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1.
Conditional simulation of USLE/RUSLE soil erodibility factor by geostatistics in a Mediterranean Catchment,Turkey 总被引：4，自引：4，他引：0
Oguz Baskan Hicrettin Cebel Suat Akgul Gunay Erpul 《Environmental Earth Sciences》2010,60(6):1179-1187
Soil erosion is a major environmental problem that threatens the sustainability and productivity of agricultural areas. Assessment
and mapping of soil erosion are extremely important in the management and conservation of natural resources. The universal
soil loss equation (USLE/RUSLE) is an erosion model that predicts soil loss as a function of soil erodibility (K-factor), as well as topographic, rainfall, cover, and management factors. The traditional approach assumes that one soil
erodibility value represents the entire area of each soil series. Therefore, that approach does not account for spatial variability
of soil series. This study was carried out to evaluate the use of the sequential Gaussian simulation (SGS) for mapping soil
erodibility factor of the USLE/RUSLE methodology. Five hundred and forty-four surface soil samples (0–20 cm) were collected
from the study area to determine the soil erodibility. A simulation procedure was carried out on 300 realizations, and histogram
and semivariogram of the simulation were compared to the observed values. The results showed that the summary statistics,
histogram, and semivariogram of the simulation results were close to the observed values. In contrary to the traditional approach
and kriging, 95% confidence interval of the simulated realizations was formed in order to determine uncertainty standard deviation
map, and the uncertainty was explained numerically. The SGS produced a more reliable soil erodibility map and it can be more
successfully used for monitoring and improving effective strategies to prevent erosion hazards especially to improve site
specific management plans. 相似文献
2.
Catchment scale geostatistical simulation and uncertainty of soil erodibility using sequential Gaussian simulation 总被引：1，自引：0，他引：1
Soil erodibility (K) affects sediment delivery to streams and needs to be appropriately quantified and interpolated as a fundamental geographic variable for implementing suitable catchment management and conservation practices. The spatial distribution of K for erosion modelling at non-sampling grid locations has traditionally been estimated using interpolation algorithms such as kriging which do not adequately represent the uncertainty of estimates. These methods cause smoothing effects through overestimating the low values and underestimating the large values. In this study observed values were used to implement a sequential Gaussian simulation (SGS) procedure to evaluate the certainty of modelled data. Soil erodibility values were computed using 41 soil samples taken from the top 10 cm soil layer regularly distributed across four catchments, 367–770 ha in area, within Kangaroo River State forest, New South Wales (NSW). One hundred realisations were applied in the simulation process to provide spatial uncertainty and error estimates of soil erodibility. The results indicated that values simulated by the SGS algorithm produced similar K values for the neighbouring cells. At the pixel level, the SGS approach generated a reliable estimation of soil erodibility in most areas. Spatial variation of the K factor in this study was strongly related to soil landscape differences across the catchments; within catchments slope gradient did not have a substantial impact on the numerical values of the K factor using pixel-by-pixel comparisons of raster grid maps. 相似文献
3.
Assessing the risk of soil vulnerability to wind erosion through conditional simulation of soil water content in Sistan plain, Iran 总被引：1，自引：0，他引：1
Surface soil water content (SWC) is one of the key factors controlling wind erosion in Sistan plain, southeast of Iran. Knowledge of the spatial variability of surface SWC is then important to identify high-risk areas over the region. Sequential Gaussian simulation (SGSIM) is used to produce a series of equiprobable models of SWC spatial distribution across the study area. The simulated realizations are used to model the uncertainty attached to the surface SWC estimates through producing a probability map of not exceeding a specified critical threshold when soil becomes vulnerable to wind erosion. The results show that SGSIM is a suitable approach for modelling SWC uncertainty, generating realistic representations of the spatial distribution of SWC that honour the sample data and reproduce the sample semivariogram model. The uncertainty model obtained using SGSIM is compared with the model achieved through sequential indicator simulation (SISIM). According to accuracy plots, goodness statistics and probability interval width plots, SGSIM performs better for modelling local uncertainty than SISIM. Sequential simulation provided a probabilistic approach to assess the risk that SWC does not exceed a critical threshold that might cause soil vulnerability to wind erosion. The resulted risk map can be used in decision-making to delineate “vulnerable” areas where a treatment is needed. 相似文献
4.
