Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques

The Harran Plain is located in the southeastern part of Turkey and has recently been developed for irrigation agriculture. It already faces soil salinity problems causing major yield losses. Management of the problem is hindered by the lack of information on the extent and geography of the salinization problem. A survey was carried out to delineate the spatial distribution of salt-affected areas by randomly selecting 140 locations that were sampled at two depths (0 to 30 and 30 to 60 cm) and analyzed for soil salinity variables: soil electrical conductivity (EC), soluble cations (Ca^2+, Mg²⁺, Na⁺, and K⁺), soluble anions (SO ₄ ^2? , Cl^?), exchangeable Na⁺ (me 100 g^?1) and exchangeable sodium percentage. Terrain attributes (slope, topographical wetness index) were extracted from the digital elevation model of the study area. Variogram analyses after log transformation and ordinary kriging (OK) were applied to map spatial patterns of soil salinity variables. Multivariate geostatistical methods—regression kriging (RK) and kriging with external drift (KED)—were used using elevation and soil electrical conductivity data as covariates. Performances of the three estimation methods (OK, RK, and KED) were compared using independent validation samples randomly selected from the main dataset. Soils were categorized into salinity classes using disjunctive kriging (DK) and ArcGIS, and classification accuracy was tested using the kappa statistic. Results showed that soil salinity variables all have skewed distribution and are poorly correlated with terrain indices but have strong correlations among each other. Up to 65 % improvement was obtained in the estimations of soil salinity variables using hybrid methods over OK with the best estimations obtained with RK using EC_0–30 as covariate. DK–ArcGIS successfully classified soil samples into different salinity groups with overall accuracy of 75 % and kappa of 0.55 (p?<?0.001).     

Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta,China

The objectives of this study were to explore the spatial variability of soil salinity in coastal saline soil at macro, meso and micro scales in the Yellow River delta, China. Soil electrical conductivities (ECs) were measured at 0–15, 15–30, 30–45 and 45–60 cm soil depths at 49 sampling sites during November 9 to 11, 2013. Soil salinity was converted from soil ECs based on laboratory analyses. Our results indicated that at the macro scale, soil salinity was high with strong variability in each soil layer, and the content increased and the variability weakened with increasing soil depth. From east to west in the region, the farther away from the sea, the lower the soil salinity was. The degrees of soil salinization in three deeper soil layers are 1.14, 1.24 and 1.40 times higher than that in the surface soil. At the meso scale, the sequence of soil salinity in different topographies, soil texture and vegetation decreased, respectively, as follows: depression >flatland >hillock >batture; sandy loam >light loam >medium loam >heavy loam >clay; bare land >suaeda salsa >reed >cogongrass >cotton >paddy >winter wheat. At the micro scale, soil salinity changed with elevation in natural micro-topography and with anthropogenic activities in cultivated land. As the study area narrowed down to different scales, the spatial variability of soil salinity weakened gradually in cultivated land and salt wasteland except the bare land.     

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.     

新疆图木舒克地区土壤盐渍化空间分布特征

土壤盐渍化已成为影响图木舒克地区农业生产与生态环境的重要因素。本文以0~150 cm深度范围内土壤和地下水为研究对象,利用实际野外调查与数据统计分析的方法,研究得出:①强盐渍化主要分布于距离河流与渠道较远的地下水浅埋深区域,在纵向上表层聚盐现象明显,盐渍土类型随着土壤层深度加大从亚氯-亚硫酸盐渍土变为亚硫酸盐渍土。②土壤易溶盐含量与地下水化学类型在平面分布上吻合程度较高、与潜水矿化度呈正的高度相关性、与潜水埋藏深度呈负的中度相关性、沿潜水径流方向各层土壤盐渍化减弱。③当地下水埋深较浅时,潜水通过毛细管将易溶盐带入土壤表层,形成表层土壤盐渍化;地下水埋藏较深时,易溶盐分会随着降水淋滤和灌溉冲洗不断降低,地表盐渍化减弱。中、轻度盐渍化区域应控制灌溉用水矿化度,重度盐渍化及盐土区域可在建立排水系统的基础上采用泡田洗盐法。     

