فصلنامه حفاظت منابع آب و خاک
سال یازدهم شماره 2 (پیاپی 42، زمستان 1400)

Evapotranspiration as one of the main components of the hydrological cycle, has a significant role in proper irrigation planning and water resources management. In this case, estimating evapotranspiration is limited due to a lack of data and a deficiency of meteorological stations. Therefore, today numerical models such as WRF are a powerful tool for generating and predicting meteorological quantities (wind speed, humidity, etc.) that are needed to estimate evapotranspiration. So far, no research has been conducted to investigate the effect of different schemes of the WRF model on the estimate of rice evapotranspiration. The purpose of this study is to evaluate the efficiency of the WRF model and obtain the result for estimating evaporation for rice plant in the central plain of Guilan.

Evapotranspiration rates vary from 2.7 to 8.5 mm per day. The average ET during three different periods of plant growth, including the initial, middle, and final periods, is estimated to be 4.63, 5.97, and 5.98 mm per day, respectively. The three configurations 1, 2, and 4 are mainly overestimated in predicting evapotranspiration of rice plants, and the computational values are estimated to be higher than the values measured by the lysimeter. The results show that the highest amount of RMSE occurred in configuration No. 4 at 8.47 and the lowest rate occurred in configuration No. 3 at 1.26. Summary of results shows that configuration No. 3 in all four criteria mentioned has performed better than other configurations to predict daily evapotranspiration of rice. The results showed that the non-local schema used in the model, simulates better than the local schemas for the daily evapotranspiration of the rice plant. Findings show that in the local YSU schema, the accuracy of predictions is significantly increased and is only 0.64 mm on average less than the estimated lysimetric data.

The results showed that using appropriate schemas in the surface layer and boundary layer of the WRF model, affects on accuracy of evapotranspiration predictions. The results of this study showed that, this model by using the YSU non-local boundary layer scheme can accurately predict the evapotranspiration rates of the rice plant for the next day and this is due to the higher ability of this schema in predicting the parameters affecting evapotranspiration (including temperature and wind). Therefore, the WRF model can be implemented by using GFS forecast data for the next few days and by applying the FAO-Penman-Monteith equations to the model outputs, the values of potential evapotranspiration for different regions of the country can be calculated. Since evapotranspiration is directly related to atmospheric thermodynamic processes, so using other different atmospheric physics schemas (not considered in this study) can produce different results.

Soil salinity and alkalinity are one of the most important destructive soil processes, especially in arid and semi-arid regions. Sodium soils are faced with degradation of soil structure, reduction of permeability and speed of water movement in the soil, runoff increasing, decrease of land use, ventilation decreasing, and finally, decrease of crop yield. Many researches have been conducted based on physical, chemical, and biological methods to improve the saline and sodic soils, but the hybrid method is the most effective in the improvement of these soils. In this study, the effect of two organic amenders (Potassium Humate and Chicken Feather-CF) with leaching in improving some physical and chemical properties of saline and sodic soils has been investigated.

In this research, a factorial experiment in a completely randomized design with 16 treatments and 3 replications was conducted. The first factor was the type of amender (Potassium Humate and Chicken Feather), the second factor was the amount of amender (0.75 and 1.5 Weight percentage) and the third factor was leaching levels (no leaching, 45, 90, and 135 days of incubation). After sampling from depth 0 to 30 cm and measuring some physical and chemical properties of the soil samples, the amenders are mixed with the soil and the soil moisture content was delivered to the field capacity (0.7-0.8 FC). Then the soil samples were poured into the columns. Soil columns were leached at three intervals of 1.5 months with the amount of one pore volume (P.V). At each leaching stage, the properties such as soil acidity (pH), electrical conductivity (ECe) and sodium adsorption ratio (SAR) in drainage water (DW) and pH, EC, SAR, cation exchange capacity (CEC), exchangeable sodium percentage (ESP), wet aggregate stability (WAS), organic carbon (OC) and hydraulic saturation conductivity (Ks) were measured in soil columns. Statistical analyzes and the comparison of means were performed by LSD test and using SPSS17 software. The graphs were drawn with EXCEL software.

