JWSS - Journal of Water and Soil Science

The occurrence of wind erosion and the spread of dust particles can be regarded as one of the most important and threatening environmental factors. Climate change and the frequency of droughts have played an important role in exacerbating or weakening these events. The primary objective of the present study was to investigate the trend of changes in four important climatic elements (precipitation, temperature, wind speed and relative humidity) and dust storm index (DSI) in Qazvin city using the Mann-Kendall pre-whitened test and to determine the relationship between them based on the multiple linear regression method. Assessment of the meteorological drought status based on two standardized precipitation index and standardized precipitation, as well as the evapotranspiration index and analysis of their effect on activity level of dust events, was the other objective of this study in the study area. For this purpose, after preparing and processing the climatic data and calculating the dust storm index, the trend of changes and the relationship between climatic parameters and dust events were investigated. The results showed that the changes of trend in the annual precipitation and relative humidity in Qazvin city were increasing, while the trend of annual changes in the wind speed and the mean air temperature was a decreasing one. Investigation of the monthly changes in the dust events also showed that there was a sharp decrease in the occurrence of wind erosion and the spread of domestic dust particles only in July. On a seasonal scale, with the exception of winter that has been reported without trends, in other seasons, the intensity of these events was significantly reduced. The effect of the meteorological drought on wind erosion was estimated to be 11% at the confidence level of 99%. In general, these findings indicate a decreasing trend of land degradation and desertification caused by wind erosion in Qazvin.

Optimal use of water resources seem to be necessary due to climate change and the recent drought conditions. One of the most important and effective management strategies is increasing water productivity in agriculture. Irrigation method and the use of different levels of nitrogen fertilizer are the effective factors in increasing the water productivity. Therefore, this study was conducted to investigate the effect of the irrigation method and nitrogen fertilizer on the harvest index and water productivity of two wheat cultivars with 36 treatments as a split-split plot based on a completely randomized design with three replications in the research farm of Natural Resources and Agricultural Research Center of Kashmar, during the 2018-2019 time period. The treatments were two irrigation methods including end blocked border and drip irrigation (tape) as  the main plots, three levels of the nitrogen fertilizer from urea source including 0, 50 and 100 kg/ha as the  sub plots and two cultivars of wheat including Pishgam and Sirvan as the sub-sub plots. The results showed that by changing the border irrigation method to the drip irrigation (tape) method, the harvest index and water productivity were increasesignificantly. The results also showed that grain yield and its components, including harvest index and water productivity, had no significant difference in 50 and 100 kg/ha nitrogen levels. On the other hand, grain yield and its components, harvest index and water productivity, were significantly higher in the Sirvan cultivar rather than the Pishgam one (P<0.01). According to the results obtained from this study, the drip irrigation method, 50 kg/ha nitrogen level and Sirvan cultivar could be recommended for the study region. 

Identification and investigation of changes in the area under cultivation of various crops seem to be essential for the management supply of crop production. In this study, r to identify and investigate change of the area under cultivation in major crop Hoseynabade Mishmast region in Qom province, we used the time series images of OLI and ETM sensors of landsat 8 and 7satellites, according to the crop calendar of this region. By using the vegetation index (NDVI) in the decision tree algorithm, the thresholds of this index were adjusted according to the major crops of this region; then a map of the cultivation pattern of the crop of this region was prepared. In order to evaluate the results, the statistics of the provinces agricultural jihad were used during 2005, 2009, 2014 and 2019 crop years. The results showed that by using the threshold of NDVI index, crops in this region in 2005 included wheat and barley and alfalfa, and their areas had an error of 17/1 and 6/1 percent in comparison with the statistics of agricultural Jihad, respectively; in 2009, wheat and barley, alfalfa and corn had an error of 0/5, 9/6 and 0/1 percent. Also, in 2014, wheat and barley, alfalfa, corn and sophie crops had an error equal to 4/9, 0.4, 11/4 and 2/4 percent, and the same crops in 2019 had an error 0/04, 11/6, 1/4 and 17/5 percent; that error was not significant. According to the results, the appropriate efficiency NDVI index in estimating crop cultivation area was determined by their phenology. Also, in 2009 and 2014, corn and sophie crops were added to the regions crops, and the area under crops cultivation in 2019 was increased, as compared to 2014.

Several indices are used for drought identification and quantification. In this paper, the new Standardized Palmer Drought index (SPDI) was introduced and then the drought condition of Chaharmahal-Va-Bakhtiari Province was studied using this index. For this study, 11 synoptic, climatology, and evaporation meteorology stations were selected. Essential information in this investigation includes monthly temperature, monthly precipitation, and soil moisture measurement. To estimate SPDI, moisture departure, was first calculated on a monthly time scale. Then, converted to cumulative moisture departure values in different time scales including 3, 6, 9, 12, and 24 months. The best statistical distribution (GEV) was then fitted to cumulative departure. These values were then standardized to have the SPDI. The results showed that, as soil moisture affects SPDI estimation, it will be more valid for analyzing and monitoring drought conditions, especially for agricultural drought. Also, the results showed that 2000, 2001, and 2008 years were the driest time in this Province from 1988 to 2012. Moreover, drought frequency was found out in the western half of the Province more than in the other parts.

