Soil Physics, Erosion and Conservation
Mitra Yarahmadi; Ataallah Khademalrasoul; hadi Amerikhah
Abstract
Introduction: Soil erosion is the most prevailing form of soil degradation which is really play an important role on the mass balance index of soil particles in the watersheds. Moreover, regarding the on-site and off-site effects of erosion essentially has to measure and predict the soil loss using different ...
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Introduction: Soil erosion is the most prevailing form of soil degradation which is really play an important role on the mass balance index of soil particles in the watersheds. Moreover, regarding the on-site and off-site effects of erosion essentially has to measure and predict the soil loss using different methods. Specifically gully erosion is a form of water erosion with the huge amount of soil dislodgement. Due to the complexity and variability of soil erosion it is necessary to apply different techniques in order to monitor the soil erosion changes. Remote sensing technology and the use of spectrometry and reflectometry basics is a suitable solution and option for monitoring coastal areas affected by erosion and deposition events, which provide high quality temporal and spatial data. Soil color is an appearance property which is meaningfully effective on soil reflectance. Generally, soils with high amount of organic matter has low reflectance because of the darkness while the light soils has high reflectance from surface (high Brightness index, BI) which is effective on soil temperature. Therefore we try to use RS and radio spectrophotometry to find a relation between soil color and its reflectance. Materials and Methods: The study area is located at Zahirieh watershed of Khuzestan province which is between Ahvaz and Masjedsoleyman cities with approximately 7100ha area. The average of rainfall is 218.6mm, the maximum temperature is 54 and the minimum is 7 degrees. Regarding the separation of erosional and depositional surfaces in the study area; first, using the visual inspection of Landsat satellite false color images, 8 regions were divided into several regions, then random sampling points were created using the random point generator tool in ArcGIS 10.4 software to implement the random sampling method within the block. Finally, 12 sampling points representing erosion surfaces and 14 sampling points representing depositional surfaces were selected and sampled to determine surface soil characteristics. Surface soil color was determined using Mansell's color book in natural daylight in two dry and moist conditions. After collecting the soil samples in air-dry moisture condition and also in wet condition, their spectroscopic analysis was done by FieldSpec3 device and this moisture condition was considered for all the soil samples of eroded and depositional surfaces. Statistical analyzes and mean comparisons were performed using SPSS 26 statistical software. Corrections of satellite images and transformations were made in ENVI 4.7 software, and visual outputs and maps were made in ArcGIS 10.4 software.Results and Discussion: Results depicted that among the evaluated soil color indicators, the dry weight parameter is significant at the level of 1%. This level of significance shows well that the Value index in the dry state can be used as an effective parameter to identify and separate erosion and deposition levels in the study area. There is a difference between the values of the statistics for red, green and blue RGB in the dry state for erosion and depositional surfaces, and these differences are also evident for the moist state. In the depositional surfaces, with the drying of the soil, blue, red, and green reflections all decrease, but this decrease is double and about six times for blue. The reduction of blue reflections in the RGB system leads to an increase in the yellowness of the color. In the case of the soils of erosion surfaces, we can see the pattern of the photo and we see the enhancement of reflections and consequently the lightening of the color of the soil when the soil is dry. According to what has been seen in the Munsell system, it seems that this issue has a direct relationship with the amount of organic matter and the ratio of fulvic acid to humic acid in the organic matter of the soil. Moreover, the results of the comparison of the average bands of Landsat 8 shows that bands 2, 3 and 4 are able to separate erosion and sedimentary surfaces at the 1% level, but thermal bands cannot be used to separate surfaces. Due to the difference in the color characteristics of erosional and sedimentary surfaces, as a result, it is possible to separate them based on reflectometric characteristics, and it is possible to separate eroded and sedimentary surfaces by using color indices.Conclusion: Due to the difference in the color characteristics of erosional and depositional surfaces, as a result, it is possible to separate them based on reflectometric characteristics, and it is possible to separate eroded and depositional surfaces by using color indices. The results showed that it was possible to model surface soil characteristics using quantified surface soil color data, and this hypothesis was confirmed by statistical investigations.
Ataallah Khademalrasoul; Hadi Amerikhah
Abstract
Introduction Climate is one of the most effective factors on soil formation, evolution and degradation. It is include different parameters which mainly based on precipitation and temperature. In the recent years the effects of global warming and climate change has extremely enhanced. Climate change as ...
