Distribution Of Soil Parameters Research Articles

Assessing the Impact of Erosion and Farming Practices on the Spatial Distribution of Topsoil Characteristics in a Sloping Vineyard Using an Open-source QGIS Software

Erosion is one of the major threats that negatively affect agricultural soils. Several studies indicated that sloping vineyards are prone to soil erosion due to the soil management method. This study was conducted in a sloping vineyard planted in 2019 (with a slope of 100 and a slope length of 38 m) in Thu Cuc commune, Tan Son district, Phu Tho province, Vietnam. The main objective of the study is to evaluate soil characteristics (pH, particle-size distribution, organic matter, cation exchange capacity (CEC), N, P, and K macronutrients) related to terrain morphology, soil erosion, and farming practices. The open-source QGIS software was used to evaluate the spatial distribution of the soil characteristics. We found that the organic matter content, CEC, and clay content in the soils taken at the footslope zones were higher than those at the top of the hill as a result of soil erosion. In contrast, higher contents of K and silt were observed at the top of the hill compared to the footslope, indicating an unapparent impact of soil erosion on these two factors in the studied vineyard. Phosphorus was evenly distributed throughout the plot and influenced by the soil characteristics. Indeed, a strong correlation between total P content and soil pH, organic matter, and silt content was revealed in our study. On the other hand, cultivation practices exerted a significant impact on the prevailing accumulation of soil organic matter, CEC, total N, and clay particle size at the lower part of the vine rows following the horizontal flow. The vine rows were planted along the contour lines rather than the dominant slope of the vineyard can explain this tendency. In addition, no cover crops sown between the vine rows probably resulted in intense soil erosion at the steepest part of the terrain. Overall, the terrain morphology and farming practices played an essential role in the spatial distribution of soil characteristics. Therefore, integrating geographic information systems into building a digital database and predicting the spatial distribution of soil parameters is necessary to perform soil quality monitoring and serve sustainable land management.

Evaluating Spatial Soil Parameters of an Apple Orchard in ‘R’ Software: A Study from Kashmir Valley

Maintaining the ecological balance of the physical and biological environment depends greatly on the spatial variation of soil parameters across a given region and its elucidation using remote sensing and GIS applications. R software was used to apply the geostatistical methods of Ordinary Kriging and Inverse Distance Weightage to the study of the spatial distribution and variability of the soil parameters of an apple orchard in the Valley of Kashmir. Average values for available phosphorus (P), potassium (K), calcium (Ca), available nitrogen (N), and soil organic carbon (OC) were 1.15 percent, 315 percent, 22.5 kg, 221.4 kg, and 250 mg, respectively. Ordinary kriging (OK) and IDW were used to plot the spatial distribution of soil parameters using spherical (pH, OC, and Ca), steradian (EC, N, P, and K), and mean square error (MSE) values. According to the semi-variogram analysis, there was a strong to moderate spatial dependence. The interpolated maps showed various soil distribution patterns for pH (5.4-5.7), EC (0.46-0.56 DSM-1), OC (0.9-1.4 percent), N (296-335) kg ha-1, P (20-25 kg ha-1), K (221-221.8 kg ha-1), and Ca (100-400 mg kg-1) at the regional scale. This study represented a wide range of spatial soil variability. The estimation of MSE for Ordinary Kriging and IDW was used to predict the best model after performing a validation accuracy assessment. The best fit model for interpolation was the standard kriging model with low MSE. The maps of soil spatial distribution created to serve as an effective tool for policymakers and those planning farms.