Soil salinization in the agricultural lands of Rhodope District, northeastern Greece

The objective of this study was to identify seasonal and spatial trends and soil salinization patterns in a part of Rhodope District irrigated land, northeastern Greece, located east of Vistonis Lagoon. The study area is irrigated from a coastal aquifer, where salt water intrusion occurs because of extensive groundwater withdrawals. Fourteen monitoring sites were established in harvest fields in the study area, where soil samples were collected. Electrical conductivity (ECe), pH, and ion concentrations were determined in the saturated paste extract of the soil samples in the laboratory using standard methods. A clear tendency was observed for ECe to increase from April to September, i.e., within the irrigation period, indicating the effect of saline groundwater to soil. In the last years, the change from moderately sensitive (e.g., corn) to moderately tolerant crops (e.g., cotton) in the south part of the study area indicates the impacts of soil salinity. The study proposes management methods to alleviate this problem.     

Assessment of spatial variability in some soil properties as related to soil salinity and alkalinity in Bafra plain in northern Turkey

The objectives of this study were to assess the variability in soil properties affecting salinity and alkalinity, and to analyze spatial distribution patterns of salinity (EC) and alkalinity (ESP) in the plain, which was used irrigation agriculture with low quality waters. Soil samples were collected from 0–30cm, 30–60cm, 60–90cm and 90–120cm soil depths at 60 sampling sites. Soil pH had the minimum variability, and hydraulic conductivity (Ks) had the maximum variability at all depths. The mean values of pH, EC, ESP and Ks increased while the mean values of CEC decreased with soil depth. Values pH, EC and ESP were generally high in the east and northeastern sides. Soil properties indicated moderate to strong spatial dependence. ESP and pH were moderately spatially dependent for three of the four depths, EC exhibited moderate spatial dependence for one of the four depths, CEC had a moderate spatial dependence at all depths, and Ks exhibited a strong spatial dependence. EC, CEC, and ESP were considerably variable in small distances. The spatial variability in small distances of EC, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1230m. It was inferred that the strong spatial dependency of soil properties would be resulted in extrinsic factors such as ground water level, drainage, irrigation systems and microtopography.     

Uncertainty assessment of mapping mercury contaminated soils of a rapidly industrializing city in the Yangtze River Delta of China using sequential indicator co-simulation

Accurate characterization of heavy-metal contaminated areas and quantification of the uncertainties inherent in spatial prediction are crucial for risk assessment, soil remediation, and effective management recommendations. Topsoil samples (0–15 cm) (n = 547) were collected from the Zhangjiagang suburbs of China. The sequential indicator co-simulation (SIcS) method was applied for incorporating the soft data derived from soil organic matter (SOM) to simulate Hg concentrations, map Hg contaminated areas, and evaluate the associated uncertainties. High variability of Hg concentrations was observed in the study area. Total Hg concentrations varied from 0.004 to 1.510 mg kg⁻¹ and the coefficient of variation (CV) accounts for 70%. Distribution patterns of Hg were identified as higher Hg concentrations occurred mainly at the southern part of the study area and relatively lower concentrations were found in north. The Hg contaminated areas, identified using the Chinese Environmental Quality Standard for Soils critical values through SIcS, were limited and distributed in the south where the SOM concentration is high, soil pH is low, and paddy soils are the dominant soil types. The spatial correlations between Hg and SOM can be preserved by co-simulation and the realizations generated by SIcS represent the possible spatial patterns of Hg concentrations without a smoothing effect. Once the Hg concentration critical limit is given, SIcS can be used to map Hg contaminated areas and quantitatively assess the uncertainties inherent in the spatial prediction by setting a given critical probability and calculating the joint probability of the obtained areas.     

