partial least squares regression

Wind erosion and dust phenomenon are considered as one of the important processes of land degradation and a serious challenge in Iran. This phenomenon occurs due to the interaction between weather and terrestrial processes. The purpose of this research is to investigate and analyze the trend of temporal and spatial changes in the number of dusty days in Iran and to investigate the role of climate in its spread.

In this research, after receiving hourly and daily statistics of dust events (through 148 synoptic and climatology stations) and parameters of precipitation, evaporation, temperature as key climatic elements (from 171 stations) in 22 provinces located in arid and semi-arid regions of Iran, The spread of dust was investigated and the correlation between climate and dust was determined. After calculating the number of dust days in Iran, the information was called in the GIS environment and a map of the spatial changes of the parameter of the number of dust days in the three decades of 1987-1996, 1997-2006, 2007-2016 was prepared. In the following, the spatial analysis of this parameter and its expansion over three decades were investigated. Next, the climate parameter that had the highest correlation with the number of dusty days was selected and after preparing a map of its spatial changes in the GIS software environment, the spatial compatibility of the desired parameter with the expansion of the number of dusty days was investigated. In the next step, after performing the homogeneity test on the dust occurrences, the partial square regression model was used to determine the contribution of the three mentioned climatic elements as independent variables and the number of dust days as a dependent variable.

The results showed that the number of dusty days and the area of its occurrence have increased in Iran, so that in the last decade under study (2007-2016) compared to the previous two decades, the dustiest event occurred in Iran. This increase has been more intense especially in the western and southwestern regions of the country, which were also affected by extra-local dust events. These dust events have spread to the central regions of the country with greater frequency. The homogeneity test in many provinces showed a common change point (year 2007-2008) in the number of dusty days, which could be related to the beginning of severe droughts and climate changes from this year in Iran. The study of climatic parameters (temperature, precipitation and evaporation) on changes in the number of dust days in Iran showed that during recent years, temperature had the highest significant correlation level (0.58) with the occurrence of dust. The temperature gradient map in the last decade (2007-2016) compared to the previous decade (1997-2006) also showed the increasing trend of temperature, especially in the western and southwestern regions as well as the southeastern regions of Iran. Modeling the effect of climatic parameters of temperature, precipitation and evaporation on the parameter of the number of dusty days showed that the contribution of the studied climatic parameters to the occurrence of dusty days is about 33% and other effects can be related to things such as human factors or soil characteristics, type of vegetation, solar radiation, etc.

In general, the results of examining the trend of changes in the number of dust days and climatic indicators for nearly 3 decades indicate a significant increase in the number of dust days in most parts of Iran and the conditions for this increase have been provided and if the trend of changing these indicators in the coming years is according to the current trend, the risk of environmental problems in the country can be expected to increase.

Knowledge about soil texture is very important in agricultural studies due to its direct impact on other soil properties. However, determining the soil texture in vast areas requires a lot of time and money. For this reason, researchers are looking for ways to determine this important feature of the soil on a large scale. One of these methods is the use of surface soil spectrometry. In this method, the choice of calibration method significantly affects the accuracy of measuring the characteristics of the surface. In this study, the performance of two regression techniques, namely, partial least-squares regression (PLSR), principal component regression (PCR) were compared to identify the best method to assess sand, silt and clay. For this purpose, 50 soil samples from Tehran province were collected and used as a data set for Calibration and Validation. Soil samples with different moisture levels (oven dry, 5, 10, 15 and 20 w/w) were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The spectra were subjected to three pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), Normalization with SG smoothing (Normal-SG). The R2 results from cross-validation indicated that the PLSR model had a better performance than PCR. Normal + SG pre-processing method for clay loam texture and SG method for sandy clay loam texture showed better estimation of measured properties. The amount of R2 for clay was 0.74, 0.81, 0.97 and 0.87, respectively, in moisture content of oven dry, 5, 15 and 20% in   clay loam texture And 0.95 and 0.61 at oven dry and 5% levels in sandy clay loam. Silt was better predicted by R2 0.67 in moisture content of 5% in clay loam texture and R2 0.97 in moisture content of 20% at sandy clay loam texture. Sand was also predicted (R2= 0.86 and 0.72) in moisture content of 5 and 10% in clay loam texture.

Landscape is one of the main factors influencing hydrological processes of the watershed. Changes in structure and spatial pattern of land use play important role in surface runoff and sediment yield. Determining the relationship between landscape patterns and hydrological processes can be used as an indicator of watershed soil erosion and sediment yield. Therefore, due to the problems in field measurement of sediment yield, its estimation using landscape properties and land use pattern is an appropriate alternative for current estimation methods. The purpose of this research is to determine the relationship between watershed sediment yield and landscape metrics in the selected sub-watersheds of Golestan Province. To this end, suspended sediment concentration data for all hydrometric stations of the studied province were obtained from the relevant resources and appropriate sub-watersheds were selected. Then, using the land use map of Golestan Province, 15 landscape metrics related to sediment yield were determined for different land uses by Fragstats 4.2 software. In order to determine the relationship between watershed sediment yield and landscape metrics, a partial least squares regression was used which combines the methods of principal component analysis and multiple linear regression. The relative importance of landscape metrics was determined through examining the values of Variable Importance for the Projection (VIP) and Regression Coefficients (RCs). The results of this study indicated that the watershed sediment yield is densely associated with land use patterns. The main indices in reducing sediment yield were the Largest Patch Index (LPI), the average of the nearest neighbor distance (ENN-MN) and the average of perimeter-area ratio (PARA –MN) with values of VIPs of 1.296, 1.184 and 1.747,  and regression coefficients of -0.014, -0.039, and -0.002, respectively. The main indices in incrising sediment yield were Landscape Shape Index (LSI) and mean patch size (AREA-MN) with regression coefficients of 0.020 and 0.017, respectively. The landscape characteristics in watersheds could account for as much as 71% of the variation in sediment yield of watershed. The results of study showed that the landscape characteristics can be used for watershed sediment yield modeling.