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Introduction Climate is one of the most effective factors on soil formation, evolution and degradation. It is include different parameters which mainly based on precipitation and temperature. In the recent years the effects of global warming and climate change has extremely enhanced. Climate change as an important phenomenon is effective on precipitation parameters including volume, intensity and concentration which categorized in the temporal and spatial variations. Quantifying the effects of climate change is important for identifying critical regions prone to soil erosion under a changing environment. Land-based ecosystems are influenced by patterns of air temperature and precipitation, which include daily and seasonal changes along with humidity and wind, and the nature of the land surface. Global climate change already has observable effects on the environment. Regarding the importance and effectiveness of climate factor and climate changes during the time, it is essential to focus on climate changes on water behavior at different scales. Indeed, precipitation parameters interacting the soil parameters are influencing on runoff potential in the fields and watersheds. In this regard Rainfall-runoff erosivity (R) is one key climate factor that controls water erosion. Universal soil loss equation (USLE) is the main common equation to predict soil loss, this equation consisting 5 factors which R-Factor (Rainfall erosivity factor) is one of the effective factors in this equation. Material and Methods Regarding the effect of climate on soil erosion processes therefore, monitoring of climate is really important. In this study in order to evaluate the climate changes based on time series, four climatological stations including, Ardal, Saman, Izeh, and Dehdez were selected. Using the statistical data of precipitation, calculation of eroding index was performed until 2017. The ACF (Auto Correlation Function) and PACF (Partial Auto Correlation Function) for precipitation data were prepared, afterwards the ADF test was performed at confidence level of 1, 5 and 10 percentage. Then the suitable parameters for p, r and q were selected and the SARIMA (Seasonal auto-regressive integrated moving average) model was provided. The statistical analyses were performed with Stata SE, Minitab 18 and SPSS 19. Moreover, the graphical trends of rainfall as an index of precipitation and the rainfall erosivity factor (R-Factor) were presented. Also, the spatial distribution of R-Factor (in the form of GIS-Maps) were provided including three separated maps based on real data, 5 year predicted and 10 year predicted data. So there was a possibility to monitor and compare the spatial distribution of R-Factor at different time periods. Then based on the area, the percentage of rainfall erosivity index was calculated for the study area based on the real data, 5 year predicted and 10 year predicted data. In addition, the statistical parameters including R-square, RMSE, P-value and so on were calculated for the best model (SAR12) regarding all climatological stations. Results and discussion Our results depicted that to present the trend of precipitation variations as erosive factor the ARIMA (0,0,1)×(1,1,1)12 was the best model. Also, the seasonal autoregressive moving average showed the variation of precipitation in the study area which located in the southwest of Iran. The results of modeling stated that reduction of precipitation for 5 and 10 year periods after 2017. According to amount of monthly simulated of precipitation, the amount of erodibility index was obtained in the area which illustrated the declining trend until 10 year. According to ADF test for all evaluated climatological stations the probability for Ardal was 0.34, for Dehdez was 0.425, for Saman was 0.345 and for Izeh was 0.177, therefore there was difference between climatological stations. Furthermore, the statistical analyses for SAR12 model revealed that the R-square for Ardal station was 0.492, for Dehdez was 0.716, for Saman was 0651 and for Izeh was 0.576. Moreover, approximately 37 % of area has very low rate of erodibility index without previous occurrence. Conclusion Our results clearly confirmed the importance of climate factors and climate change during the time. As results illustrated regarding the variations of precipitation the R-Factor changed. Moreover, climate change is effective on spatial variations of crop cover in the watersheds. Climate change is capable to alter the crop cover patterns in the watersheds and the changes in crop cover distribution and runoff could change the soil erosion potential. Generally, based on results has to focus on water resources conservation in the study area to preserve soil and water against erosive forces and try to improve the vegetation cover because of decreasing of precipitation. In order to manage the soil resources, we need to monitor the climate changes in the watersheds and try to enhance the vegetation covers in the critical parts on the fields.
Fatemeh Hassani; Ataallah Khademalrasoul; Hosein Shekofteh
Abstract
IntroductionSoil is the upper layer of earth in which plants grow and is consequently very important for organisms and human nutrition. The protection of the soil against degrading processes, such as soil salinization and alkalization, is one of the main challenges in sustainable land management. Soil ...
