spatial variability

Mechanical properties of soil, such as shear strength and penetration resistance, play a crucial role in optimizing crop productivity and proper soil management. The objective of the research was to produce digital map of soil shear strength and penetration resistance in Kielaneh watershed, located in Kurdistan Province, covering an area of 12,000 hectares using Gradient Boosted Decision Trees (XGBoost), Random Forest (RF), and k-Nearest Neighbors (KNN). Soil penetration resistance and shear strength were measured using handheld penetrometers and vane shear devices at 150 observation points from the surface soil layer (0 to 10 centimeters). Spectral data and auxiliary variables derived from the Digital Elevation Model and Sentinel-2 satellite images were used to predict soil shear strength and penetration resistance. These variables include CHND, VD, RSP, CHNBL, Brightness, WE, NDVI, Band12, Greenness, PLC, as well as soil parameters such as organic matter, calcium carbonate, bulk density, geometric mean particle size, soil texture (percentages of clay, sand, silt), and visible near-infrared spectral data as latent variable (LT), representing soil formation factors. The results showed that the XGBoost had higher accuracy compared to other models for predicting shear strength in surface soil layer with an (R2) of 0.61 and an nRMSE of 0.16, as well as for predicting penetration resistance in the surface soil layer with an (R2) of 0.60 and an nRMSE of 0.11. In conclusion, the XGBoost model, using spectral data along with topographic variables and soil parameters, was able to estimate the spatial variability of soil mechanical properties with acceptable accuracy in the study area. The generated maps can be used to make necessary management decisions regarding of the region.

The rainfall system of a major part of Iran is mediterranean, where the precipitation amount during the vegetation period is low. In addition, the occurrence of precipitation in the non-vegetation period or beginning of the vegetation period, which does not cover the surface of the earth well, is one of the important reasons for water erosion in Iran. Since vegetation has a special role in soil erosion control and runoff retention, any change in the vegetation structure and pattern, which expresses the landscape pattern and function, can have a significant effect on changing hydrological processes. Therefore, the assessment of soil and water loss and the quantification of its relationship with landscape metrics provide key information for the development of water and soil quality management strategies.

The current research was conducted to investigate the hydrological component changes with landscape metrics on 2 m2 plots using simulated rainfall at an intensity of 32 mm.h-1 in a part of rangelands of Ardabil County. At first, considering the type and percentage of vegetation as the main variable, eight groups of vegetation composition along with one group without vegetation (control) were considered with three replications. The composition (and percentage) of the vegetation from the first to the eighth groups, respectively, include low-height graminea predominance (45), the composition of dense bushes with graminea (43), bushes with low-height and medium-distribution (37), sparse bushes mostly with low and medium height (31), the composition of sparse bushes with graminea (56), dense bushes in upper parts (54), low-height bushes with very low distribution (15), and dense bushes with almost uniform distribution (56). After measuring the runoff and sediment at the plot outlets, different hydrological components were calculated. Then, plots with nine different vegetation combinations were imaged in three replicates before and after rainfall simulation. After transferring the images prepared from the plots to the Arc/Map10.8 environment, nine important landscape metrics were calculated.

Changes in the mean patch density (4.43-26.90), largest patch index (54.16-86.75), edge density (17.12-107.38), landscape shape index (1.50-4.47), mean shape area (4.16-37.46), mean Euclidean nearest neighbor distance (0.00-1.65), landscape division index (0.19-2.31), mean patch shape index (1.24-22.85), and the effective mesh size (15.80-43.96) indicate their different influence from different percentage and composition of vegetation cover. Spearman's correlation matrix analysis showed a nonsignificant relationship between the mean soil loss, runoff volume, runoff coefficient, and sediment concentration with landscape metrics (r<0.26 and p-value>0.10). The small scale of the studied plots, the lack of diversity in the vegetation composition, and the uniformity in terms of vegetation height can be cited as the reasons for the lack of correlation. In general, groups with vegetation values above 50% had a better condition in terms of LPI, AREA_MN, and MESH, which indicates more connectivity and less degradation. The increase in vegetation cover and spatial heterogeneity above the landscape surface can change the path of sediment transport, reduce sediment connectivity, and lead to a decrease in sedimentation.

The obtained results are applicable in explaining the appropriate reference to optimize water and soil protection measures on the watershed scale. However, It is suggested that similar and more comprehensive research be done in different scales of erosion plots and even in the landscape (slope) scale so that by considering a wide range of vegetation, topography, climatic conditions, as well as successive rains, it is possible to compare the results, optimum selection of study scale, and finally planning to manage and protect vegetation and water and soil resources.