Seismic Stability Analysis of Tunnel Faces in Heterogeneous and Anisotropic Soils Using Modified Pseudodynamic Method

This work assesses the seismic stability of tunnel faces advanced in heterogeneous and anisotropic soils based on the plastic limit theorem. A discretized kinematic velocity field respecting the normal flow rule is generated via a point-to-point discretization technique. The distribution of soil parameters in the depth direction including cohesion, friction angle, and unit weight are considered by four kinds of profiles. The variation in cohesion with shear direction caused by consolidation and sedimentation is considered by including an anisotropy coefficient. The seismic acceleration is represented by the modified pseudodynamic method (MPD) rather than the conventional pseudodynamic method (CPD). Based on the energy equilibrium equation, an upper-bound solution is derived. The accuracy and rationality of the proposed procedure are substantiated by comparing with the solutions obtained by conventional log-spiral mechanism and CPD. A parametric study indicates that nonlinear profiles tend to predict a smaller required face pressure than the constant and linear profiles due to the convexity of nonlinear profiles. The over-consolidated soil is more sensitive to the anisotropy coefficient than normally consolidated soil. Moreover, the adverse effect of horizontal seismic acceleration is much greater than that of vertical acceleration, and the resonance effect is more prone to happen, especially for shallow-buried tunnels.

A borehole array data–based approach for conducting 1D site response analyses II: Accounting for modeling errors

Site response estimates from one-dimensional (1D) site response analyses (SRAs) carry inaccuracies due to modeling and parametric errors. Modeling errors are due to the condensation of the three-dimensional (3D) wave propagation phenomenon to the vertical propagation of a horizontally polarized wave through a soil column, and parametric errors are due to the incomplete knowledge of the distributions of soil parameters, leading to the selection of nonoptimal input parameters for a site of interest. While parametric errors are traditionally handled using different soil parameters (e.g. alternative shear-wave velocity profiles), modeling errors are generally neglected. This paper proposes an approach for conducting linear elastic 1D SRAs to improve site response predictions and account for modeling errors. First, ground-motion data from borehole array sites are collected, processed, and screened for appropriateness (e.g. expected shear strains lower than 0.01%, signal-to-noise ratio higher than 3). Second, 1D SRA predictions in terms of transfer functions and amplification factors are compared against observations, and the discrepancies are quantified as residuals. Finally, the residuals are partitioned into a model bias term [Formula: see text], a site term [Formula: see text] with standard deviation [Formula: see text], and a event- and site-specific remaining residual [Formula: see text] with standard deviation [Formula: see text]. Values for [Formula: see text] and [Formula: see text] for forward predictions are recommended. The sensitivity of the site response residuals to region, site type (1D- or 3D-like), and the applicability of findings to outcropping applications are discussed, and an example application for a hypothetical project site is presented.

Spatial Variability in Soil Properties, Delineation Site-specific Management Division Based on Soil Fertility Using Fuzzy Clustering in Gwalior, Madhya Pradesh, India

Farmers who want to improve nitrogen usage efficiency (NUE) and crop yield must have access to information regarding the geographical variability and distribution of soil parameters. Fertilizer application relying on soil characteristic maps and fertilizers recommendations may help reduce fertilizer input without sacrificing crop production. The current research focused heavily on evaluating the variability of soil fertility status in the Madhya Pradesh Gwalior region using geostatistical techniques. To do this, 150 GPS-based surfaces (0–15 cm) soil samples were obtained from the Gwalior region's five districts (Gwalior, Shivpuri, Datia, Guna, & Ashok Nagar) during crop harvest during the rabi season of 2019–20. Statistics and geo-statistics were used to analyze the results of the laboratory analysis. The analysis revealed that the soil samples' pH, EC, SOC, and CaCO3 values, respectively, varied from 4.40 to 8.30, 0.09 to 1.03 dSm-1, 2.0 to 10.60 gkg-1, and 3.0 to 24.0 gkg-1. In contrast, the amounts of N, P, K, and S that are present in soil vary from 102.0 to 356.0 kg ha-1, 6.0 to 61.0 kg ha-1, 114.0 to 896.0 kg ha-1, and 5.90 to 49.20 mg kg-1, respectively.
 Through building semi-variograms and mapping the data utilizing standard kriging techniques, the information was studied using both traditional statistics and geostatistics. For soil properties, semi-variograms were created, and their geographical distributions were delineated. The Nugget/Sill (Co/Co+ C) ratio for the modeled variables revealed moderate to high spatial dependences. The best-fit models for the reported soil characteristics were exponential, spherical, and circular. The findings of this research demonstrated that soil fertility quality varied significantly across the Gwalior area. This knowledge may aid in making choices about crop succession and the usage of plant nutrients to increase farmers' financial returns.