The results showed that Potassium Humate reduced the pH of the soil and increased the pH of DW. This effect was more as the amender’s weight and leaching increased. Also, Potassium Humate reduced the ECe of soil and this effect was more as the leaching increased. The findings showed that the high amount of amenders led to the high SAR in DW. As the leaching stage increased, the SAR of soil decreased in both amenders. The 1.5% of amenders amount had more effect on the reduction of SAR than the level of 0.75% in both amenders. With increasing leaching, the CEC of soil decreased. The highest CEC (31.2 cmolc.kg-1) was obtained in the CF with 1.5% of the amender’s amount. When the number of amenders increased, the CEC of soil increased. Also, when the leaching and the amount of CF increased, the ESP of soil decreased.The results showed that with increasing the leaching in Potassium Humate, the % OC of soil decreased. The WAS increased with increasing the amount of CF. The highest amount of WAS was in CF (1.5%). Potassium Humate in high amounts (1.5 %) reduced Ks. Ks decreased with increasing leaching from the second leaching stage onwards.

In general, it can be concluded that the addition of amenders increases the pH and SAR in the DW and decreases the pH (from 8.24 to 7.39), ECe (from 17.07 to 0.8 dS.m-1) and SAR (from 19.34 to 11.1 (cmolc.l-1)0.5) in the soil. Leaching increased pH and ECe in DW and decreased CEC, ESP in soil and decreased KS by increasing leaching stages after the second leaching stage. Potassium Humate reduced OC%, KS and Chicken Feather increased CEC and WAS and decreased ESP in soil. In general, Chicken Feather (with 0.75%) along with leaching is recommended to modify the saline and sodic soils.

The use of agricultural and industrial wastes as a organic materials are increased. In order to evaluate of the effect of sawdust and nitrogen on soil quality indices under bean cultivation, an experiment was conducted on split plot with RCBD design with three replications. Sawdust in 4 levels 0, 5, 10 and 15 ton/ha as a main factor and nitrogen fertilizer in 3 levels 75, 150 and 225 kg/ha. and then mixed with soil. In the use of nitrogen,the maximum grain yield was 1302.8 kg/ha were obtained of 225 kg/ha of urea application. In traits of soil properties, sawdust in all traits and nitrogen fertilizer on some traits had meaningful effects. With increasing of sawdust, all of trait increased, but the increasing of traits with increasing of nitrogen showed some of traits. The investigating of rates of sawdust on MWDwet showed the difference between 5 and 10 ton/ha of sawdust was not significant, but by applying of 15 ton/ha difference and was significant. The maximum effect of MWDwet was 0.84 mm were obtained of 15 ton/ha of sawdust, in compared with 10 ton/ ha and control increasing was 100 and 265% respectively. This research indicates that sawdust as an organic conditioner and nitrogen fertilizer improved the quality of soil and the use of this treatments increased porosity, water holding capacity in FC and PWP points and organic carbon. Thus, sawdust can be used as a suitable organic fertilizer to improve the organic matter of soil and unsuitable condition of soil.

Climate Climate change is one of the important factors that will affect different parts of human life on the planet and will have detrimental effects on the environment, socio-economic, and especially water resources. Knowledge of climate change can provide comprehensive plans in various areas of management regarding the monitoring of droughts and their potential risks. Drought can occur in any area, even wetlands. This phenomenon depends on various factors and parameters and one of the most important symbols of this phenomenon is the occurrence of drought is a decrease in rainfall and therefore the analysis of precipitation data is of special importance to study drought. The purpose of this study is to analyze drought variables using SPI and SPImod indices and detailed functions.

 In this study, to model the multivariate analysis of drought in Lake Urmia basin using RCP8.5 and RCP4.5 representative concentration pathway scenarios, data and models of atmospheric circulation of historical data (1991-2010) for three near horizons (2030- 2011), medium (2065-2046) and round (2099-2080) were simulated and produced. Then, using SPImod index and copula functions, drought multivariate analysis was performed in MATLAB software environment. In general, first, using the mentioned indicators (two indicators, SPI and SPImod), the characteristics of drought intensity and duration were extracted, then, using coding in MATLAB software environment, eight families of Archimedean detailed functions were used.