The contact angle is a numerical index of differentiation between hydrophilic and hydrophobic soils. The objectives of this research are: 1) assessing different methods such as capillary rise, the molarity of ethanol droplet, repellency index, and sessile drop, and 2) Determining the most efficient method in a typical soil with sandy loam texture. In this study, hydrophobic soil was hydrophobized artificially using stearic acid and according to the water drop penetration time classification method. Calculated contact angles of hydrophilic soil with capillary rise method, the molarity of ethanol droplet method, repellency index (two methods of calculation), and sessile drop method were 89.9, 75.41, (57.81), 56.28, and 58.91, respectively. Using the contact angle measuring device, the contact angle of five hydrophobic levels were 58.91, 104.92, 120.48, 129.96, and 173.07, respectively. According to the precession of the device where the operator is capable to control data and processes and there is no limitation in usage, therefore, the sessile drop method is the most suitable method to measure contact angle. The contact angle of the late method and water drop penetration data are positively correlated (R² = 0.975).

The upcoming climate change has become a serious concern for the human society. These changes, caused and aggravated by the industrial activities of the international community and the increase in the concentration of greenhouse gases in the atmosphere, are seen as a threat to the food security and environment. Temperature change and precipitation are studied in the form of different probabilistic scenarios in order to have an outlook for the future. The present study was conducted to address the effects of climate changes on temperature and precipitation in Qazvin plain in the form of five AOGCMs including Hadcm3, CSIRO-MK3, GFDL, CGCM3 and MICROC3.2, and 3 greenhouse gas emission scenarios of A1B, A2 and B1, based on different possible scenario combinations in the next 30 years, 2021-2050 and 2051-2080 (near and far future). On basis of the study results, all 4 target stations, on average, will have experienced a change between two ratios of 0.5 and 1.4 of  the observed precipitation period  by the end of 2050, and the mean temperature will have had a change  between -0.1 to 1.6 °C, relative to the observed period.  By the end of 2080,  the  precipitation will also have fluctuated between the two proportions of 0.5 and 1.7 times of the observed precipitation period and the mean temperature will touch an increase between 0.6 and 2.6 °C. Both SPI and SPEI indices suggest the increment in the number of dry periods in the near and far future. However, the total number of negative sequences differed considering the 3, 12 and 24-month intervals at the stations level. Given the SPEI index, as compared to the base period, the total negative sequences of drought and number of dry periods will increase at 3 stations of Avaj, Bagh-Kowsar and Shahid-rajaei-powerhouse and decrease at Qazvin station in the future; however, SPI gives different results, such that  for Bagh-Kowsar, there will be an increase in both total negative sequences of drought and number of dry periods, as  compared to the baseline period; three other stations will have more dry periods, specifically, but less total negative sequences. The results reported that the drought events would become severe, and the wet events would become extreme in the future.

Snow cover is one of the important climatic elements based on which climate change may have a special effect. In general, climate change may be reflected in different climatic elements. Therefore, it is very important to study and measure changes in snow level as one of the important sources of water supply. Ardebil and Sarein cities are located at 48° 18׳ east longitude and 38° 15׳ north latitude. In this study, Sentinel-2 optical satellite was used to monitor the snow cover surface in 2018, and NDVI, S3, NWDI, NDSI, Cloud mask indices were applied to detect snow-covered surfaces using ArcGIS and Snap software. Next, to validate the snow maps extracted from the images, it was compared with the snow data in terrestrial stations using linear regression in MATLAB software and to evaluate the accuracy of the model statistical indices including RMSE, MSE, BIAS, CORR were used. The present study showed that according to Ardabil city climatic conditions, maximum-snow covered area in January with an area of 356.52 km2 and minimum snow-covered area in March with an area of 96.10 km2. The highest snow cover is observed in the high slope areas in the western slopes (Sabalan Mountain Heights) and the lowest snow cover is observed in the lower eastern slopes. The results of linear regression with generalization coefficient are 85% and the results of statistical indices of error are equal to MSE: 0.086, BASAS: 0.165, CORR: 0.924, and RMSE: 0.03. Correlation relationships between terrestrial data and estimated snow maps showed a high degree of correlation. This result is statistically significant at the 99% level. The use of optical images in estimating snow levels is very cost-effective due to the size of the areas and the high cost of installing snowmobiles. The results obtained in the present study indicated that traditional radar images with high spatial resolution and good correlation with terrestrial data can be a good alternative to snowmobiling ground stations at high altitudes or in passable areas.