Spatial Study of Enzymatic Activities from Bacterial Isolates in a Mediterranean Urban Park

Urban parks are a rich source of microbial diversity, as they are heavily used by city residents. In this study, we sampled a Mediterranean park and were able to isolate bacteria that have the ability to inhibit the growth of control microorganisms. Out of the 560 bacteria we tested, many displayed antibacterial activity, particularly against Salmonella sp. and K. pneumoniae. These results suggest that the microorganisms in the park are in close proximity to the human population. Additionally, the isolated bacteria demonstrated diverse enzymatic activities, possibly as a response to the environmental substances available to them, which could aid in the degradation of different compounds of interest. The study of the spatial distribution of soil parameters and the inhibition against relative-safe pathogens in an urban park in València (Spain) demonstrated a higher proportion of isolates in certain areas. These spatial data maps can help researchers understand the behaviors of bacterial populations on a regional level, which can assist in the creation of novel antimicrobial agents and promote advancements in public health.

Mapping of Soil pH Based on SVM-RFE Feature Selection Algorithm

The explicit mapping of spatial soil pH is beneficial to evaluate the effects of land-use changes in soil quality. Digital soil mapping methods based on machine learning have been considered one effective way to predict the spatial distribution of soil parameters. However, selecting optimal environmental variables with an appropriate feature selection method is key work in digital mapping. In this study, we evaluated the performance of the support vector machine recursive feature elimination (SVM-RFE) feature selection methods with four common performance machine learning methods in predicting and mapping the spatial soil pH of one urban area in Fuzhou, China. Thirty environmental variables were collected from the 134 samples that covered the entire study area for the SVM-RFE feature selection. The results identified the five most critical environmental variables for soil pH value: mean annual temperature (MAT), slope, Topographic Wetness Index (TWI), modified soil-adjusted vegetation index (MSAVI), and Band5. Further, the SVM-RFE feature selection algorithm could effectively improve the model accuracy, and the extreme gradient boosting (XGBoost) model after SVM-RFE feature selection had the best prediction results (R2 = 0.68, MAE = 0.16, RMSE = 0.26). This paper combines the RFE-SVM feature selection with machine learning models to enable the fast and inexpensive mapping of soil pH, providing new ideas for predicting soil pH at small and medium scales, which will help with soil conservation and management in the region.

Machine learning algorithms in geostatistical data analysis: formulation and observation

Geostatistical analyses on spatiotemporal datasets have been increasingly popular on diverse disciplines including environmental sciences, soil and earth studies, meteorology, hydrology, and oceanography. They help in documenting and summarizing information to understand the variation of the process and other parameters. Due to high spatial variability and noise in the dataset, traditional geostatistical tools often fail to produce desirable results. In view of this, the present study proposes two machine learning methods, namely artificial neural network (ANN) and k-nearest neighbour (kNN), along with the kriging interpolation to derive temporal distribution of soil parameters. Results in terms of smooth porosity maps demonstrate the effectiveness of the proposed algorithm.

Electrical Geophysical method and GIS in Agricultural Crop Productivity in a Typical Sedimentary Environment