The results of multivariate analysis showed that the Joe copula function is the best copula function for drought multivariate analysis (For analysis of both severity and duration of drought for the study area). Also, the results of probability and the joint return period showed that in the coming periods, at least droughts of the same level as historical droughts and even more severe will occur. Thus, by studying the period of combined and conditional returns and Kendall, the results showed that at a certain critical probability level, the amount of Kendall return period is much more than the standard return period, so that this difference increases with increasing that certain amount.

The results obtained with the climate change approach on the meteorological drought of Lake Urmia showed that in the coming periods we will see an increase in temperature, which will affect the rate of trade in the region and water resources, on the other hand, because the data Meteorology and hydrology are used to calculate the types of droughts, so droughts affected by climate change will be so that in future periods 46% to 48% of the months will be dry in different horizons. Finally, the results of the time series of indicators showed that during the statistical period at least 40% of the months were dry and this intensity of droughts in the Urmia station is much higher than others. The modified SPI largely eliminates the disadvantages of conventional SPIs and takes into account seasonal variations in precipitation in the calculation of the SPI index.

The effect of various biochars and nano-clay on Cd immobilization and uptake by plants has been widely studied, but few studies have focused on the migration of different Cd fractions in saline-alkaline soils. Moreover, the remediation potential of biochar and nano-clay in saline soils polluted with heavy metals is still to be studied. Therefore, it was hypothesized that the Cd movement is influenced by biochar and nano-clay application in soil contaminated with the matter. The aim of the present study was to evaluate the effects of biochar and zeolite on the uptake of cadmium by green bell pepper (Capsicum annuum) and its downward movement in saline-alkaline and cadmium-contaminated soils.

The present study was carried out under two soil modification materials including wheat straw biochar (Triticum ( and zeolite nanoparticles at the level of 5 g/kg of cadmium-contaminated soil. The soil was collected at 0 to 30 cm depth from a plain in Sejzi, Esfahan. The soil was air-dried and stones, as well as plant litter, were removed, and then the soil was passed through a 5 mm sieve and prepared for the experiment. The heavy metal contaminated soil was created by placing 500 g air-dried soil into a 2 L glass beaker and mixing it with 250 mL cadmium nitrate (1.2 g Cd, Cd(NO3)2· 4H2O). In the Sejzi plain area, three plots were filled with biochar and zeolite at the level of 5 g/kg of cadmium-contaminated soil. After preparing the contaminated soils, green bell peppers (Capsicum annuum) were planted in them under natural conditions. 

The results showed that biomass of green bell pepper increased significantly by 79.2% and 18.3% using biochar and zeolite, respectively. The concentration of cadmium in green bell pepper’s fruit in biochar application was reduced by almost 30% compared to both control and soil treatments with zeolite. Cadmium absorbed by green bell pepper stems was about 50% of the total plant cadmium. Application of 5 gr of biochar and zeolite per kg of soil increased 42% and 78% of soil cadmium in topsoil (0-12 cm), respectively, compared to the subsoil.

According to the results, it can be stated that green bell pepper can be introduced as a cadmium absorber. The results also show the superiority of zeolite treatment over biochar treatment in reducing contamination transfer to the underlying layers of saline and alkaline soils and the addition of biochar caused a greater increase in green bell pepper biomass compared to zeolite nanoparticles in cadmium-contaminated soil.

Food waste is one of the biggest challenges in today’s modern world and leads to greenhouse gas emissions and global warming issues. Converting organic waste into vermicompost and compost tea by efficient biotechnology methods could prevent natural resources depletion and decrease carbon footprint. These soil conditioners promote soil fertility, crop yields, soil resources conservation, and community health.