Assessment of long-term wastewater irrigation impacts on the soil geochemical properties and the bioaccumulation of heavy metals to the agricultural products

An extensive field survey was employed for assessing the impacts of long-term wastewater irrigation of forage crops and orange orchards in three suburban agricultural areas in Cyprus (areas I, II, and III), as compared to rainfed agriculture, on the soil geochemical properties and the bioaccumulation of heavy metals (Zn, Ni, Mn, Cu, Co) to the agricultural products. Both ryegrass fields and orange orchards in areas I and II were continuously wastewater irrigated for 10 years, whereas clover fields in area III for 0.5, 4, and 8 years. The results revealed that wastewater reuse for irrigation caused a slight increase in soil salinity and Cl^? content in areas I and II, and a remarkable increase, having strong correlation with the period in which wastewater irrigation was practiced, in area III. Soil salinization in area III was due to the high electrical conductivity (EC) of the wastewater applied for irrigation, attributed to the influx of seawater to the sewage collection network in area III. In addition, the wastewater irrigation practice resulted in a slight decrease of the soil pH values in area III, while a subtle impact was identified regarding the CaCO₃, Fe, and heavy metal content in the three areas surveyed. The heavy metal content quantified in the forage plants’ above-ground parts was below the critical levels of phytotoxicity and the maximum acceptable concentration in dairy feed, whereas heavy metals quantified in orange fruit pulp were below the maximum permissible levels (MPLs). Heavy metal phytoavailability was confined due to soil properties (high pH and clay content), as evidenced by the calculated low transfer factor (TF).     

The environmental effects of salinity load in Great Menderes Basin irrigation schemes

This study aimed to assess how poor planning for irrigation led to heavy salt loads, how those salt loads adversely affected the environment, and how to identify management practices to deal with these potential problems in the Great Menderes River Basin, Aydin Plain Irrigation Scheme of Turkey for the period between 2000 and 2006. In order to determine the effects on the environment; electrical conductivity, salinity load, total amount of water table salinity, inflow and outflow salinity were measured and calculated for the research area. There was no problem on account of water table fluctuations. The mean of area prevented the plant growth by high water table was 8.5%. The percentage of the area occurring water table salinity problem was 39.2. The total amount of salt transported to the Great Menderes River and accumulated in the soil of the research area was 211,609 and 246,565 tones respectively. Salt, 458,174 tones in total, had adverse effects on the environment.     

Correlated Parameters Uncertainty Propagation in a Rainfall-Runoff Model,Considering 2-Copula; Case Study: Karoon III River Basin

Hydrological models are widely used to investigate practical issues of water resources. Parametric uncertainty is considered as one of the most important sources of uncertainty in environmental researches. Generally, it is assumed that the parameters are independent mutually, but correlation within the parameter space is an important factor having the potential to cause uncertainty. The objective and innovation of this study was to address the effects of parameters correlation on a continuous hydrological model uncertainty. HEC-HMS with soil moisture accounting (SMA) infiltration method was used to model daily flows and simulate certainty bounds for Karoon III basin, southwest of IRAN, in two scenarios, independent and correlated parameters using 2-copula. The parameters were represented by probability distributions, and the effect on prediction error were evaluated using Latin hypercube sampling (LHS) on Monte Carlo simulation (MCS). Saturated hydraulic conductivity (K), Clark storage-coefficient (R), and time of concentration (tc) were chosen for investigation, based on observed sensitivity analysis of simulated peak over threshold (POT). One hundred runs were randomly generated from 100 parameter sets captured from LHS of parameters distributions in each sub-basin. Using generated parameter sets, 100 continuous hydrographs were simulated and values of certainty bounds calculated. Results showed that when 2-copula correlated R and tc, with 0.656 Kendall’s Tau and 0.818 Spearman’s Rho coefficients, were propagated, decreasing of outputs’ sharpness was more than when considering K and R (K-R), with 0.166 and 0.262; therefore, incorporation of correlations in the MCS is important, especially when the correlation coefficients exceed 0.65. The model was evaluated at the outlet of the basin using daily stream flow data. Model reliability was better for above-normal flows than normal and below-normal. Reliability increases of simulated flow when considering correlated R-tc was more than K-R because of the correlation values. Incorporation of copula for K-tc not only did not improve the model reliability but also decreased it. Results showed improvement of model reliability, by decreasing predicted error of hydrologic modeling, when dealing with correlated parameters in the system.     

