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.
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.
Javad Khanifar; Ataallah Khademalrasoul; Hadi Amerikhah
Abstract
IntroductionIn recent decades, the potential of soil erosion as serious threat against sustainable land management (SLM) and soil sustainability has been recognized. Furthermore, human-induced soil erosion lead to harmful environmental effects and transportation of sediment to water bodies is accompanied ...
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IntroductionIn recent decades, the potential of soil erosion as serious threat against sustainable land management (SLM) and soil sustainability has been recognized. Furthermore, human-induced soil erosion lead to harmful environmental effects and transportation of sediment to water bodies is accompanied by loss of nutrients and eutrophication. Therefore, there is a need to focus on soil erosion outcomes to prevent its environmental impacts and mitigate the negative feedbacks of soil erosion. One of the most important factors that affecting the amount of erosion and sediment yield is soil erodibility (K-factor in USLE), which is an index of soil susceptibility. Moreover, K-factor is one of the 6 effective factors on soil erosion in universal soil loss equation and mainly is representative of soil properties. Regarding the difficulty of soil erodibility ´s measurement, therefore we need to an easily measurable parameter. In this study soil aggregate is used as an index of soil erodibility factor. Generally, topography alters from one slope to another one, which conduces to dramatically changes of soil erosion intensity, therefore topography in terms of geomorphometric parameters is effective on soil erosion processes; morphometric analysis and soil erosion modeling are robustly interconnected. Also, digital elevation model (DEM) is a base to extract geomorphometric parameters and the quality of DEM is effective on geomorphometric characteristics. Methods and materialThis study conducted with the aim of modelling the aggregate stability using multivariate linear regression and K-means clustering algorithm as an index of soil erodibility factor. For this purpose a study area with an approximate area of 20000 hectares selected. This area is located in Dehdez which is in the north east of Khuzestan province. Based on the climatologic data this area has xeric and thermic regimes. The main land uses in this area including rangeland and forest and in the rangeland the crop cover is mostly grasses. According to the purpose of this study and degree of homogeneity the soil sampling accomplished using completely randomized method. From each land use 25 soil samples collected (the depth of soil sampling was 0-30 cm). Then the soil samples prepared and the mean weight diameter (MWD) as an index of soil aggregate stability using wet sieving, soil texture with hydrometer method and organic carbon content measured in at least 3 replications. In another part of this project the digital elevation model (DEM) of study area extracted from SRTM (Shuttle radar topographic mission) with the spatial resolution of 30 m. After DEM preparation all preprocessing operations performed on DEM using Arc Hydro extension in ArcGIS 10.3 software in order to remove sinks and enhance the quality of DEM. In the next step the primary and secondary geomorphometric parameters extracted from this DEM using ArcGIS 10.3 and SAGA 6.3. software. Finally, multivariate linear regression and K-means clustering models developed between geomorphometric parameters and soil properties as dependent variables and aggregate stability as independent variable to achieve statistical indices in order to evaluate developed models. Results and discussionOur results showed that there was a significant correlation between some geomorphometric parameters and soil aggregate stability; therefore there is a possibility to apply these parameters in order to parameterize aggregate stability. Moreover, the results of modelling indicate that regression models using geomorphometric parameters and soil properties was able to cover approximately 75 and 70 percentage of spatial variation of aggregate stability in the rangeland and forest land use of the study area, respectively. While clustering-regression models were able to explain 77 and 82 percentage of the spatial variation of the aggregates stability in the first and second cluster respectively. Also, the results of validation of developed models in this study showed that the root mean square error (RMSE) of regression models for rangeland and forest land use was 0.33 and 0.26 respectively and RMSE of clustering-regression models for first and second clusters was 0.93 and 0.62 respectively. According to importance of soil aggregate stability on soil erodibility factor (K-factor) and the difficulty of soil erodibility ´s measurement therefore these developed models are useful tools in order to predict soil erosion and based on the distribution of soil erosion and deposition using STI (Sediment transport index) and TWI (Topographical wetness index) could be able to select and apply the best management practices in the critical areas. Indeed STI map and TWI map are indices of spatial distribution of soil erosion and deposition in the studied area therefore using these geomorphometric indices we able to control soil erosion and its negative feedbacks. ConclusionRegarding the difficulty of soil erodibility ´s measurement therefore we used some indices to simplify this process and our results illustrated that it is possible to develop some regression models in order to estimate aggregate stability as an index of soil erodibility. In summary our results confirm that geomorphometric parameters are easily available parameters based on the DEM to predict soil erodibility. Keywords: Digital elevation model (DEM), geomorphometry, modelling, soil erosion, topographical indices