Based on this study, organic waste of the industrial kitchen of the science and research branch university, was converted to vermicompost. The compost tea was produced by a mixture of seaweed, vermicompost, molasses, and humic acid. Greenhouse studies to compare the effect of experimental treatments including irrigation level with compost tea 25, 50, and 100% and chemical fertilizer containing calcium nitrate, potassium sulfate, and 10-52-10 fertilizer on vegetative and biochemical traits of tomato (Karun variety) in six replications were performed.

The results showed that irrigation with 100% compost tea and chemical fertilizer, were not significantly different from each other and had the highest effect compared to other treatments. Irrigation with 100% compost tea had the greatest effect on leaf number (66 pieces), plant height (29.8 mm), root length (660 mm), and root fresh weight (29.84 mg). The highest number of flower clusters (8.94 clusters), root diameter (8.94 mm), peroxidase enzyme (3.5 µmol mg-1 protein min-1), chlorophyll a (14.44 mg g-1 FM), and total chlorophyll content (191.91 mg g-1 FM) obtained from 25% compost tea.

There was no difference between irrigation with 25 and 100% compost tea, and chemical fertilizers. Furthermore, they had similar effects on most of the studied traits. So, irrigation with compost tea can decrease chemical fertilizers in tomato fields and leads to soil conservation.

Soil is one of the important natural resources of any country, which plays an important role in preserving the environment and producing food. Increasing and decreasing the amount of total soil nitrogen due to various agricultural methods, the entry of industrial wastewater into water and other factors, leads to microbial contamination of soil, reduced vegetation and deficiency in agricultural products needed by humans. Mapping soil nutrient distribution helps mangers in decisions. Since laboratory analysis of these parameters is time consuming and costly across large scales, attempts have been made in recent years to study soil nitrogen based on remote sensing techniques. In this regard, the present study investigated the applicability of remote sensing predicting soil total nitrogen in the east of Lenjan city.

Nitrogen reference points were identified by analyzing 50 randomly-selected surface soil samples from 0-20 cm depth. Nitrogen of soil samples was measured by Kjeldahl method after drying soil at 25 ° C, passing through a 2 mm mesh sieve and transferring to the laboratory, to compare the final results obtained from field sampling and remote sensing technology. Landsat 8 OLI Satellite Image of 2019 (Path 164/Row 37) was obtained and geometric and radiometric correction were applied. Cloud cover for image provided was less than 10%. To reduce the effect of atmospheric diffusion on the quality of image, radiation and atmospheric correction were performed using the FLASH model. the Landsat-8 satellite image (rows 164 and 37) taken on 15 Sep. 2019 and along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the multiple linear regression and random forest models. 

The digital elevation map of the area showed elevation values ​​between 1100 and 2050 meters. The slope of the study area was less than eight percent. Numerical values ​​of TWI index near water bodies were found to be 0.77. DVI and EVI index values ​​increased with increasing vegetation cover. NDVI index showed values ​​higher than 0.3 and NDWI index as a water index showed a maximum value of 0.77. The SAVI index showed high differences from areas without cover to sparse cover and areas with strong vegetation. SBI index and SI salinity indices showed very high variability in terms of soil parameters in barren lands. Nitrogen regression model was built using three indices RVI, DVI and TWI with p-values of 0.049 and 0.00, respectively. In the nitrogen random forest model, however, plant and soil indices played a more important role in model construction with an of r2 value of 0.44.

Total soil nitrogen in soil parameters showed correlation with density and sand and clay from soil texture and in topographic parameters with elevation and spectral indices with EVI RVI, SAVI, NDWI, NDVI and DVI at the level of 0.01 and with SI3 of salinity indices at the 0.05 level. In soil parameters, silt is correlated with sand and clay at the level of 0.05 and sand with clay as well as density with clay are correlated at the level of 0.01. The results of this study showed that the topographic condition of the region along with red and near infrared-based indices had a significant role in predicting soil total nitrogen. Results also showed a slight difference was observed between the two models in predicting soil nitrogen.