Spatial variability of salinity and alkalinity of a field having salination risk in semi-arid climate in northern Turkey

Spatial variability of salinity and alkalinity is important for site-specific management since they are the most important factors influencing soil quality and agricultural production. The objectives of this study were to analyze spatial variability in salinity and alkalinity and some soil properties affecting salinity and alkalinity, using classical statistics and geostatistical methods, in an irrigated field with low-quality irrigation water diverted from drainage canals. A field of 5 da was divided into 10 m x 10 m grids (5 lines in the east-west direction and 10 lines in the north-south direction). The soil samples were collected from three depths (0-30, 30-60 and 60-90 cm) at each grid corner. The variation coefficients of OM and sand contents were higher than other soil properties. OM had the maximum variability, with a mean of 1.63% at 0-30 cm depth and 0.71% at 30-60 cm depth. Significant correlations occurred between ESP, EC and each of Ca, Mg, K and CaCO(3) contents of the soils (p<0.01). Experimental semivariograms were fitted to spherical and gaussian models. All geostatistical range values were greater than 36 m. The soil properties had spatial variability at small distances at 60-90 cm depth. EC was variable within short distances at 30-60 cm depth. The nugget effect of ESP increased with soil depth. Kriged contour maps revealed that soils had a salinisation and alkalisation tendency at 60-90 cm depth based on spatial variance structure of the EC and ESP values. Spatial variability in EC and ESP can depend on ground water level, quality of irrigation water, and textural differences.     

The assessment of spatial distribution of soil salinity risk using neural network

Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains, and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km²) results were used to upscale soil salinity to a district area (∼300 km²). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94 vs. 1.04 dS m⁻¹). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling proved to be satisfactory; depending on critical salinity threshold values, around 70–90% of locations were correctly estimated.     

Soil salinity evolution and its relationship with dynamics of groundwater in the oasis of inland river basins: case study from the Fubei region of Xinjiang Province, China

Soil salinization is an important worldwide environmental problem, especially in arid and semi-arid regions. Knowledge of its temporal and spatial variability is crucial for the management of oasis agriculture. The study area has experienced dramatic change in the shallow groundwater table and soil salinization during the 20th century, especially in the past two decades. Classical statistics, geostatistics and geographic information system (GIS) were applied to estimate the spatial variability of the soil salt content in relation to the shallow groundwater table and land use from 1983 to 2005. Consumption of reservoir water for agricultural irrigation was the main cause of a rise in the shallow groundwater table under intense evapotranspiration conditions, and this led indirectly to soil salinization. The area of soil salt accumulation was greater in irrigated than in non-irrigated landscape types with an increasing of 40.04% from 1983 to 2005 in cropland at ∼0.43 t ha⁻¹ year⁻¹, and an increase at ∼0.68 t ha⁻¹ year⁻¹ in saline alkaline land. Maps of the shallow groundwater table in 1985 and 2000 were used to deduce maps for 1983 and 1999, respectively, and the registration accuracy was 99%.     