ABSTRACT Accurate information about soil properties is required in plant growth and yield. Hence, the rising knowledge in the preservation of favourable soil properties is basically driven by its vital role in agricultural crop productivity. Electrical Resistivity Tomography (ERT) and GIS were used for site selection, soil suitability for agricultural use and topographical effect on soil properties distribution in Ambrose Alli University. Twelve traverses were conducted across the study area using Wenner array configuration. The least-square inversion method was adopted for the inversion model using RES2Dinver. Seven soil samples were collected from the selected site for soil test and grain analysis using IS standard in order to validate the ERT results. The results of the ERT gave resistivity signatures in the form of colour variations that depict the organic matter and mineral constituents of the soil. Soil in the valley side of the study area showed low resistivity signatures < 100 Ωm, slopy portion ranged from 100 to 600 Ωm, and soil at the top of the slope has resistivity values > 600 Ωm. GIS results generally classified spatial distribution of soil parameters across the area. Soil with low resistivity values < 100 Ωm has Organic Matter (OM): 4.27–4.80 %, Phosphorus (P): 50.23–54.22 mg/kg, Potassium (K): 290.0–340.0 cmol/kg, Total Nitrogen (TN): 62.20–65.11%, and pH: 6.8 to 6.9. Soil with resistivity values ranging from 100 to 600 Ωm has OM: 2.21–2.75%, P: 30.14–34.00 mg/kg, K: 100.2–107.2 cmol/kg, TN: 54.00–59.20%, and pH: 6.7–6.8. Soil with resistivity values > 600 Ωm showed lowest value ranges for OM: 0.51–0.61%, P: 10.20–15.20 cmol/kg, K: 37.20–40.20 mg/kg, TN: 10.20–15.20 %, and pH: 7.0 − 7.2. This study has shown that the study area with resistivity values < 100 Ωm would be most appropriate for high agricultural productivity as well as soil management.

Dependence Structures of Soil Parameters in Sandy Clay

AbstractThe probability density distribution of soil parameters is of ultimate importance in reliability analysis of a geotechnical structure. Constructing the probability density function often ne...

Management zone delineation and spatial distribution of micronutrients in cold-arid region of India.

The plant health is governed by many factors: soil playing a central role which exhibits huge variability in its characteristics. Micronutrients even though needed in small quantities by plants play an indispensable role in affecting the crop growth and development. The assessment of spatial variability of different soil parameters is incumbent for tackling the loss of crop productivity on account of non-receipt of desirable inputs. The present investigation centered on the spatial distribution of soil parameters and micronutrients was conducted to delineate management zones (MZs) in cold arid region (Kargil) of India for proficient micronutrient management. Overall 454 georeferenced representative soil samples at the depths of 0-15 cm were garnered. The soil samples were processed and analyzed for different soil parameters encompassing pH, EC (electrical conductivity), SOC (soil organic carbon), and available micro-nutrient (iron, manganese, zinc, copper and nickel) concentrations. The distinct variation in the soil properties including micronutrients was identified with coefficient of variation ranging as low as 5.62% to moderate (21.16 to 42.49%) and as high as 159.63%. Semivariogram analysis and ordinary kriging of soil variables under study revealed diverse spatial distribution exhibiting medium to high spatial dependence in the region. PCA (principal component analysis) and K-means clustering were expended for the delineation of MZs. Four principal components (PCs) having eigen values > 1 and accounting for 70% of the total variation were subjected to further analysis. The five potential MZs were demarcated on the basis of K-means cluster performance index, and heterogeneity in parameters was discerned. The results of study corroborate that the spatial variability analysis of different soil parameters for delineation/identification of MZs might be effectually employed for site-specific micronutrient management.

Elevation and aspect determine the differences in soil properties and plant species diversity on Himalayan mountain summits

AbstractAlthough vegetation has been the focus of recent studies on mountain summits, little is known about smaller‐scale spatial patterns of soil physico‐chemical properties. Here, we report patterns and drivers of soil physico‐chemical properties and their role in shaping the plant diversity on mountain summits of the Himalaya. Using the globally standardized Multi‐Summit Approach, four summits along an elevation gradient from treeline to nival zone were selected in Kashmir Himalaya. Sampling of the summits was carried out to collect soil samples together with vegetation data and soil temperature. The results revealed that there is a significant effect of elevation and aspect on soil physico‐chemical properties and species richness on the summits. A significant correlation was observed between soil parameters and elevation, indicating that summits with distinct pool of species possess distinct soil properties. Moreover, soil temperature clearly determined the aspect‐wise distribution of soil parameters and species richness. The Canonical Correspondence Analysis revealed that elevation, soil temperature, pH, electrical conductivity and C:N ratio were more prominent in determining the plant diversity on summits. Our results demonstrate that mosaic of micro‐climatic conditions driven by elevation and aspect favor a suite of soil properties, which in turn determine the patterns of species diversity on the mountain summits. The present study, therefore, enhances understanding of the spatial patterns of variation in soil and plant diversity of mountain summits, which will help to monitor, model and predict how these ecologically unique ecosystems will respond to climate warming in the Himalaya, with implications for such environments elsewhere.