It is predicted that the water scarcity will rise in the coming years due to a population growth, increased pollution sources, climate change and occurrence of droughts can ultimately affect the amount of available water resources. A global cooperation and foresight and joint planning of water resources is necessary to resolve the water crisis. In recent years, development and designing new multidimensional and comprehensive indices seems to be the most important issue for assessment of the current condition and predicting the future trend of surface and ground water resources changes. Towards this attempt, the Water Poverty Index (WPI) is recommended as a multi-dimensional indicator to assess the availability of surface water resources. Some applications of the WPI is highlighted at different countries and also its calculation steps is demonstrated through different criteria. According to the results, the WPI is derived based on the weighted combination of five components in different studies. Main WPI components are: resources (availability, seasonal and inter-annual variability and water quality), access (accessibility of water for effective use), capacity (managing water resources based on socio-economic variables), use (level of water use by different sectors), and environment (environmental impact of water management on ecological integrity). According to multidimensional nature of Water Poverty Index along with considering all affecting factors on water scarcity and availability behind social and economic characteristics can be a useful tool in prioritizing critical areas can be considered as an effective step in optimal planning of water resources. Water Poverty Index is a combination of influencing factors on water stress and scarcity, which provide a basis to prioritize and specifying management options for different watersheds. It should be noted that determination and analysis of water scarcity in different areas is depend on the condition of water resources, data availability and the employed sub-criteria in the calculation of water poverty index.

The aim of present study was performed for study of spatial variation of Artemisia aucheri yield, density and canopy cover and its spatial relationship with soil properties and topographical factors in Baladeh semi-steppe rangelands, Noor. For this reason, 132 sample points were carried out within the form of a network of 550 × 600 m with the systematic method by the plots in 50 m through transects. Soil samples were taken in depth of 0 to 20 cm and the samples elevation, latitude and longitude were registered by GPS device and the slope percent was measured using a slope meter. To determine the yield, density and canopy cover percentage of Artemisia aucheri was used plots of 4 m2. The data normality was determined using Kolmogorov–Smirnov test and correlation of data using Pearson correlation coefficient using SPSS software. To perform of geostatictics analyses and provide of spatial distribution map were used GS and Arc/GIS softwares. According to spatial correlation analysis, among soil properties and topographical factors, soil organic matter and clay showed, a more strong space structure with the study species, Interpolation and obtained maps for spatial distribution of Artemisia aucheri was performed using Kriging, Cokriging and inverse distance weighting methods. In this study, except density, yield and canopy cover of Artemisia aucheri showed a weak space structure using Kriging. This result shows that this method were not validated for spatial variability of yield and canopy cover of Artemisia aucheri but were obtained a strong space structure using Cokriging methods for studying variables.

The spatial pattern of soil properties can be a key element in shaping water and nutrient flows in ecosystems and its identification is relevant for understanding soil processes in these areas, and then establishing adequate management practices. One of the factors affecting soil erosion is aggregate stability and its change. If factors affecting aggregate stability and soil aggregation are determined and also appropriate method and spatial variability index is characterized, it can overcome the limitation in this context. To this end, an investigation on the part of Taleghan watershed with area of approximately 3200 hectare and mainly bedrock marl of tertiary including rock units of gy (red mudstone and gypsum silt) and Ngm (red mudstone and silt) was conducted. Aggregate stability and factors affecting soil aggregation was measured using Le Bissonnais method and Pearson correlation test respectively. Spatial variability of aggregate stability and the factors affecting it was studied using kriging method. In classical statistic part, results showed that soil physico-chemical properties including clay, fine sand, very fine sand contents, moisture saturation, Caco3 and organic matter percentages are effective on aggregate stability in studied area. Geostatistical results also showed that spatial dependence of most of the factors affecting soil aggregation are weak to moderate, so that only measurement error of organic matter is low, which implies high dependencyand low random variance of variable distribution in studied soils. This means that organic matter content in samples close together is very similar and is very different from the ones farther. Aggregate stability indices are anisotropic and influence range min and max are from 740 to 2500 and 3250 to 5940 meters respectively.

Geostatistic has both interpretation (variogram) and estimation (i.e. Kriging) tools for studying spatial variability of soil properties. The analysis of dependence is based on the structure of the semivariogram, which demonstrates the existence of spatial dependence is increasingly used in the assessment of spatial variability in agronomy. In this study, determined spatial variability of soil chemical and physical properties includes, texture, pH, soil organic Carbon, CaCO3, total nitrogen, C:N ratio and Electrical conductivity and was surveyed by using of geostatistics. Results showed that all of the studied properties except EC and total N, showed structural spatial variations. Ordinary Kriging as the best estimator used to interpolation of properties. According to the map and variogram analysis showed that the amount of organic carbon and carbon to nitrogen ratio affected by land use type and management practice such as irrigation and surface plowing of afforest area. Variation of The other properties was affected by soil natural condition effects. These results suggest that Kriged maps can be used for planning and Management Park.

Planning and suitable management is necessary for optimal use of soil and for this; spatial variability of soil characteristics is important which may be edcarried out through geostatistical methods of parametric and non-parametric predictors such as TPSS, WMA, Kriging and Co-kriging. This research work was done in Southern part of Uromieh plain with 36690 ha surface area in order to study the spatial variability of soil lime, sand and saturation moisture percentage. Distance between soil profiles ranged 1300 to 4700 meters. For estimation and prediction of them in non-sampled points, the Kriging, Co- kriging and Weighted Moving Average were used in Geographic Information System environment. For selecting suitable interpolation method, Cross validation and MAE and MBE parameters were used. Selected method was also used for estimating and mapping of the selected soil characteristics. The Sturges rule was used for defining map classification. Results showed that the Kriging method has the highest accuracy with correlation coefficient of 0.83 and error of 3.98 percent for prediction of soil characteristics in non-sampled points.