parts of the Black Sea region. The main objective of this research is to quantify soil fertility by developing a soil fertility index (SFI) model at the regional level. The related objectives were to map the spatial distribution of soil fertility by using auxiliary variables and to model soil fertility within the study region. To accomplish this, a data set for soil fertility differences was collected and a model was developed to predict the spatial distribution of differences across the region. The study area was divided into 2.5 × 2.5-km grid squares. A total of 3400 soil samples were collected from the surface (0–20 cm) of each grid intersection point. The geostatistical method was used to generate the SFI distribution map of the study area for surface soils. Of the total study area, 93.76% had good (S1) or moderately fertile (S2) soil while 6.15% of the area had marginally fertile (S3) soil. Only a very small area (N) had low-fertility soil.     

Assessment of the spatio-temporal distribution of soil properties in East Kolkata wetland ecosystem (A Ramsar site: 1208)

The present investigation was made to characterize spatial and temporal variations in soil properties and to evaluate possible differences that could be dependent on the tannery effluent discharges, municipal sewage discharges, vegetation cover, soil settlement rate, crop rotation, etc. Soil total organic matter (TOM), cations like, Sodium (Na), Ammonium (NH₄), Potassium (K), Calcium (Ca) and Magnesium (Mg) contents in the bank soils and bottom sediments were recorded from seven different characteristic sites in East Kolkata wetland ecosystem, a Ramsar site (Ramsar site No. 1208). The profile maps were constructed by geostatistical methods to describe the spatial distribution as well as temporal variations of all the factors to identify the influences of composite wastewaters. The work was initiated to identify causes and consequences of the waste dumping in the concerned region for the past hundred years and thereby to suggest necessary precautionary measures to prevent further loss of soil quality.     

Conditional Latin Hypercube Simulation of (Log)Gaussian Random Fields

In earth and environmental sciences applications, uncertainty analysis regarding the outputs of models whose parameters are spatially varying (or spatially distributed) is often performed in a Monte Carlo framework. In this context, alternative realizations of the spatial distribution of model inputs, typically conditioned to reproduce attribute values at locations where measurements are obtained, are generated via geostatistical simulation using simple random (SR) sampling. The environmental model under consideration is then evaluated using each of these realizations as a plausible input, in order to construct a distribution of plausible model outputs for uncertainty analysis purposes. In hydrogeological investigations, for example, conditional simulations of saturated hydraulic conductivity are used as input to physically-based simulators of flow and transport to evaluate the associated uncertainty in the spatial distribution of solute concentration. Realistic uncertainty analysis via SR sampling, however, requires a large number of simulated attribute realizations for the model inputs in order to yield a representative distribution of model outputs; this often hinders the application of uncertainty analysis due to the computational expense of evaluating complex environmental models. Stratified sampling methods, including variants of Latin hypercube sampling, constitute more efficient sampling aternatives, often resulting in a more representative distribution of model outputs (e.g., solute concentration) with fewer model input realizations (e.g., hydraulic conductivity), thus reducing the computational cost of uncertainty analysis. The application of stratified and Latin hypercube sampling in a geostatistical simulation context, however, is not widespread, and, apart from a few exceptions, has been limited to the unconditional simulation case. This paper proposes methodological modifications for adopting existing methods for stratified sampling (including Latin hypercube sampling), employed to date in an unconditional geostatistical simulation context, for the purpose of efficient conditional simulation of Gaussian random fields. The proposed conditional simulation methods are compared to traditional geostatistical simulation, based on SR sampling, in the context of a hydrogeological flow and transport model via a synthetic case study. The results indicate that stratified sampling methods (including Latin hypercube sampling) are more efficient than SR, overall reproducing to a similar extent statistics of the conductivity (and subsequently concentration) fields, yet with smaller sampling variability. These findings suggest that the proposed efficient conditional sampling methods could contribute to the wider application of uncertainty analysis in spatially distributed environmental models using geostatistical simulation.     