Geostatistical based soil fertility mapping of Horticultural Research Station, Rajikot, Jumla, Nepal

Geostatistical soil mapping is important for determining spatial distribution of soil parameters. This study was conducted to assess soil spatial distribution of the Horticultural Research Station, Rajikot, Jumla, Nepal. The total 27 samples were collected randomly at a depth of 0-20 cm by using soil sampling auger. A GPS device was used for determination of geographical position of soil sampling points. The collected samples were analyzed following standard analytical methods in the laboratory of National Soil Science Research Centre, Khumaltar. The Arc-GIS 10.1 software was used for the mapping spatial distribution of various soil parameters. The observed data revealed the structure was sub-angular blocky and granular, whereas colour were dark brown to dark grayish brown and dark yellowish brown. The sand, silt and clay content were ranged 24.40-72.10%, 19.40-63.10% and 6.20-14.50%, respectively and categorized loam, sandy loam and silt loam in texture. The soil pH was slightly acidic to moderately alkaline (5.01-8.06), and very low available sulphur (0.59-2.41 mg/kg) and very low to low available boron (0.04-0.87 mg/kg). Moreover, very low to medium available manganese (2.18-13.46 mg/kg), very low to very high available iron (4.50-138.58 mg/kg), and low to medium available magnesium (7.20-177.60.53 mg/kg) and zinc (0.26-1.66 mg/kg). Similarly, medium total nitrogen (0.09-0.22%), medium to high organic matter (1.71-6.26%) and available calcium (1200-3144 mg/kg), medium to very high available phosphorus (3.71-82.4 mg/kg) and potassium (59.37-173.05 mg/kg). Correspondingly, high to very high available copper (0.78-4.20 mg/kg). The determined soil test data can be used for sustainable soil management as well as developing future research strategy in the farm.

Establishing management zones of soil sulfur and micronutrients for sustainable crop production

AbstractDeficiency of nutrients in agricultural soils of the world is one of the major factors of soil degradation. The deficiency of nutrients especially sulfur (S) and micronutrients in different soils affect global crop production. Delineation of soil management zones (MZs), by understanding spatial distribution of soil parameters, could be an efficient way for devising strategies for effective nutrient management and sustainable crop production. In this study, we aimed at creation of soil MZs for ameliorating S and micronutrients deficiencies in the Narmada River basin (NRB), an important agricultural area of central India. We collected 5,984 geo‐referenced top layer (0–15 cm) soil samples from the NRB, India, and analyzed for soil acidity (soil‐water suspension), electrical conductivity (soil‐water suspension), soil organic carbon (Walkley and Black carbon), phyto‐available S (0.15% calcium chloride [CaCl2] extractable), and phyto‐available (diethylene triamine penta acetic acid extractable) micronutrients namely zinc (Zn), copper (Cu), manganese (Mn), iron (Fe), and boron (B) (hot‐water‐soluble). The values of studied soil parameters varied widely with coefficient of variation values ranging from 11.0 to 74.9%. Pearson's correlation coefficient analysis revealed significant correlations among the soil parameters. Geostatistical analysis revealed exponential, pentaspherical, K‐Bessel, and circular best‐fit semi‐variogram models for different soil parameters with moderate to strong spatial dependence. About 41.2, 78.6, 10.1, 2.70, and 32.6% of the area of the NRB had a deficit concentration of phyto‐available S, Zn, Fe, Mn, and B, respectively. The principal component analysis and fuzzy c‐means clustering produced five MZs. The produced MZ maps could be utilized for prioritization of nutrients supply and adoption of zone‐specific soil nutrient management strategies in order to achieve sustainable crop production in the NRB of India.