Nazzareno Diodato Massimo Fagnano Ines Alberico Giovanni Battista Chirico 《Natural Hazards》2011,58(1):445-457
Evaluation of soil erodibility is an important task for Mediterranean lands, in which fertility and crop yield are significantly
affected by soil erosion. The soil physicochemical parameters affecting soil erodibility are highly variable in space and,
as for many other environmental variables, sample measurements are generally not enough for assessing its spatial variability
with an acceptable level of uncertainty at the scales of practical interest. This study illustrates the procedure applied
for estimating the pattern of soil erodibility across the Sele Basin (Southern Italy), where soil properties have been measured
on a limited number of sparse samples. Sampled data were integrated with other sparse data estimated by local regression functions,
which relate soil erodibility to auxiliary variables, such as terrain attributes and land system class memberships. Sampled
and estimated data were merged in a composed data set to assess the spatial pattern of soil erodibility by ordinary kriging.
The proposed approach offers effective spatial predictions, and it is exportable to regions where financial costs for soil
sampling are not feasible. 相似文献
5.
Uncertainty quantification is typically accomplished by simulating multiple geological realizations, which can be very expensive computationally if the flow process is complicated and the models are highly resolved. Upscaling procedures can be applied to reduce computational demands, though it is essential that the resulting coarse-model predictions correspond to reference fine-scale solutions. In this work, we develop an ensemble level upscaling (EnLU) procedure for compositional systems, which enables the efficient generation of multiple coarse models for use in uncertainty quantification. We apply a newly developed global compositional upscaling method to provide coarse-scale parameters and functions for selected realizations. This global upscaling entails transmissibility and relative permeability upscaling, along with the computation of a-factors to capture component fluxes. Additional features include near-well upscaling for all coarse parameters and functions, and iteration on the a-factors, which is shown to improve accuracy. In the EnLU framework, this global upscaling is applied for only a few selected realizations. For 90 % or more of the realizations, upscaled functions are assigned statistically based on quickly computed flow and permeability attributes. A sequential Gaussian co-simulation procedure is incorporated to provide coarse models that honor the spatial correlation structure of the upscaled properties. The resulting EnLU procedure is applied for multiple realizations of two-dimensional models, for both Gaussian and channelized permeability fields. Results demonstrate that EnLU provides P10, P50, and P90 results for phase and component production rates that are in close agreement with reference fine-scale results. Less accuracy is observed in realization-by-realization comparisons, though the models are still much more accurate than those generated using standard coarsening procedures. 相似文献
6.
Soil erodibility is one of the most important factors used in spatial soil erosion risk assessment. Soil information derived from soil map is used to generate soil erodibility factor map. Soil maps are not available at appropriate scale. In general, soil maps at small scale are used in deriving soil erodibility map that largely generalized spatial variability and it largely ignores the spatial variability since soil map units are discrete polygons. The present study was attempted to generate soil erodibilty map using terrain indices derived from DTM and surface soil sample data. Soil variability in the hilly landscape is largely controlled by topography represented by DTM. The CartoDEM (30 m) was used to derive terrain indices such as terrain wetness index (TWI), stream power index (SPI), sediment transport index (STI) and slope parameters. A total of 95 surface soil samples were collected to compute soil erodibility factor (K) values. The K values ranged from 0.23 to 0.81 t ha?1R?1 in the watershed. Correlation analysis among K-factor and terrain parameters showed highest correlation of soil erodibilty with TWI (r 2= 0.561) followed by slope (r 2= 0.33). A multiple linear regression model was developed to derive soil erodibilty using terrain parameters. A set of 20 soil sample points were used to assess the accuracy of the model. The coefficient of determination (r 2) and RMSE were computed to be 0.76 and 0.07 t ha?1R?1 respectively. The proposed methodology is quite useful in generating soil erodibilty factor map using digital elevation model (DEM) for any hilly terrain areas. The equation/model need to be established for the particular hilly terrain under the study. The developed model was used to generate spatial soil erodibility factor (K) map of the watershed in the lower Himalayan range. 相似文献
7.
三维剖面地质界线是构建三维地质结构模型的重要基础数据,其不确定性会影响三维模型的几何形态和属性分布。以单一分布为假设前提的统计学不确定性分析方法掩盖了其他概率分布特征对模型的影响。突破单一误差分布条件的假设前提,本文使用Monte Carlo方法模拟了不同概率分布情况下地质剖面数据中地质界线的抽样采集,以及地质界线空间分布的不确定性;依托地质界线空间位置与地质属性的耦合关系,提出了用地质属性概率分布实现地质界线空间不确定性的定量可视化,并结合实际地质剖面探讨了多种概率分布条件下地质界线的空间不确定性。实例研究表明,基于Monte Carlo模拟的不确定性分析方法可以突破单一误差分布假设条件,结合地质属性概率可充分揭示出建模数据的内在不确定性与模型外在要素形态之间的耦合关系。 相似文献
8.