Major ion chemistry and hydrochemical studies of groundwater of parts of Palar river basin, Tamil Nadu, India

Groundwater is almost globally important for human consumption as well as for the support of habitat and for maintaining the quality of base flow to rivers, while its quality assessment is essential to ensure sustainable safe use of the resources for drinking, agricultural, and industrial purposes. In the current study, 50 groundwater samples were collected from parts of Palar river basin to assess water quality and investigate hydrochemical nature by analyzing the major cations (Ca, Mg, Na, K) and anions (HCO(3), Cl, F,SO(4), NO(3), PO(4),CO(3), HCO(3), and F) besides some physical and chemical parameters (pH, electrical conductivity, alkalinity, and total hardness). Also, geographic information system-based groundwater quality mapping in the form of visually communicating contour maps was developed using ArcGIS-9.2 to delineate spatial variation in physicochemical characteristics of groundwater samples. Wilcox classification and US Salinity Laboratory hazard diagram suggests that 52% of the groundwater fall in the field of C2-S1, indicating water of medium salinity and low sodium, which can be used for irrigation in almost all types of soil with little danger of exchangeable sodium. Remaining 48% is falling under C1-SI, indicating water of low salinity and low sodium.     

Field-scale monitoring of the long-term impact and sustainability of drainage water reuse on the west side of California's San Joaquin Valley

Diminishing freshwater resources have brought attention to the reuse of degraded water as a water resource rather than a disposal problem. Drainage water from tile-drained, irrigated agricultural land is degraded water that is often in large supply, but the long-term impact and sustainability of its reuse on soil is unknown. Similarly, nothing is known of the ramifications of terminating drainage water reuse. The objective of this study is (i) to monitor the long-term impact on soil chemical properties and thereby the sustainability of drainage water reuse on a marginally productive, saline-sodic, 32.4 ha field located on the west side of California's productive San Joaquin Valley and (ii) to assess spatially what happens to soil when drainage water reuse is terminated. The monitoring and assessment were based on spatial chemical data for soil collected during 10 years of irrigation with drainage water followed by 2 years of no applied irrigation water (only rainfall). Geo-referenced measurements of apparent soil electrical conductivity (EC(a)) were used to direct the soil sampling design to characterize spatial variability of impacted soil properties. Chemical analyses of soil samples were used (i) to characterize the spatial variability of salinity, Na, B, and Mo, which were previously identified as critical to the yield and quality of Bermuda grass (Cynodon dactylon (l.) Pers.) grown for livestock consumption and (ii) to monitor their change during the 12 year study. Soil samples were taken at 0.3 m increments to a depth of 1.2 m at each of 40 sample sites on five occasions: August 1999, April 2002, November 2004, August 2009, and May 2011. Drainage water varying in salinity (1.8-16.3 dS m(-1)), SAR (5.2-52.4), Mo (80-400 μg L(-1)), and B (0.4-15.1 mg L(-1)) was applied from July 2000 to June 2009. Results indicate that salts, Na, Mo, and B were leached from the root zone causing a significant improvement in soil quality from 1999 to 2009. Salinity and SAR returned to original levels or higher in less than two years after termination of irrigation. Boron and Mo showed significant increases. Long-term sustainability of drainage water reuse was supported by the results, but once application of irrigation water was terminated, the field quickly returned to its original saline-sodic condition.     

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.     

Spatial distribution and concentration of sulfur in relation to vegetation cover and soil properties on a reclaimed sulfur mine site (Southern Poland)

This work aims to assess the spatial distribution and concentration of sulfur in the topsoil layer and to determine the relationships between sulfur concentration, soil pH, soil electrical conductivity, and plant cover at the reforested site of the former sulfur mine (Southern Poland). Soil samples were collected from 0 to 20 cm (topsoil) from a total of 86 sampling points in a regular square grid with sides of 150 m. Plant cover was assayed in circular plots with an area of 100 m², divided into a woody plant layer and herbaceous plant layer. Soil properties such as particle size distribution, pH in KCl and H₂O, soil electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (N_T), and total sulfur (S_T) were determined. The degree of soil contamination with sulfur was assessed based on the guidelines of the Institute of Soil Science and Plant Cultivation (IUNG), Poland. The results indicate that remediation and application of lime were not fully effective in spatial variation, because 33 points with sulfur contamination above 500 mg kg^?1 were observed. These spots occurred irregularly in the topsoil horizons. This high sulfur concentration in the soil did not result in severe acidification (below 4.5) in all cases, most likely due to neutralization from the application of high doses of flotation lime. High vegetative cover occurred at some points with high soil sulfur concentrations, with two points having S concentration above 40,000 mg kg^?1 and tree cover about 60%. Numerous points with high soil EC above 1500 μS cm^?1 as well as limited vegetation and high soil sulfur concentrations, however, indicate that the reclamation to forest is still not completely successful.     