Efficient probabilistic back analysis of spatially varying soil parameters for slope reliability assessment

The probability distributions of soil parameters can be updated with limited site-specific information via probabilistic back analyses. The updated probability distributions can be further used for a more realistic slope reliability assessment. However, few attempts have been made to conduct probabilistic back analyses accounting for the inherent spatial variability of soil properties. The main challenge is the so-called curse of dimensionality encountered when thousands of random variables are used to model spatial variability. The BUS approach (Bayesian Updating with Structural reliability methods) can tackle the high-dimensional back analysis problem by transforming it into an equivalent structural reliability problem, but it requires an evaluation of the likelihood multiplier that is tedious and time-consuming. This paper proposes a modified BUS approach for the probabilistic back analysis of soil parameters and reliability updating of slopes in spatially variable soils. With this approach, the curse of dimensionality and evaluation of the likelihood multiplier can be effectively avoided, and the computational accuracy is significantly improved. Two slope examples are investigated to illustrate the effectiveness of the proposed approach. The soil parameters and their probability distributions for a slope section can be well determined through a probabilistic back analysis, which facilitates an accurate identification of the causes of slope failures and a deep understanding of the actual performance of in-service slopes.

Modeling multivariate distribution of multiple soil parameters using vine copula model

This study introduces the vine copula model to model the multivariate distribution of multiple soil parameters. First, the conventional bivariate and multivariate copulas are presented to model the joint probability distribution of soil parameters. Then, the procedure for modeling the multivariate distribution of soil parameters using the vine copula model is explained. Finally, two soil databases (CLAY/5/345 and CLAY/6/535) containing complete multivariate data of multiple soil parameters are studied to demonstrate the validity of the vine copula model. The results indicate that there exist different levels of correlation between all pairs of soil parameters. The dependence structure among multiple soil parameters shows obvious diversity and non-Gaussianity, which cannot be adequately characterized by the commonly-used multivariate normal distribution. The vine copula model performs well in modeling the multivariate distribution of multiple soil parameters. It can effectively consider the diversity and non-Gaussianity in the dependence structure among multiple soil parameters. In comparison with the original distributions, the coefficients of variation (COVs) for the conditional distributions of soil parameters may be significantly reduced. The incorporation of more information may produce smaller COVs for the conditional distributions. The reduced COVs for soil parameters can be eventually converted to cost savings in the reliability-based design of geotechnical structures, which provides an incentive for geotechnical engineers to collect more information of soil parameters from various sources.

Assessing soil physical properties variability and their impact on vegetation using geospatial tools in Kebbi State, Nigeria

Geospatial distribution of soil physical properties using Geographic Information System (GIS) is essential and efficient to site-specific farming and environmental management processes. This study attempts to evaluate the spatial distribution of soil physical properties and their impact on vegetation status in Kebbi State, Nigeria using the geospatial technique. A total of one hundred and fifty-six (156) soil samples were collected and analysed for soil organic matter (SOM), porosity, texture, Bulk Density (BD) and pH. The data were analyzed both statistically and geospatially to describe the spatial distribution of soil physical properties in the area. The Normalized Difference Vegetation Index (NDVI) was examined on Sentinel 2 satellite imagery. Results show the normal distribution for all parameters, except for soil texture and SOM indicating a positively skewed distribution. The spatial distribution of soil parameters results revealed the existence low to moderate spatial distribution. In comparison to the NDVI, soil properties significantly correlate with vegetation status of the area, except soil porosity which shows an inverse correlation. The study revealed that the use of geospatial technique accurately generates the spatial distribution maps of soil properties in the area and therefore, present a recommendable tool for sustainable land management and environmental management.