Gilles Bourgault 《Mathematical Geosciences》2012,44(8):1005-1037
The likelihood of Gaussian realizations, as generated by the Cholesky simulation method, is analyzed in terms of Mahalanobis distances and fluctuations in the variogram reproduction. For random sampling, the probability to observe a Gaussian realization vector can be expressed as a function of its Mahalanobis distance, and the maximum likelihood depends only on the vector size. The Mahalanobis distances are themselves distributed as a Chi-square distribution and they can be used to describe the likelihood of Gaussian realizations. Their expected value and variance are only determined by the size of the vector of independent random normal scores used to generate the realizations. When the vector size is small, the distribution of Mahalanobis distances is highly skewed and most realizations are close to the vector mean in agreement with the multi-Gaussian density model. As the vector size increases, the realizations sample a region increasingly far out on the tail of the multi-Gaussian distribution, due to the large increase in the size of the uncertainty space largely compensating for the low probability density. For a large vector size, realizations close to the vector mean are not observed anymore. Instead, Gaussian vectors with Mahalanobis distance in the neighborhood of the expected Mahalanobis distance have the maximum probability to be observed. The distribution of Mahalanobis distances becomes Gaussian shaped and the bulk of realizations appear more equiprobable. However, the ratio of their probabilities indicates that they still remain far from being equiprobable. On the other hand, it is observed that equiprobable realizations still display important fluctuations in their variogram reproduction. The variance level that is expected in the variogram reproduction, as well as the variance of the variogram fluctuations, is dependent on the Mahalanobis distance. Realizations with smaller Mahalanobis distances are, on average, smoother than realizations with larger Mahalanobis distances. Poor ergodic conditions tend to generate higher proportions of flatter variograms relative to the variogram model. Only equiprobable realizations with a Mahalanobis distance equal to the expected Mahalanobis distance have an expected variogram matching the variogram model. For large vector sizes, Cholesky simulated Gaussian vectors cannot be used to explore uncertainty in the neighborhood of the vector mean. Instead uncertainty is explored around the n-dimensional elliptical envelop corresponding to the expected Mahalanobis distance. 相似文献
9.
A geostatistical approach for mapping and uncertainty assessment of geogenic radon gas in soil in an area of southern Italy 总被引：2，自引：2，他引：0
Gabriele Buttafuoco Adalisa Tallarico Giovanni Falcone Ilaria Guagliardi 《Environmental Earth Sciences》2010,61(3):491-505
Spatial distribution of concentrations of radon gas in the soil is important for defining high risk areas because geogenic
radon is the major potential source of indoor radon concentrations regardless of the construction features of buildings. An
area of southern Italy (Catanzaro-Lamezia plain) was surveyed to study the relationship between radon gas concentrations in
the soil, geology and structural patterns. Moreover, the uncertainty associated with the mapping of geogenic radon in soil
gas was assessed. Multi-Gaussian kriging was used to map the geogenic soil gas radon concentration, while conditional sequential
Gaussian simulation was used to yield a series of stochastic images representing equally probable spatial distributions of
soil radon across the study area. The stochastic images generated by the sequential Gaussian simulation were used to assess
the uncertainty associated with the mapping of geogenic radon in the soil and they were combined to calculate the probability
of exceeding a specified critical threshold that might cause concern for human health. The study showed that emanation of
radon gas radon was also dependent on geological structure and lithology. The results have provided insight into the influence
of basement geochemistry on the spatial distribution of radon levels at the soil/atmosphere interface and suggested that knowledge
of the geology of the area may be helpful in understanding the distribution pattern of radon near the earth’s surface. 相似文献
10.