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IntroductionSoil is the upper layer of earth in which plants grow and is consequently very important for organisms and human nutrition. The protection of the soil against degrading processes, such as soil salinization and alkalization, is one of the main challenges in sustainable land management. Soil salinization and alkalization are two major environmental concerns leading to soil degradation especially in arid and semi-arid regions across the world. The balance of organic carbon in the soil is important for soil sustainability. Intensive cultivation enhance soil organic carbon (SOC) depletion. In order to alleviate the detrimental effects of SOC depletion, carbon-rich organic amendments such as biochar or compost are often applied to the soil. Therefore application of organic amendments to soil is an effective strategy to improve soil properties and to mitigate the negative impacts of inappropriate management strategies. Biochar is a carbon-rich compound produced by the pyrolysis of biomass in oxygen-limited conditions. Its use as an organic amendment to soil with specific inherent characteristics has been recognized. In this regard recent studies have shown that application of biochar to soil as an organic amendment can improve soil physical properties and help to keep the carbon balance in the soil. Moreover, compost as an organic amendment is capable to improve soil properties and increase the soil productivity. Methods and MaterialsThe soil sampling was carried out near Kabutar Khan in Rafsanjan, Iran (56°22′N, 30°18′E), on a saline-sodic soil with Silty Clay soil texture (42% silt, 50% clay and 8% sand). The biochar was obtained from three different feedstocks consist of Conocarpus erectus, bagasse of Sugarcane and hard shell of Pistacia Vera. The obtained feedstocks were pyrolyzed at 400°C for 2 h with increasing rate of 7 °C/min in a sealed reactor to prevent O2 input (Muffle Furnace, SEF-101 Model). Afterwards the produced biochar was cooled slowly to the room temperature, then the EC, pH, specific surface area and CHNS of biochars were measured using the standard methods. The required amounts of soils and biochars were weighed by a total 5000 g dry weight of sample and mixed in the dry state. The soil samples were received three doses of biochar (0, 2, 4 % biochar, w/w). The mixtures of soil and biochar were packed into pots and controlled a bulk density of about 1.5 g cm-3 by artificial compaction. Treatments were replicated three times. The soil without any biochar was used as the control. The mixtures were wetted at three soil moisture contents (25, 50 and 75% field capacity) during incubation time (120 days). The treatments were kept at a temperature-controlled glasshouse. After 120 days of incubation, the untreated soils and biochar-amended soils were taken for physical and chemical analyses.Particle size distribution was measured by hydrometer method and soil organic carbon by oxidation method with potassium dichromate. The consistency limits (liquid limit and plastic limit) of soils were determined according to the ASTMD4318 procedure. The field capacity was measured using the pressure plates with the standard rings in the lab. Mechanical strength is a sensitive indicator of the soil physical condition and has been commonly used to evaluate soil water erosion, structural stability, tillage performance, and root penetration. Higher strength found in saline-sodic soil often impedes seedling emergence and root penetration. Results and discussionOur results revealed that application of organic matter in the form of biochars and compost was effective on soil aggregation. The formation and stability of the soil aggregates play an important role in the crop production and soil degradation prevention. Moreover, the biochar application showed two main effects including direct and indirect effects. Our results confirm the addition of biochar to soil can cause a substantial and significant change in the soil physical characteristics of the strongly acidic Ultisol, namely a significant increase in LL and PI, higher water-holding capacity, and reduction in mechanical strength. These changes are undoubtedly associated with the particular properties of biochar and in particular with its high porosity and low bulk density. The beneficial effect of biochars on soil physical properties is mainly due to the dilution effect of biochar with higher porosity and lower density. When the biomass is heated, volatile matters may release out of the biomass to create micropores on the surface, and meanwhile those trapped inside the biomass are evaporated to expand the microstructure. Thus, the resulting biochar has much higher surface area and porosity. These properties are particularly useful for soil application of biochar especially for enhancing soil water-holding capacity, reducing mechanical strength, and increasing soil aggregation. The dilution effect can be attributed to the increased volume of pores as well as the decreased particle density in soil amended with biochar. The effectiveness of different biochars in improving the soil physical properties can be explained by their porosity and bulk density.ConclusionOur results depicted that application of biochars and compost as an organic amendments improved mechanical quality of the saline and sodic studied soil. Indeed all organic treatments decreased bulk density and enhanced soil aggregate stability while the biochar of Conocarpus illustrated the greatest effectiveness on soil physical and mechanical properties. Therefore it is a possibility to apply this biochar to the soil in the field scale but regarding the accessibility of biochar of Pistachio skin in the study area therefor we have another alternative to utilize in the soil. This research was conducted in the small scale and in a short time. Therefore, it is suggested that supplementary studies are carry out on farm scale for a longer periods.