Ecosystems disturbances induced from social factors affect the environmental changes, temperature, evapotranspiration, runoff production and flow rate. In this regard, Hurst index has been used to analyze changes in hydrological processes due to various factors. The Hurst index is known as an important feature for analyzing hydrological effects. One of the most appropriate tests for long-term memory detection is the rescaling range (R/S) test. The R/S test makes it possible to calculate the self-similarity parameter H (Hurst), which measures the severity of long-term dependence over a time series. Towards this, the present study was conducted to determine the long-term memory using Hurst index for precipitation and discharge time series throughout some selected stations in Ardabil Province, NW Iran.

In the present study, long-term memory for annual precipitation and discharge time series (1991-2013) in 17 rain gauges stations and28 river gauge stations in Ardabil Province was assessed. The Hurst index computational values were classified into five categories from very weak to very strong in terms of dependency and scale of instability in the time series. Spatial correlation analysis of Hurst index was performed using Moran index. The Hurst index values were then interpolated by the inverse weighted distance (IDW) method in Arc Map 10.8.

The results showed that the among 17 study stations, 23.53, 29.41, 17.65, and 23.53% respectively were classified in the stability scale of very weak (0.50<H<0.55), relatively weak (0.55<H<0.65), relatively strong (0.65<H<0.75), and strong (0.75<H<0.80). Meanwhile, only 5.88% including Shamsabad station were classified as very weak (0.45<H<0.55) in terms of instability scale. According to the analysis of 28 hydrometric stations, 25, 50, and 21.42% were respectively relatively weak, relatively strong, and strong, and 3.58% were very weak on the instability scale, respectively. In the meantime, only the Amuqin Station was categorized with very poorly scale. According to the results of Hir, KoozehTapraghi, Shamsabad and Ahmadkandi rain gauge stations, a positive value of Moran index was found indicating similar values in terms of location. In the other stations, the Moran index values are negative, inficating non-similar valuses and no clusters were formed. The results of clustering in hydrometric stations showed that Iril Station was in the high-high clusters and the Atashgah Station was classified in the low-low clusters and positive values of Moran index. The rest of the study stations did not form specific clusters.

The results showed that the Hurst index was obtained for the rainfall stations with an average of 0.64 and a standard deviation of 0.11. The Hurst index was also obtained in hydrometric stations with an average of 0.74 and a standard deviation of 0.12. In general, the range of Hurst index values and its spatial variations on annual precipitation data showed that precipitation values in the study period are not stable. Spatial changes of the mentioned indicators showed that there is a clear difference between different regions of the province in terms of stability of precipitation and discharge. However, according to the box diagram, amplitude of changes and spatial distribution of stations with strong and relatively strong stability, most stations located in the central part of the province have stability in discharge values, which can indicate the continuation of water currents and the occurrence of maximum discharges.

Land surface temperature (LST) is a key boundary condition in many ground-based modeling schemes based on remote sensing. Previous literature has shown that LST products from satellite imagery can be used to detect land surface changes, including urbanization, deforestation and desertification, which can improve our ability to monitor surface changes continuously. The objective of the present study was to evaluate the results of DisTRAD and TsHARP thermal sharpening methods to downscale the spatial resolution of MODIS LST from 1000 m to 250 m.

The research method in the present article is applied in terms of purpose and based on correlation relations in terms of method of work.

The performance of DisTRAD and TsHARP thermal downscaling methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE) between the downscaled and original LSTs. Statistical analysis showed that the RMSE between the downscaled images of DisTRAD and TsHARP methods with the original LST (1000 m (terra)) for 3 May 2019 were found to be 1.77 ° C and 1.7 ° C, respectively, whereas the R2 were found to be about 53% and for 17 October 2019, the RMSE were found to be 2.44 ° C and 2.38 ° C respectively, whereas the R2 were found to be about 85%.

The study of the results of Terra and Aqua satellites generally shows the superiority of Terra satellite results over Aqua. The main reason could be the different passage times of the satellites from the study area. Since that changes in soil moisture and water body such as the Karun River are common sources of error, so the use of these methods is recommended only in areas without excessive changes in moisture.