Representing Spatial Uncertainty Using Distances and Kernels

Assessing uncertainty of a spatial phenomenon requires the analysis of a large number of parameters which must be processed by a transfer function. To capture the possibly of a wide range of uncertainty in the transfer function response, a large set of geostatistical model realizations needs to be processed. Stochastic spatial simulation can rapidly provide multiple, equally probable realizations. However, since the transfer function is often computationally demanding, only a small number of models can be evaluated in practice, and are usually selected through a ranking procedure. Traditional ranking techniques for selection of probabilistic ranges of response (P10, P50 and P90) are highly dependent on the static property used. In this paper, we propose to parameterize the spatial uncertainty represented by a large set of geostatistical realizations through a distance function measuring “dissimilarity” between any two geostatistical realizations. The distance function allows a mapping of the space of uncertainty. The distance can be tailored to the particular problem. The multi-dimensional space of uncertainty can be modeled using kernel techniques, such as kernel principal component analysis (KPCA) or kernel clustering. These tools allow for the selection of a subset of representative realizations containing similar properties to the larger set. Without losing accuracy, decisions and strategies can then be performed applying a transfer function on the subset without the need to exhaustively evaluate each realization. This method is applied to a synthetic oil reservoir, where spatial uncertainty of channel facies is modeled through multiple realizations generated using a multi-point geostatistical algorithm and several training images.     

Modelling seasonal variations of natural radioactivity in soils: A case study in southern Italy

The activity of natural radionuclides in soil has become an environmental concern for local public and national authorities because of the harmful effects of radiation exposure on human health. In this context, modelling and mapping the activity of natural radionuclides in soil is an important research topic. The study was aimed to model, in a spatial sense, the soil radioactivity in an urban and peri-urban soils area in southern Italy to analyse the seasonal influence on soil radioactivity. Measures of gamma radiation naturally emitted through the decay of radioactive isotopes (potassium, uranium and thorium) were analysed using a geostatistical approach to map the spatial distribution of soil radioactivity. The activity of three radionuclides was measured at 181 locations using a high-resolution ?-ray spectrometry. To take into account the influence of season, the measurements were carried out in summer and in winter. Activity data were analysed by using a geostatistical approach and zones of relatively high or low radioactivity were delineated. Among the main processes which influence natural radioactivity such as geology, geochemical, pedological, and ecological processes, results of this study showed a prominent control of radio-emission measurements by seasonal changes. Low natural radioactivity levels were measured in December associated with winter weather and moist soil conditions (due to high rainfall and low temperature), and higher activity values in July, when the soil was dry and no precipitations occurred.     

北京市土壤中Cr,Ni含量的空间结构与分布特征

本文以一个完整的省级行政单元(北京市)为例,进行系统的、大尺度的土壤Cr,Ni含量的空间分布与污染评价研究,通过地统计学方法分析揭示了北京市土壤中Cr,Ni的空间结构与分布特征,并探索其主要成因,为全面了解北京市的土壤环境质量和开展大规模的土壤环境质量评价研究等提供方法学借鉴和参考.结果表明:土壤Cr,Ni含量的空间结构具有较好的可迁性特点;指数模型拟合效果较好,变程分别为174.6km和15km;半变异函数的方向性分析表明,Cr,Ni均为各向同性,土壤中Cr,Ni具有中等程度的空间相关性.土壤中Cr含量的分布特征为空间相关范围较大,从总体趋势来看,土壤Cr含量的分布较为连续,呈明显的东高西低的分布趋势,大致可以分为3个大的斑块,西北方向(太行山山脉)土壤Cr含量较低,而东南方向(冲积平原)土壤Cr含量相对居中,东北部(燕山山脉)的土壤Cr含量最高.土壤Ni含量的空间分布比较零散,其分布大致表现为东北部地区最高,西南部居中,中部地区最低.研究还揭示,北京市土壤中Cr,Ni含量目前仍主要受成土母质的影响,但是个别地区也存在明显的Cr,Ni含量严重偏高现象.     

漓江流域表层土壤水分物理性质空间异质性

为了解漓江流域土壤水分空间变化特征及其影响因素,基于2'经纬网格,采用地统计学方法对该流域表层（0~10 cm）土壤水分物理性质的空间变异进行分析。结果表明:从空间结构比上,漓江流域表层土壤含水量、容重、最大和最小持水量均具有高度的空间自相关性（各指标空间结构比均大于0.87）,且其空间分布趋势基本一致,由流域上游向中下游逐渐变化。土地利用对流域表层土壤水分物理性质及其空间变异具有显著影响。受时间尺度和土地利用类型等因素的影响,漓江流域表层土壤含水量相比其他土壤水分物理性质,其空间异质性由随机引起的空间变异增加,空间自相关减小,为0.87;而土壤容重最大,为0.92。相关结果对于漓江流域土壤水分动态模拟与预测研究具有一定参考价值。     