Shamim Shirjandi; A Khademalrasoul; Adel Moradi Sabzkuhi; Hadi Amerikhah
Abstract
IntroductionSoil degradation is a phenomenon which destructs the soil structure and mitigates its capacity for production. Among several processes that cause soil degradation, soil erosion as one of the most common forms of soil degradation leads to loss of soil surface and including on-site and off-site ...
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IntroductionSoil degradation is a phenomenon which destructs the soil structure and mitigates its capacity for production. Among several processes that cause soil degradation, soil erosion as one of the most common forms of soil degradation leads to loss of soil surface and including on-site and off-site effects. Although soil erosion is a natural process on the earth, but destructive human activities such as burning agriculture residue, deforestation, overgrazing, and lack of proper soil conservation practices; accelerate the soil erosion and enhance the negative outcomes of erosion. Selecting and implementing of management scenarios requires assessment of soil losses from different management operations. Generally, management practices consist of structural and non-structural methods that used to reduce erosion, prevent nutrient removal, and increase soil infiltration capacity. Application of simulation models is an appropriate technique to evaluate erosional conditions. GeoWEPP is a process-based, distributed parameters and continuous simulation model of water erosion in watersheds with the possibility to simulate hillslopes and hydrographical network. Locating problems in real world usually face with a large amount of information and decision space that need to be optimized using evolutionary algorithms due to the variety of aims considered. Considering diversity of evolutionary algorithms, NSGA-II is one of the most common and a usable multiobjective evolutionary algorithm (MOEA) which is very powerful tool for solving problems with conflicting objectives. Development of simulation models along with optimization algorithms that are capable of analyzing very complex systems, have found to be very efficient in real world problems. Simulation-optimization models are powerful tools for solving problems for least cost and best performance.Methods and materialsTo predict sediment yield and runoff in the studied watershed, the GeoWEPP integrates WEPP model with TOPAZ (Topography Parameterization), CLIGEN (Climate Generation) and GIS tool (ArcGIS). The GeoWEPP model provides the processing of digital data including DEM ASCII file, soil ASCII file and landcover ASCII file. To generate climate file, the CLIGEN module which is a stochastic weather generation model was utilized. Furthermore in TOPAZ part the CSA (critical source area) and MSCL (minimum source channel length) to delineate streams and also the outlet point of studied watershed were defined using GeoWEPP linked to ArcGIS. Using the basic maps including DEM, slope, soil great groups and soil database the GeoWEPP model simulates and generates the hillslopes automatically; therefore this is an important advantage of GeoWEPP compared to WEPP model which is capable of performing the simulation of watershed components spontaneously. In this study in order to optimize the placement of Gabions, 118 channels and 5110 candidate sites for gabion construction were simulated and evaluated. For optimization process; regarding the number of objectives firstly the AHP technique was used to prioritize the effective factors on the placement of Gabions. Analytical hierarchy process is a structured technique for organizing and analyzing complicated decisions based on mathematical calculations. The AHP depicts the accurate approach for quantifying the weights of criteria and estimates the relative magnitudes of factors through pair-wise comparisons. The AHP technique includes creating hierarchical structure, prioritizing and calculating relative weights of the criteria, calculating the final weights and system results compatibility. The main criteria (objectives) for our study were minimum distance from road, minimum distance from residential area, maximum length of main channel, maximum sediment yield, maximum discharge volume and maximum volume structure. Indeed using the AHP technique it was possible to restrict the decision making space and the number of possible options, therefore simplify the optimization process. Then NSGA-II (Non-dominated Sorting Genetic Algorithm) was applied in order to find the best solutions, i.e. the Pareto front, of alternatives for optimal location of structures based on the two objectives with higher priority and distance constraint. Results and discussionThe results of paired comparison matrix and prioritizing showed that the length of main channel in the watershed is the main effective criterion in locating Gabion structures. The first priority is considered as the most critical channel which produces the highest sediment yield; therefore the most expensive structure is established on that channel. After channel length, the volume discharge was the second priority of effective factors for gabion placement. Using the results of AHP, based on channel length and discharge volume the non-dominated sorting genetic algorithm (NSGA-II) was performed and the priority of critical channels and the specific position was determined from 1 to 35 among 5110 candidate sites for Gabion construction. Using the ArcGIS, slope map and the lowest width of the critical channels the place for gabion construction as a point was determined. Moreover the main output of GeoWEPP is the spatial distribution of sediment yield and based on this map the sediment yield was classified in the watershed. Based on this map the red color was the highest amount of sediment yield (more than 4 ton) in the watershed. ConclusionResults confirmed that application of simulation-optimization techniques helps to select the best sites to construct the Gabion as structural best management practice therefore is a cost-effective technique.