Drought is one of the most complex natural disasters due to its slow onset and long-term impact. Today, the use of remote sensing techniques and satellite imagery has been considered a useful tool for monitoring agricultural drought. The objective of the present study was to evaluate spatial and temporal monitoring of agricultural drought in the lake Urmia catchment area with the ETDI drought index which is calculated from Nova satellite images based on actual evapotranspiration from the SEBS algorithm and compared with the ground index SPI. For this purpose, 248 AVHRR sensor images and NOAA satellites during the statistical period of 1998-2000 and 17 meteorological stations with a statistical period of 30 years were used to calculate the indicators. To determine agricultural lands, six thousand points were marked for different uses and their actual evapotranspiration was calculated using the SEBS algorithm. The results showed that with the onset of the drought period in 1998, the ETDI index indicated 9.4% in weak drought conditions in May and 90.6% in normal conditions. Over time, in June of 1998, the situation was different with 95% in a weak drought situation and 5% in a normal situation for the city of Tabriz. In July, the entire catchment area experiences a slight drought. Then, in August, 84% of the basin is in normal condition and 16% in Tabriz and Urmia are declared weak drought. It was also founded that the ETDI drought index due to the combination of visible and infrared bands and its combination with terrestrial data has a physical meaning and has high certainty and predicts drought faster and more accurately.

In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R², MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.

In precision agriculture, a productivity rating system is a significant tool to quantitatively assess soil quality. An experiment was conducted in Bilavar, Kermanshah to evaluate the spatial variability of physical indicators of soil quality of a rapeseed (Brassica napus) field. Spatial variability analysis of soil physical properties measured on a rectangular grid (100 m×100 m) was carried out using a geostatistical analyst extension of Arc-GIS software. Five physical soil quality indicators including bulk density (BD), non-capillary porosity (NCP), field saturated hydraulic conductivity (Ks), available water retention capacity (AWC), and organic carbon (OC) were determined. The physical rating index (PRI) at each sampling point was determined by multiplying the rating values for all five parameters. Results revealed that major ranges of semivariogram for Ks and AWC varied between 137-145 m and for BD, OC, and NCP they were relatively long (161-205 m). Clay and NCP showed moderate spatial dependence (0.68 and 0.28, respectively) whereas the rest of the parameters showed weak spatial dependence. Also, the correlation between PRI and the biological yield of rapeseed was fairly good (R²=0.68). Investigation of zoning maps of soil physical properties showed an increase in BD and a decrease in AWC and NCP parameters depending on changes in soil texture and organic matter content in some parts of the field. In general, the PRI index is an important tool in the quantitative assessment of soil physical conditions, and based on it and zoning maps can improve the physical quality of soil in agricultural fields.

The ability of remote sensing (RS) in irrigation scheduling has been accepted in the world due to the collection of data on a large scale and the determination of water stress indicators with greater speed and less cost. Crop Water Stress Index (CWSI) and Water Deficit Index (WDI) are components of the most recognized water stress indices. Despite the accuracy and precision of the CWSI index that has been proven in plant irrigation scheduling, the lack of complete density of vegetation, especially in the early stages of growth, is one of the most important defects of using this method in crop irrigation scheduling. While estimating the water deficit index using remote sensing technology does not have these limitations. An experiment was performed in the crop year 98-99 in the city of Karaj to check the accuracy of this index. The amount of WDI and CWSI in a wheat field with optimized irrigation management was determined and compared and evaluated using statistical parameters. The results showed that the coefficient of explanation between these two indicators in the months of April, May, and June is 0.77, 0.85, and 0.71, respectively.

Water quality characteristics play a crucial role in water resources management, watershed health assessment, and implementing effective management strategies. The objective of this research was to present an overall assessment of the surface water quality in the Gorganrood River Basin to be utilized for developing effective watershed management plans and programs. Various physicochemical water quality data including main anions and cations, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Sodium Absorption Ratio (SAR), pH, and total hardness recorded at 25 hydrometric stations across the basin were analyzed and assessed with the Canadian (CCME) Water Quality Index. The mean water quality index for drinking, agriculture, and industrial purposes indicated that headwaters and higher areas generally exhibited better water quality compared to the downstream areas of the basin. Geochemical processes and the introduction of various pollutants during water flow from the headwaters to the basin outlet contribute to a decline in water quality. The highest water quality was observed in the Kabudval and Shirabad stations, whereas the Baghesalian station exhibited the lowest. For drinking water use, hardness, bicarbonate, and chloride were identified as variables contributing to water quality decline in the headwaters and upstream areas. However, these areas predominantly maintained a moderate to good quality for drinking purposes. Conversely, downstream areas experienced a significant deterioration in water quality with higher pollutant levels such as total dissolved solids (TDS), sulfate, and sodium, resulting in relatively poor to poor conditions. Approximately 60% of the stations in the basin had excellent water quality for agricultural use, with no limiting factors. Only three stations near the basin's outlet exhibited relatively poor to poor water quality due to elevated chloride levels, sodium adsorption ratio (SAR), and electrical conductivity. only 28% of hydrometric stations demonstrated good water quality for industrial use. Hardness, pH, and TDS are the main variables contributing to water quality decline for industrial use in the upstream, while downstream areas are impacted by chloride and sulfate. The outcomes of this study hold significant implications for effective water resources management, watershed preservation, and natural resource conservation in the Gorganrood basin. From industry and especially health aspects, however, more detailed investigations are needed, taking into account some other important variables of water quality (including nitrate, total coliform, fecal coliform, etc.).