Matthew A. McClerren Hiroshan Hettiarachchi Donald D. Carpenter 《Geotechnical and Geological Engineering》2012,30(4):881-892
Scour and erosion potential of a soil are closely related to each other. Similarities or differences between them have not been defined fully and the terms are often used interchangeably or in association with one another. Erodibility is a property of soil that describes erosion potential. Therefore, a proper understanding of erodibility should help predict scour more accurately. In the past, researchers have looked into erosion of soils with the ultimate objective of understanding the erodibility with respect to the standard geotechnical properties. Most research has shown the difficulties associated with correlating erodibility to any one or more soil properties. The research described in this paper is mainly focused on the relationship between erodibility and dry unit weight of soil with varying fractions of fines. Soils tested using laboratory Jet Erosion Test (JET) indicated that the logarithm of erodibility makes a linear inverse relationship with the dry unit weight. In situ JETs confirmed the range of erodibilities established by the laboratory JETs. The best correlations between erodibility and dry unit weight appeared within a single category of soil as classified by the Unified Soil Classification System. In addition, it was also determined that the logarithm of erodibility is inversely related to the angle of internal friction of the fluvial soils tested during this investigation. 相似文献
11.
Joint geostatistical simulation techniques are used to quantify uncertainty for spatially correlated attributes, including mineral deposits, petroleum reservoirs, hydrogeological horizons, environmental contaminants. Existing joint simulation methods consider only second-order spatial statistics and Gaussian processes. Motivated by the presence of relatively large datasets for multiple correlated variables that typically are available from mineral deposits and the effects of complex spatial connectivity between grades on the subsequent use of simulated realizations, this paper presents a new approach for the joint high-order simulation of spatially correlated random fields. First, a vector random function is orthogonalized with a new decorrelation algorithm into independent factors using the so-termed diagonal domination condition of high-order cumulants. Each of the factors is then simulated independently using a high-order univariate simulation method on the basis of high-order spatial cumulants and Legendre polynomials. Finally, attributes of interest are reconstructed through the back-transformation of the simulated factors. In contrast to state-of-the-art methods, the decorrelation step of the proposed approach not only considers the covariance matrix, but also high-order statistics to obtain independent non-Gaussian factors. The intricacies of the application of the proposed method are shown with a dataset from a multi-element iron ore deposit. The application shows the reproduction of high-order spatial statistics of available data by the jointly simulated attributes. 相似文献
12.
Bago River is an important river in Myanmar. Although shorter than other rivers, it has its own river system, and people along
the river rely heavily on it for their daily lives. The upper part of the watershed has changed rapidly from closed forest
to open forest land in the 1990s. Since the recent degradation of the forest environment, annual flooding has become worse
during the rainy season in Bago City. This paper aims at determining soil conservation prioritization of watershed based on
soil loss due to erosion and morphometric analysis in the Bago Watershed by integrating remote sensing and geographic information
system (GIS) techniques. In this study, soil erosion of the Bago watershed was determined using the Universal Soil Loss Equation.
Such factormaps as rainfall, soil erodibility, slope length gradient, and crop management were compiled as input parameters
for the modeling; and the soil loss from 26 sub-watersheds were estimated. Then, the soil erosion maps of the Bago watershed
for 2005 were developed. The resulting Soil Loss Tolerance Map could be utilized in developing watershed management planning,
forestry management planning, etc. 相似文献
13.
土壤侵蚀现象是黄土高原的一个重大环境问题。土壤侵蚀的发生和发展有其自然的规律。黄土高原的土壤侵蚀可概括为三种类型:(1)水流侵蚀;(2)重力侵蚀;(3)风力侵蚀。控制黄土高原土壤侵蚀的自然因素为:降水、地形、植被和岩土性质。降水和地形两个因素称之为“侵蚀性因素”;植被和岩土性质两个因素称之为“可蚀性因素”。土壤侵蚀的发生与否,以及侵蚀的程度,最终取决于上述侵蚀性因素与可蚀性因素两者之间相互影响的结果。如果侵蚀性因素的效果大于可蚀性因素的效果,则发生侵蚀作用;反之,则侵蚀作用不显示。 相似文献
14.
Histograms of observations from spatial phenomena are often found to be more heavy-tailed than Gaussian distributions, which
makes the Gaussian random field model unsuited. A T-distributed random field model with heavy-tailed marginal probability density functions is defined. The model is a generalization
of the familiar Student-T distribution, and it may be given a Bayesian interpretation. The increased variability appears cross-realizations, contrary
to in-realizations, since all realizations are Gaussian-like with varying variance between realizations. The T-distributed random field model is analytically tractable and the conditional model is developed, which provides algorithms
for conditional simulation and prediction, so-called T-kriging. The model compares favourably with most previously defined random field models. The Gaussian random field model
appears as a special, limiting case of the T-distributed random field model. The model is particularly useful whenever multiple, sparsely sampled realizations of the
random field are available, and is clearly favourable to the Gaussian model in this case. The properties of the T-distributed random field model is demonstrated on well log observations from the Gullfaks field in the North Sea. The predictions
correspond to traditional kriging predictions, while the associated prediction variances are more representative, as they
are layer specific and include uncertainty caused by using variance estimates. 相似文献
15.