Geomorphological analysis in soil profile assessment of the margin of Warsaw morainic plateau in Ursynów

Abstract This paper addresses the application of geomorphological analysis to recognize the geotechnical conditions at the margin of a moraine plateau in Warsaw. Aerial photographs, borehole data logging, CPT and DMT tests results were taken into account to compare the conditions at two sites, i.e. the well recognized part of the moraine plateau (SGGW Stadium site) and the second part located at a distance of 5 km (Warsaw slope – church). Spatial variability of the strength and deformation properties in the study areas were determined using CPT and DMT tests results. The general conclusion is that the specific geomorphological units allow for prediction to some extent the sequence of geotechnical layers and the general distribution of soil parameters. The example from the margin of the moraine plateau in Warsaw shows that such procedure is a promising statistical tool related to geotechnical problems.

Simulation of future groundwater recharge using a climate model ensemble and SAR-image based soil parameter distributions — A case study in an intensively-used Mediterranean catchment

We used observed climate data, an ensemble of four GCM–RCM combinations (global and regional climate models) and the water balance model mGROWA to estimate present and future groundwater recharge for the intensively-used Thau lagoon catchment in southern France. In addition to a highly resolved soil map, soil moisture distributions obtained from SAR-images (Synthetic Aperture Radar) were used to derive the spatial distribution of soil parameters covering the full simulation domain. Doing so helped us to assess the impact of different soil parameter sources on the modelled groundwater recharge levels. Groundwater recharge was simulated in monthly time steps using the ensemble approach and analysed in its spatial and temporal variability. The soil parameters originating from both sources led to very similar groundwater recharge rates, proving that soil parameters derived from SAR images may replace traditionally used soil maps in regions where soil maps are sparse or missing. Additionally, we showed that the variance in different GCM–RCMs influences the projected magnitude of future groundwater recharge change significantly more than the variance in the soil parameter distributions derived from the two different sources. For the period between 1950 and 2100, climate change impacts based on the climate model ensemble indicated that overall groundwater recharge will possibly show a low to moderate decrease in the Thau catchment. However, as no clear trend resulted from the ensemble simulations, reliable recommendations for adapting the regional groundwater management to changed available groundwater volumes could not be derived.

Vertical and horizontal distribution of soil parameters in intensive agricultural zone and effect on diffuse nitrogen pollution

Understanding the spatial relationships of diffuse agricultural pollution with soil properties at a sub-basin scale is an innovative way to characterize the pollution yield. The objectives of this study was to identify the interaction of soil parameters with diffuse nitrogen (N) loading at spatial-vertical directions, which provide an optional method to assess the diffuse N pollution. The diffuse N loading of a sub-basin was simulated with the Soil and Water Assessment Tool with the consideration of land use (upland and paddy rice). Soil properties were analyzed in terms of eight parameters by employing a 1.5km-grid soil sampling process at two depths across multiple sub-basins. The spatial soil properties were interpolated for each sub-basin. The spatial distributions of total nitrogen (TN), iron (Fe), manganese (Mn), and cadmium (Cd) exhibited the same pattern at two soil depths, but the vertical patterns of available nitrogen (AN), soil organic carbon (SOC), sulfur (S) and lead (Pb) varied. Overlaying the diffuse N loading and land use distribution, it was found that the sub-basins of upland and paddy rice were primary contributors. The diffuse N discharged from upland sub-basins was about 50% lower than that from paddy rice. The correlation analysis of soil variables with diffuse N indicated that the top layer had a stronger relationship with the diffuse N than the subsurface, which had more direct impact on the pollution. A partial least squares (PLS) regression analysis was employed to identify the key soil variables in surface layer. The analysis indicated that the metal parameters (Fe, Mn, Cd, and Pb) were the important factors for diffuse N loading. However, the TN in the paddy rice was the principal predictor. With the soil property information, the diffuse N yield from the two types of farmlands can be assessed in a simplified way. The developed method in this paper is flexible and efficient, which also is compatible with results of the SWAT model.