Investigation of spatial variability and pattern analysis of soil properties in the northwest of Iran

Analysis of the spatial variability of soil properties is important to explain the site-specific ecosystems. Spatial patterns of some soil properties such as soil texture, exchangeable sodium percentage (ESP), electrical conductivity (ECe), soil pH and cation exchange capacity (CEC) were analyzed in salt and sodic affected soils in the south of the Ardabil province, in the northwest of Iran, to identify their spatial distribution for performance of a site-specific management. Soil samples were collected from 0 to 30, 30 to 60, 60 to 90, 90 to 120 and 120 to 150 cm soil depths at sampling sites. Data were investigated both statistically and geostatistically on the basis of the semivariogram. The spatial distribution model and spatial dependence level varied in the study area. Among the considered parameters, maximum and minimum spatial variability were observed in EC and pH parameters, respectively. Soil properties showed moderate to strong spatial dependence, except for a few. ECe was strongly spatially dependent in the total soil depth and clay was strongly spatially dependent at the first depth. Sand and pH were moderately spatially dependent for three of the five depths. ESP was strongly spatially dependent and silt was moderate in the total soil depths, except at 90–120 cm depth. Furthermore, CEC had strong spatial dependence for three of the five depths. All geostatistical range values were >1,389 m in this study. It was concluded that the strong spatial dependency of soil properties would lead to extrinsic factors such as bedrock, agricultural pollution, drainage and ground water level.     

Assessing soil water content spatio-temporal variability at the hillslope scale in a headwater catchment using a multi variable interpolation model based on EMI surveys (Draix,South Alps,France)

To improve flood prediction in headwater catchments, hydrologists need to know initial soil moisture conditions that precede rain events. In torrential hydrology, soil moisture mapping provides a valuable tool for investigating surface runoff generation processes. In these mountainous environments, soil moisture prediction is challenging because of highly heterogeneous land cover and soil properties. This survey propose a methodology to study spatial soil moisture variations in the mountainous and torrential environment of the Draix Bléone experimental site—Laval 0.86 km². This approach associates water content measurements at the plot scale with spatialized soil bulk electrical conductivity (EC_a) measurements combined in a multivariate statistical analysis based on topographical parameters. Between the summer of 2015 and winter of 2016, four geophysical surveys were conducted under various moisture conditions and along the same pathway, using the Slingram electromagnetic induction (EMI) technique (EM31 device) in horizontal dipole to identify changes in soil properties to a depth of 3 m. These results were analyzed to determine water dynamics in this mountainous catchment. Temporal variations of EC_a vary among land cover types (forest, grassland, and black marl). A significant relationship was observed between EC_a and soil water content (SWC) measured with capacitive sensors in forest and grassland. A multiple linear regression produced using the spatial interpolation code LISDQS shows a significant correlation between EC_a and landform units depicted on a high-resolution DEM. EC_a variations decrease with distance to talwegs. Riparian zones appear as potential hydrological contributing areas with patterns varying according to moisture status. This study shows that multiple linear regression analysis and EMI make it possible to fill gaps between SWC plot measurements, over wide areas that are steep and that present numerous obstacles due to vegetation cover.     

Effect of different soil data on hydrological process modeling in Weihe River basin of Northwest China

Hydrological process modeling depends on the soil data spatial resolution of the watershed. Especially, in a large-scale watershed, could a higher resolution of soil data contribute to a more accurate result? In this study, two soil datasets with different classification systems FAO (World Reference Base) and GSCC (the Genetic Soil Classification of China) were used as inputs for the SWAT model to study the effects of soil datasets on hydrological process modeling in Weihe River basin, China. Results show that the discharge simulated using FAO soil data was better than one simulated using GSCC soil data before model calibration, which indicates that FAO soil data needed less effort to calibrate. After model calibration, discharges were simulated better by both of FAO and GSCC soil data but statistical parameters demonstrate that we can make a relatively more accurate estimation of discharge using the GSCC rather than FAO soil data. Soil water content (SW) simulated using GSCC soil data was statistically significantly higher than those simulated using FAO soil data. However, variations in other hydrological components (surface runoff (SURQ), actual evapotranspiration (ET), and water yield (WYLD) were not statistically significant. This might be because SW is more sensitive to soil properties. For studies aiming to simulate or compare SW, merely calibrating and validating models using river discharge observations is not enough. The hydrological modelers need to identify the key hydrological components intrinsic to their study and weigh the advantages and disadvantages before selecting suitable soil data.