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. 相似文献
16.
Xavier Emery 《Mathematical Geology》2007,39(6):607-623
Conditioning realizations of stationary Gaussian random fields to a set of data is traditionally based on simple kriging.
In practice, this approach may be demanding as it does not account for the uncertainty in the spatial average of the random
field. In this paper, an alternative model is presented, in which the Gaussian field is decomposed into a random mean, constant
over space but variable over the realizations, and an independent residual. It is shown that, when the prior variance of the
random mean is infinitely large (reflecting prior ignorance on the actual spatial average), the realizations of the Gaussian
random field are made conditional by substituting ordinary kriging for simple kriging. The proposed approach can be extended
to models with random drifts that are polynomials in the spatial coordinates, by using universal or intrinsic kriging for
conditioning the realizations, and also to multivariate situations by using cokriging instead of kriging. 相似文献
17.
Soil erosion assessment and its verification using the Universal Soil Loss Equation and Geographic Information System: a case study at Boun,Korea 总被引：10，自引：0，他引：10
This study is aimed at the evaluation of the hazard of soil erosion and its verification at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Precipitation, topographic, soil, and land use data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. Areas that had suffered soil erosion were analysed and mapped using the Universal Soil Loss Equation (USLE). The factors that influence soil erosion are rainfall erosivitiy (R) from the precipitation database, soil erodibility (K) from the soil database, slope length and steepness (LS) from the topographic database, and crop and management (C) and conservation supporting practices (P) from the land use database. Land use was classified from Landsat Thematic Mapper satellite images. The soil erosion map verified use of the landslide location data. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys. 相似文献
18.
不确定性是指关于空间过程和特征不能被准确确定的过程，它是自然界各种空间现象自身固有的属性，如何在GIS数据库中合理的表示不确定性一直是学术界关注的疸。根据数据性质的不同，GIS中数据不确定性可分为定位不确定性和属性不确定性。由于属性数据有定量和定性之分，故相应的属性不确定性可以有2种表示方法：一种是用点误差分析的方法，另一种则是用离散型的数据误差的方法来进行评价，因此可以用标准差来表示定量数据的不确定性，用概率矢量和概率面来分别表示矢量和栅格数据模型中的定位数据不确定性。以此为基础可以很快地导出基于确定的算术运算和逻辑叠加等空间分析操作后的误差传播关系。 相似文献
19.
The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing,wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, etc.which causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover(LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km~2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation(USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor,topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential(reversible soil loss) or 0-1 t ha~(-1) yr~(-1) soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC condition.Results from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions(1984-2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significance of LULC in the control of erosion. Maps generated from the study may be useful to planners and land use managers to take appropriate decisions for soil conservation. 相似文献
20.
Soil erosion by water is a significant problem in arid and semi-arid areas of large parts of Iran. Water erosion is one of the most effective phenomena that leads to decreasing soil productivity and pollution of water resources; especially, in the Mazayjan watershed in the southwest of Fars Province gully erosion contributes to the sediment dynamics in a significant way. Consequently, the intention of this research is to identify the different types of soil erosion processes acting in the area and to assess the process dynamics in an integrative way. Therefore, we applied GIS and satellite image analysis techniques to derive input information for the numeric models. For sheet and rill erosion the Unit Stream Power-based Erosion Deposition Model (USPED) was utilized. The spatial distribution of gully erosion was assessed using a statistical approach, which used three variables (stream power index, slope, and flow accumulation) to predict the spatial distribution of gullies in the study area. The eroded gully volumes were estimated for a 7-year period by fieldwork and Google Earth high-resolution images. Finally the gully retreat rates were integrated into the USPED model. The results show that the integration of the SPI approach to quantify gully erosion with the USPED model is a suitable method to qualitatively and quantitatively assess water erosion processes. The application of GIS and stochastic model approaches to spatialize the USPED model input yields valuable results for the prediction of soil erosion in the Mazayjan catchment. The results of this research help to develop an appropriate management of soil and water resources in the southwestern parts of Iran. 相似文献