Assessing the effects of land use changes on soil sensitivity to erosion in a highland ecosystem of semi-arid Turkey

There has been increasing concern in highlands of semiarid Turkey that conversion of these systems results in excessive soil erosion, ecosystem degradation, and loss of sustainable resources. An increasing rate of land use/cover changes especially in semiarid mountainous areas has resulted in important effects on physical and ecological processes, causing many regions to undergo accelerated environmental degradation in terms of soil erosion, mass movement and reservoir sedimentation. This paper, therefore, explores the impact of land use changes on land degradation in a linkage to the soil erodibility, RUSLE-K, in Cankiri–Indagi Mountain Pass, Turkey. The characterization of soil erodibility in this ecosystem is important from the standpoint of conserving fragile ecosystems and planning management practices. Five adjacent land uses (cropland, grassland, woodland, plantation, and recreational land) were selected for this research. Analysis of variance showed that soil properties and RUSLE-K statistically changed with land use changes and soils of the recreational land and cropland were more sensitive to water erosion than those of the woodland, grassland, and plantation. This was mainly due to the significant decreases in soil organic matter (SOM) and hydraulic conductivity (HC) in those lands. Additionally, soil samples randomly collected from the depths of 0–10 cm (D ₁) and 10–20 cm (D ₂) with irregular intervals in an area of 1,200 by 4,200 m sufficiently characterized not only the spatial distribution of soil organic matter (SOM), hydraulic conductivity (HC), clay (C), silt (Si), sand (S) and silt plus very fine sand (Si + VFS) but also the spatial distribution of RUSLE-K as an algebraically estimate of these parameters together with field assessment of soil structure to assess the dynamic relationships between soil properties and land use types. In this study, in order to perform the spatial analyses, the mean sampling intervals were 43, 50, 64, 78, 85 m for woodland, plantation, grassland, recreation, and cropland with the sample numbers of 56, 79, 72, 13, and 69, respectively, resulting in an average interval of 64 m for whole study area. Although nugget effect and nugget effect–sill ratio gave an idea about the sampling design adequacy, the better results are undoubtedly likely by both equi-probable spatial sampling and random sampling representative of all land uses.     

Earth-tide-induced fluctuations in the salinity of an inland river,New South Wales,Australia: a short-term study

Wybong Creek discharges salts into the agriculturally and industrially important Hunter River in New South Wales, Australia. Abrupt increases in salinity occur periodically in the mid-Wybong Creek catchment. In order to understand the processes which cause these abrupt increases, changes in surface and groundwater were investigated. It is shown that salinity increases can be attributed to highly discrete groundwater discharge directly into the river from below. Hourly electrical conductivity data measured in the river showed regular, diurnal electrical conductivity fluctuations of up to 350 μS cm^?1. These fluctuations could not be attributed to barometric pressure, temperature, or evapotranspiration. Instead, a similar periodicity in surface water electrical conductivity and groundwater height in nearby groundwater wells was found. Fluctuations were of similar periodicity to the orthotides which cause fluctuations in surface water height and are induced by Earth tides. The geology in the mid-catchment area indicates conditions are optimal for Earth tides to impact groundwater. The reporting of orthotidal changes in water chemistry in this article is believed to be the first of its kind in the scientific literature, with the large fluctuations noted having important implications for water monitoring and management in the catchment. Further research investigating Earth-tide-induced phases of groundwater heights will better constrain the relationships between surface water chemistry and groundwater height.     

An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems

This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0–30, 30–60, and 60–90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0–30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 × 150 m²) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.